Scaling and Unifying SciKit Learn and Apache Spark Pipelines

•

0 gefällt mir•670 views

Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark. Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations. Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.

Daten & Analysen

Scaling and Unifying
Scikit Learn and Spark
Pipelines using Ray
Raghu Ganti
Principal Research Staff Member
IBM T J Watson Research Center
Team (IBM & Red Hat):
Michael Behrendt, Linsong Chu, Carlos
Costa, Erik Erlandson, Mudhakar Srivatsa

Ray.IO
§ Can we do pipelines on
Ray?
§ Can we scale popular
AI/ML pipelines on Ray?
§ Can we unify scikit learn
and Spark pipelines?

Current pipeline API
• Focus on scikit learn and Spark pipelines
• Scikit learn missing scaling; Spark focus on data parallel
scaling
Transform
Fit
X
X
y
X’
Fitted model

Scaling Pipelines: I/O as List of Objects
Transform
Fit
[X1, X2, … XN]
[X1, X2, … XN]
[y1, y2, … yN]
[X1’, X2’, …, XN’]
[FM1, FM2, … FMN]

Scaling Pipelines: AND/OR Graphs
And node
X1
X2
XN
X1’
X2’
XM’
Or node
X
Step1
Step2
StepN
X’
X’
X’

Key Features
▪ Python function as
unit of compute
▪ Intuitive for data
scientist
▪ Follows transformer
APIs
▪ MPI-style scaling
▪ Object references
as I/O for unit of
compute
▪ Sharing of objects
using Plasma store
▪ Enables zero-copy
object sharing
• List of objects as I/O
• Function as unit of
compute
▪ Scikit learn typically
in Python
▪ Ray.IO with RayDP
enables efficient
data exchange
• Cross environment
▪ Enriched DAGs from
plain pipelines
▪ OR nodes for fan-
out expressions
▪ AND nodes for
arbitrary lambdas
• AND/OR Graphs

Illustrative Example
8
Preprocess
Random
Forest
Gradient
Boost
Decision
Tree
Sample Pipeline
Scikit learn Pipeline
Our Pipeline

Pipelines Galore…
Airflow Kubeflow Scikit learn
Spark
Pipeline
Our
pipeline
Task
parallelism
✓ ✓ ✗ ✓ ✓
Data
parallelism
✗ ✗ ✗ ✓ ✓
And/Or Graphs ✓ ✓ ✗ ✗ ✓
Computational
unit
Container Container
Python
function
Python/Java
function
Python/Java
function
Mutability of
DAG
✗ ✗ ✓ ✓ ✓

What to expect?
• Execution strategies based on graph traversals
• Early stopping criteria
• Mutability of execution pipelines
• Current status: Proposal discussion with Ray and OSS
community

Q&A
Contacts:
Raghu Ganti (rganti@us.ibm.com)
Michael Behrendt (michaelbehrendt@de.ibm.com)
Linsong Chu (lchu@us.ibm.com)
Carlos Costa (chcost@us.ibm.com)
Erik Erlandson (eerlands@redhat.com)
Mudhakar Srivatsa (msrivats@us.ibm.com)

Feedback
Your feedback is important to us.
Don’t forget to rate and review the sessions.

Weitere ähnliche Inhalte

Was ist angesagt?

Independent of the source of data, the integration and analysis of event streams gets more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analyzed, often with many consumers or systems interested in all or part of the events. In this session we compare two popular Streaming Analytics solutions: Spark Streaming and Kafka Streams. Spark is fast and general engine for large-scale data processing and has been designed to provide a more efficient alternative to Hadoop MapReduce. Spark Streaming brings Spark's language-integrated API to stream processing, letting you write streaming applications the same way you write batch jobs. It supports both Java and Scala. Kafka Streams is the stream processing solution which is part of Kafka. It is provided as a Java library and by that can be easily integrated with any Java application. This presentation shows how you can implement stream processing solutions with each of the two frameworks, discusses how they compare and highlights the differences and similarities.

Spark (Structured) Streaming vs. Kafka Streams

Guido Schmutz

In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data. In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.

Re-imagine Data Monitoring with whylogs and Spark

Databricks

(Jason Gustafson, Confluent) Kafka Summit SF 2018 Kafka has a well-designed replication protocol, but over the years, we have found some extremely subtle edge cases which can, in the worst case, lead to data loss. We fixed the cases we were aware of in version 0.11.0.0, but shortly after that, another edge case popped up and then another. Clearly we needed a better approach to verify the correctness of the protocol. What we found is Leslie Lamport’s specification language TLA+. In this talk I will discuss how we have stepped up our testing methodology in Apache Kafka to include formal specification and model checking using TLA+. I will cover the following: 1. How Kafka replication works 2. What weaknesses we have found over the years 3. How these problems have been fixed 4. How we have used TLA+ to verify the fixed protocol. This talk will give you a deeper understanding of Kafka replication internals and its semantics. The replication protocol is a great case study in the complex behavior of distributed systems. By studying the faults and how they were fixed, you will have more insight into the kinds of problems that may lurk in your own designs. You will also learn a little bit of TLA+ and how it can be used to verify distributed algorithms.

Hardening Kafka Replication

confluent

Airbnb has a wide variety of ML problems ranging from models on traditional structured data to models built on unstructured data such as user reviews, messages and listing images. The ability to build, iterate on, and maintain healthy machine learning models is critical to Airbnb’s success. Many ML Platforms cover data collection, feature engineering, training, deploying, productionalization, and monitoring but few, if any, do all of the above seamlessly. Bighead aims to tie together various open source and in-house projects to remove incidental complexity from ML workflows. Bighead is built on Python and Spark and can be used in modular pieces as each ML problem presents unique challenges. Through standardization of the path to production, training environments and the methods for collecting and transforming data on Spark, each model is reproducible and iterable. This talk covers the architecture, the problems that each individual component and the overall system aims to solve, and a vision for the future of machine learning infrastructure. It’s widely adapted in Airbnb and we have variety of models running in production. We have seen the overall model development time go down from many months to days on Bighead. We plan to open source Bighead to allow the wider community to benefit from our work.

Bighead: Airbnb’s End-to-End Machine Learning Platform with Krishna Puttaswa...

Databricks

Did you know 160,000,000,000 pounds of food ends up in North American landfills each year? Flashfood is helping reduce food waste by providing a mobile marketplace where grocers can sell food nearing its best before date. In 2020 alone Flashfood diverted 11.2 million pounds of food waste while saving shoppers 29 million dollars on groceries. To operate and optimize the marketplace, Flashfood ingests, processes, and surfaces a wide variety of data from the core application, partners, and external sources. As the volume, variety and velocity of sources and sinks proliferate, the complexity of scheduling and maintaining jobs increases in tandem. We noticed this complexity largely stemmed from different implementations of core ETL mechanics, rather than business logic itself. We’ve implemented declarative data pipelines following a mantra of ‘code once use often’ to solve for this complexity. We started by building a highly configurable Apache Spark application which is initialized with details of the source, file type, transformation, load destination, etc. We then used Airflow to extend on the DatabricksRunSubmitOperator which allowed us to customize the cluster and parameters used in execution. Finally, we used airflow-declartive to generate DAGs in YAML, enabling us to set configurations, instantiate jobs, and orchestrate execution in a human readable file. The declarative nature means less specialized personnel are able to set up an ETL with confidence, no longer requiring a deep knowledge of Apache Spark intricacies. Additionally, by ensuring that boilerplate logic was only implemented once, we reduced maintenance and increased delivery speed by 80%.

Code Once Use Often with Declarative Data Pipelines

Databricks

Zipline is Airbnb’s data management platform specifically designed for ML use cases. Previously, ML practitioners at Airbnb spent roughly 60% of their time on collecting and writing transformations for machine learning tasks. Zipline reduces this task from months to days – by making the process declarative. It allows data scientists to easily define features in a simple configuration language. The framework then provides access to point-in-time correct features – for both – offline model training and online inference. In this talk we will describe the architecture of our system and the algorithm that makes the problem of efficient point-in-time correct feature generation, tractable. The attendee will learn Importance of point-in-time correct features for achieving better ML model performance Importance of using change data capture for generating feature views An algorithm – to efficiently generate features over change data. We use interval trees to efficiently compress time series features. The algorithm allows generating feature aggregates over this compressed representation. A lambda architecture – that enables using the above algorithm – for online feature generation. A framework, based on category theory, to understand how feature aggregations be distributed, and independently composed. While the talk if fairly technical – we will introduce all the concepts from first principles with examples. Basic understanding of data-parallel distributed computation and machine learning might help, but are not required.

Zipline—Airbnb’s Declarative Feature Engineering Framework

Databricks

DB 모니터링 신규 & 개선 기능 (박명규)

WhaTap Labs

Building an open data platform with apache iceberg

Alluxio, Inc.

A successful machine learning platform allows ML practitioners to focus solely on their experiments and models and minimizes the time it takes to develop ML applications and take them to production. However, building an ML Platform is typically not an easy task due to the many different components involved in the process. In this talk, we will show how two open source projects, Ray (https://ray.io/) and MLflow (https://mlflow.org/), work together to make it easy for ML platform developers to add scaling and experiment management to their platform. We will first provide an overview of Ray and its native libraries: Ray Tune (https://tune.io) for distributed hyperparameter tuning and Ray Serve (https://docs.ray.io/en/master/serve/index.html) for scalable model serving. Then we will showcase how MLflow provides a perfect solution for managing experiments through integrations with Ray for tracking and model deployment. Finally, we will finish with a demo of an ML platform built on Ray, MLflow, and other open source tools.

Building an ML Platform with Ray and MLflow

Databricks

Hadoop Summit Tokyo Apache NiFi Crash Course

DataWorks Summit/Hadoop Summit

Moving from Lambda and Kappa Architectures to Kappa+ at Uber Kappa+ is a new approach developed at Uber to overcome the limitations of the Lambda and Kappa architectures. Whether your realtime infrastructure processes data at Uber scale (well over a trillion messages daily) or only a fraction of that, chances are you will need to reprocess old data at some point. There can be many reasons for this. Perhaps a bug fix in the realtime code needs to be retroactively applied (aka backfill), or there is a need to train realtime machine learning models on last few months of data before bringing the models online. Kafka's data retention is limited in practice and generally insufficient for such needs. So data must be processed from archives. Aside from addressing such situations, enabling efficient stream processing on archived as well as realtime data also broadens the applicability of stream processing. This talk introduces the Kappa+ architecture which enables the reuse of streaming realtime logic (stateful and stateless) to efficiently process any amounts of historic data without requiring it to be in Kafka. We shall discuss the complexities involved in such kind of processing and the specific techniques employed in Kappa+ to tackle them.

Flink Forward San Francisco 2019: Moving from Lambda and Kappa Architectures ...

Flink Forward

Flink Forward San Francisco 2022. In modern data platform architectures, stream processing engines such as Apache Flink are used to ingest continuous streams of data into data lakes such as Apache Iceberg. Streaming ingestion to iceberg tables can suffer by two problems (1) small files problem that can hurt read performance (2) poor data clustering that can make file pruning less effective. To address those two problems, we propose adding a shuffling stage to the Flink Iceberg streaming writer. The shuffling stage can intelligently group data via bin packing or range partition. This can reduce the number of concurrent files that every task writes. It can also improve data clustering. In this talk, we will explain the motivations in details and dive into the design of the shuffling stage. We will also share the evaluation results that demonstrate the effectiveness of smart shuffling. by Gang Ye & Steven Wu

Tame the small files problem and optimize data layout for streaming ingestion...

Flink Forward

Apache Flink internals

Kostas Tzoumas

Introduction to Apache NiFi 1.11.4

Timothy Spann

Luigi presentation NYC Data Science

Erik Bernhardsson

Introduction to Apache NiFi dws19 DWS - DC 2019

Timothy Spann

Flink Forward San Francisco 2022. Being in the payments space, Stripe requires strict correctness and freshness guarantees. We rely on Flink as the natural solution for delivering on this in support of our Change Data Capture (CDC) infrastructure. We heavily rely on CDC as a tool for capturing data change streams from our databases without critically impacting database reliability, scalability, and maintainability. Data derived from these streams is used broadly across the business and powers many of our critical financial reporting systems totalling over $640 Billion in payment volume annually. We use many components of Flink’s flexible DataStream API to perform aggregations and abstract away the complexities of stream processing from our downstreams. In this talk, we’ll walk through our experience from the very beginning to what we have in production today. We’ll share stories around the technical details and trade-offs we encountered along the way. by Jeff Chao

Squirreling Away $640 Billion: How Stripe Leverages Flink for Change Data Cap...

Flink Forward

GSJUG: Mastering Data Streaming Pipelines 09May2023 https://www.meetup.com/futureofdata-princeton/events/293233881/ This is a repost from the Garden State Java Users Group Event. Join me at https://www.meetup.com/garden-state-java-user-group/events/293229660/ See: https://www.eventbrite.com/e/mastering-data-streaming-pipelines-tickets-627677218457?_ga=2.253257801.1787151623.1682868226-741104479.1678110925 Please note that registration via EventBrite is required to attend either in-person or online. We are happy to announce that Tim Spann will be our special guest for the May 9, 2023 meeting! Abstract: In this session, Tim will show you some best practices that he has discovered over the last seven years in building data streaming applications including IoT, CDC, Logs, and more. In his modern approach, we utilize several Apache frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Kafka. From there we build streaming ETL with Apache Flink, enhance events with NiFi enrichment. We build continuous queries against our topics with Flink SQL. We will show where Java fits in as sources, enrichments, NiFi processors and sinks. We hope to see you on May 9! Speaker Timothy Spann Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Pulsar, Apache Kafka, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science. In this session, Tim will show you some best practices that he has discovered over the last seven years in building data streaming applications, including IoT, CDC, Logs, and more. In his modern approach, we utilize several Apache frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Kafka. From there, we build streaming ETL with Apache Flink, enhance events with NiFi enrichment. We build continuous queries against our topics with Flink SQL. We will show where Java fits in as sources, enrichments, NiFi processors, and sinks. https://www.eventbrite.com/e/mastering-data-streaming-pipelines-tickets-627677218457?_ga=2.253257801.178

GSJUG: Mastering Data Streaming Pipelines 09May2023

Timothy Spann

Presented at the ML Platforms Meetup at Pinterest HQ in San Francisco on August 16, 2018. Abstract: At LinkedIn we observed that much of the complexity in our machine learning applications was in their feature preparation workflows. To address this problem, we built Frame, a shared virtual feature store that provides a unified abstraction layer for accessing features by name. Frame removes the need for feature consumers to deal directly with underlying data sources, which are often different across computing environments. By simplifying feature preparation, Frame has made ML applications at LinkedIn easier to build, modify, and understand.

Frame - Feature Management for Productive Machine Learning

David Stein

Building a Streaming Microservice Architecture: with Apache Spark Structured ...

Databricks

Was ist angesagt? (20)

Spark (Structured) Streaming vs. Kafka Streams

Re-imagine Data Monitoring with whylogs and Spark

Hardening Kafka Replication

Bighead: Airbnb’s End-to-End Machine Learning Platform with Krishna Puttaswa...

Code Once Use Often with Declarative Data Pipelines

Zipline—Airbnb’s Declarative Feature Engineering Framework

DB 모니터링 신규 & 개선 기능 (박명규)

Building an open data platform with apache iceberg

Building an ML Platform with Ray and MLflow

Hadoop Summit Tokyo Apache NiFi Crash Course

Flink Forward San Francisco 2019: Moving from Lambda and Kappa Architectures ...

Tame the small files problem and optimize data layout for streaming ingestion...

Apache Flink internals

Introduction to Apache NiFi 1.11.4

Luigi presentation NYC Data Science

Introduction to Apache NiFi dws19 DWS - DC 2019

Squirreling Away $640 Billion: How Stripe Leverages Flink for Change Data Cap...

GSJUG: Mastering Data Streaming Pipelines 09May2023

Frame - Feature Management for Productive Machine Learning

Building a Streaming Microservice Architecture: with Apache Spark Structured ...

Ähnlich wie Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Python business intelligence (PyData 2012 talk)

Stefan Urbanek

Balancing Infrastructure with Optimization and Problem Formulation

Alex D. Gaudio

Azure 機器學習 - 使用Python, R, Spark, CNTK 深度學習

Herman Wu

Scilab Challenge@NTU 2014/2015 Project Briefing

TBSS Group

https://www.eventbrite.com/e/talk-by-paco-nathan-graph-analytics-in-spark-tickets-17173189472 Big Brains meetup hosted by BloomReach, 2015-06-04 Case study / demo of a large-scale graph analytics project, leveraging GraphX in Apache Spark to surface insights about open source developer communities — based on data mining of their email forums. The project works with any Apache email archive, applying NLP and machine learning techniques to analyze message threads, then constructs a large graph. Graph analytics, based on concise Scala coding examples in Spark, surface themes and interactions within the community. Results are used as feedback for respective developer communities, such as leaderboards, etc. As an example, we will examine analysis of the Spark developer community itself.

Graph Analytics in Spark

Paco Nathan

GraphX: Graph analytics for insights about developer communities

Paco Nathan

The machine learning libraries in Apache Spark are an impressive piece of software engineering, and are maturing rapidly. What advantages does Spark.ml offer over scikit-learn? At Data Science Retreat we've taken a real-world dataset and worked through the stages of building a predictive model -- exploration, data cleaning, feature engineering, and model fitting; which would you use in production? The machine learning libraries in Apache Spark are an impressive piece of software engineering, and are maturing rapidly. What advantages does Spark.ml offer over scikit-learn? At Data Science Retreat we've taken a real-world dataset and worked through the stages of building a predictive model -- exploration, data cleaning, feature engineering, and model fitting -- in several different frameworks. We'll show what it's like to work with native Spark.ml, and compare it to scikit-learn along several dimensions: ease of use, productivity, feature set, and performance. In some ways Spark.ml is still rather immature, but it also conveys new superpowers to those who know how to use it.

A full Machine learning pipeline in Scikit-learn vs in scala-Spark: pros and ...

Jose Quesada (hiring)

YARN webinar series: Using Scalding to write applications to Hadoop and YARN

Hortonworks

IBM Strategy for Spark

Mark Kerzner

MathWorks Interview Lecture

John Yates

Dev Ops Training

Spark Summit

Paradigmas de procesamiento en Big Data: estado actual, tendencias y oportu...

Facultad de Informática UCM

Introduction to elasticsearch

hypto

An R primer for SQL folks

Thomas Hütter

What’s New in the Berkeley Data Analytics Stack

Turi, Inc.

Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1tfbFMp. Martin Thompson focuses on the evolution of Java and how it contrasts to C/C++, covering the cultural challenges of pushing the limits of performance and how to collaborate with industry experts and organize teams, which often stands at odds with the culture in many organisations. Filmed at qconlondon.com. Martin Thompson is a high-performance and low-latency specialist, with over two decades working with large scale transactional and big-data systems, in the automotive, gaming, financial, mobile, and CMS domains. He believes in Mechanical Sympathy, which is applying an understanding of the hardware to the creation of software, being fundamental to delivering elegant high-performance solutions.

Practicing at the Cutting Edge

C4Media

Big data distributed processing: Spark introduction

Hektor Jacynycz García

Data Science with Spark

Krishna Sankar

Apache Arrow (Strata-Hadoop World San Jose 2016)

Wes McKinney

Spark meetup TCHUG

Ryan Bosshart

Ähnlich wie Scaling and Unifying SciKit Learn and Apache Spark Pipelines (20)

Python business intelligence (PyData 2012 talk)

Balancing Infrastructure with Optimization and Problem Formulation

Azure 機器學習 - 使用Python, R, Spark, CNTK 深度學習

Scilab Challenge@NTU 2014/2015 Project Briefing

Graph Analytics in Spark

GraphX: Graph analytics for insights about developer communities

A full Machine learning pipeline in Scikit-learn vs in scala-Spark: pros and ...

YARN webinar series: Using Scalding to write applications to Hadoop and YARN

IBM Strategy for Spark

MathWorks Interview Lecture

Dev Ops Training

Paradigmas de procesamiento en Big Data: estado actual, tendencias y oportu...

Introduction to elasticsearch

An R primer for SQL folks

What’s New in the Berkeley Data Analytics Stack

Practicing at the Cutting Edge

Big data distributed processing: Spark introduction

Data Science with Spark

Apache Arrow (Strata-Hadoop World San Jose 2016)

Spark meetup TCHUG

Mehr von Databricks

DW Migration Webinar-March 2022.pptx

Databricks

The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse. Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today. Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow. This is an educational event. Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.

Data Lakehouse Symposium | Day 1 | Part 1

Databricks

Data Lakehouse Symposium | Day 1 | Part 2

Databricks

Data Lakehouse Symposium | Day 2

Databricks

Data Lakehouse Symposium | Day 4

Databricks

In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

Databricks

Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how do we ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Investing in shared, flexible components and practices for monitoring data health is crucial for a complex data organization to rapidly and effectively scale. At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including: Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal Performing data quality validations using libraries built to work with spark Dynamically generating pipelines that can be abstracted away from users Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time

Democratizing Data Quality Through a Centralized Platform

Databricks

Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.

Learn to Use Databricks for Data Science

Databricks

Application performance monitoring (APM) has become the cornerstone of software engineering allowing engineering teams to quickly identify and remedy production issues. However, as the world moves to intelligent software applications that are built using machine learning, traditional APM quickly becomes insufficient to identify and remedy production issues encountered in these modern software applications. As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored. In this talk, I draw upon my (and my team’s) experience building an ML Monitoring system from the ground up and deploying it on customer workloads running large-scale ML training with Spark as well as real-time inference systems. I will highlight how the key principles and architectural choices of APM don’t apply to ML monitoring. You’ll learn why, understand what ML Monitoring can successfully borrow from APM, and hear what is required to build a scalable, robust ML Monitoring architecture.

Why APM Is Not the Same As ML Monitoring

Databricks

Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. We enable data scientists to deploy and operate their models independently, with minimal need for handoffs or gatekeeping. By writing a simple function and calling out to an intuitive API, data scientists can harness a suite of platform-provided tooling meant to make ML operations easy. In this talk, we will dive into the abstractions the Data Platform team has built to enable this. We will go over the interface data scientists use to specify a model and what that hooks into, including online deployment, batch execution on Spark, and metrics tracking and visualization.

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

Databricks

In this talk, I will dive into the stage level scheduling feature added to Apache Spark 3.1. Stage level scheduling extends upon Project Hydrogen by improving big data ETL and AI integration and also enables multiple other use cases. It is beneficial any time the user wants to change container resources between stages in a single Apache Spark application, whether those resources are CPU, Memory or GPUs. One of the most popular use cases is enabling end-to-end scalable Deep Learning and AI to efficiently use GPU resources. In this type of use case, users read from a distributed file system, do data manipulation and filtering to get the data into a format that the Deep Learning algorithm needs for training or inference and then sends the data into a Deep Learning algorithm. Using stage level scheduling combined with accelerator aware scheduling enables users to seamlessly go from ETL to Deep Learning running on the GPU by adjusting the container requirements for different stages in Spark within the same application. This makes writing these applications easier and can help with hardware utilization and costs. There are other ETL use cases where users want to change CPU and memory resources between stages, for instance there is data skew or perhaps the data size is much larger in certain stages of the application. In this talk, I will go over the feature details, cluster requirements, the API and use cases. I will demo how the stage level scheduling API can be used by Horovod to seamlessly go from data preparation to training using the Tensorflow Keras API using GPUs. The talk will also touch on other new Apache Spark 3.1 functionality, such as pluggable caching, which can be used to enable faster dataframe access when operating from GPUs.

Stage Level Scheduling Improving Big Data and AI Integration

Databricks

In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks. Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model? The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity. The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters. In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Databricks

There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal. In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.

Scaling your Data Pipelines with Apache Spark on Kubernetes

Databricks

In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.

Sawtooth Windows for Feature Aggregations

Databricks

Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components. We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure. This allows us to introduce optimization rules that (i) reduce unnecessary computations by passing information between the data processing and ML operators (ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and (iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator. We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.

Raven: End-to-end Optimization of ML Prediction Queries

Databricks

Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis. Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them. Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy. This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.

Processing Large Datasets for ADAS Applications using Apache Spark

Databricks

At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences. What are we storing? Multi Source – Multi Channel Problem Data Representation and Nested Schema Evolution Performance Trade Offs with Various formats Go over anti-patterns used (String FTW) Data Manipulation using UDFs Writer Worries and How to Wipe them Away Staging Tables FTW Datalake Replication Lag Tracking Performance Time!

Massive Data Processing in Adobe Using Delta Lake

Databricks

Detecting advanced email attacks at scale is a challenging ML problem, particularly due to the rarity of attacks, adversarial nature of the problem, and scale of data. In order to move quickly and adapt to the newest threat we needed to build a Continuous Integration / Continuous Delivery pipeline for the entire ML detection stack. Our goal is to enable detection engineers and data scientists to make changes to any part of the stack including joined datasets for hydration, feature extraction code, detection logic, and develop/train ML models. In this talk, we discuss why we decided to build this pipeline, how it is used to accelerate development and ensure quality, and dive into the nitty-gritty details of building such a system on top of an Apache Spark + Databricks stack.

Machine Learning CI/CD for Email Attack Detection

Databricks

Sarah: CEO-Finance-Report pipeline seems to be slow today. Why Jeeves: SparkSQL query dbt_fin_model in CEO-Finance-Report is running 53% slower on 2/28/2021. Data skew issue detected. Issue has not been seen in last 90 days. Jeeves: Adding 5 more nodes to cluster recommended for CEO-Finance-Report to finish in its 99th percentile time of 5.2 hours. Who is Jeeves? An experienced Spark developer? A seasoned administrator? No, Jeeves is a chatbot created to simplify data operations management for enterprise Spark clusters. This chatbot is powered by advanced AI algorithms and an intuitive conversational interface that together provide answers to get users in and out of problems quickly. Instead of being stuck to screens displaying logs and metrics, users can now have a more refreshing experience via a two-way conversation with their own personal Spark expert. We presented Jeeves at Spark Summit 2019. In the two years since, Jeeves has grown up a lot. Jeeves can now learn continuously as telemetry information streams in from more and more applications, especially SQL queries. Jeeves now “knows” about data pipelines that have many components. Jeeves can also answer questions about data quality in addition to performance, cost, failures, and SLAs. For example: Tom: I am not seeing any data for today in my Campaign Metrics Dashboard. Jeeves: 3/5 validations failed on the cmp_kpis table on 2/28/2021. Run of pipeline cmp_incremental_daily failed on 2/28/2021. This talk will give an overview of the newer capabilities of the chatbot, and how it now fits in a modern data stack with the emergence of new data roles like analytics engineers and machine learning engineers. You will learn how to build chatbots that tackle your complex data operations challenges.

Jeeves Grows Up: An AI Chatbot for Performance and Quality

Databricks

Hyperparameter tuning is critical in model development. And its general form: parameter tuning with an objective function is also widely used in industry. On the other hand, Apache Spark can handle massive parallelism, and Apache Spark ML is a solid machine learning solution. But we have not seen a general and intuitive distributed parameter tuning solution based on Apache Spark, why? Not every tuning problem is on Apache Spark ML models. How can Apache Spark handle general models? Not every tuning problem is a parallelizable grid or random search. Bayesian optimization is sequential, how can Apache Spark help in this case? Not every tuning problem is single epoch, deep learning is not. How to fit algos such as hyperband and ASHA into Apache Spark? Not every tuning problem is a machine learning problem, for example simulation + tuning is also common. How to generalize? In this talk, we are going to show how using Fugue-Tune and Apache Spark together can eliminate these painpoints Fugue-Tune like Fugue, is a “super framework” – an absraction layer unifying existing solutions such as Hyperopt and Optuna It firstly models the general tuning problems, independent from machine learning It is designed for both small and large scale problems. It can always fully parallelize the distributable part of a tuning problem It works for both classical and deep learning models. With Fugue, running hyperband and ASHA becomes possible on Apache Spark. In the demo, you will see how to do any type of tuning in a consistent, intuitive, scalable and minimal way. And you will see a live demo of the amazing performance.

Intuitive & Scalable Hyperparameter Tuning with Apache Spark + Fugue

Databricks

Mehr von Databricks (20)

DW Migration Webinar-March 2022.pptx

Data Lakehouse Symposium | Day 1 | Part 1

Data Lakehouse Symposium | Day 1 | Part 2

Data Lakehouse Symposium | Day 2

Data Lakehouse Symposium | Day 4

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

Democratizing Data Quality Through a Centralized Platform

Learn to Use Databricks for Data Science

Why APM Is Not the Same As ML Monitoring

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

Stage Level Scheduling Improving Big Data and AI Integration

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Scaling your Data Pipelines with Apache Spark on Kubernetes

Sawtooth Windows for Feature Aggregations

Raven: End-to-end Optimization of ML Prediction Queries

Processing Large Datasets for ADAS Applications using Apache Spark

Massive Data Processing in Adobe Using Delta Lake

Machine Learning CI/CD for Email Attack Detection

Jeeves Grows Up: An AI Chatbot for Performance and Quality

Intuitive & Scalable Hyperparameter Tuning with Apache Spark + Fugue

Kürzlich hochgeladen

April 2024 - Crypto Market Report's Analysis

manisha194592

Saudi Arabia [ Abortion pills) Jeddah/riaydh/dammam/+966572737505☎️] cytotec tablets uses abortion pills 💊💊 How effective is the abortion pill? 💊💊 +966572737505) "Abortion pills in Jeddah" how to get cytotec tablets in Riyadh " Abortion pills in dammam*💊💊 The abortion pill is very effective. If you’re taking mifepristone and misoprostol, it depends on how far along the pregnancy is, and how many doses of medicine you take:💊💊 +966572737505) how to buy cytotec pills At 8 weeks pregnant or less, it works about 94-98% of the time. +966572737505[ 💊💊💊 At 8-9 weeks pregnant, it works about 94-96% of the time. +966572737505) At 9-10 weeks pregnant, it works about 91-93% of the time. +966572737505)💊💊 If you take an extra dose of misoprostol, it works about 99% of the time. At 10-11 weeks pregnant, it works about 87% of the time. +966572737505) If you take an extra dose of misoprostol, it works about 98% of the time. In general, taking both mifepristone and+966572737505 misoprostol works a bit better than taking misoprostol only. +966572737505 Taking misoprostol alone works to end the+966572737505 pregnancy about 85-95% of the time — depending on how far along the+966572737505 pregnancy is and how you take the medicine. +966572737505 The abortion pill usually works, but if it doesn’t, you can take more medicine or have an in-clinic abortion. +966572737505 When can I take the abortion pill?+966572737505 In general, you can have a medication abortion up to 77 days (11 weeks)+966572737505 after the first day of your last period. If it’s been 78 days or more since the first day of your last+966572737505 period, you can have an in-clinic abortion to end your pregnancy.+966572737505 Why do people choose the abortion pill? Which kind of abortion you choose all depends on your personal+966572737505 preference and situation. With+966572737505 medication+966572737505 abortion, some people like that you don’t need to have a procedure in a doctor’s office. You can have your medication abortion on your own+966572737505 schedule, at home or in another comfortable place that you choose.+966572737505 You get to decide who you want to be with during your abortion, or you can go it alone. Because+966572737505 medication abortion is similar to a miscarriage, many people feel like it’s more “natural” and less invasive. And some+966572737505 people may not have an in-clinic abortion provider close by, so abortion pills are more available to+966572737505 them. +966572737505 Your doctor, nurse, or health center staff can help you decide which kind of abortion is best for you. +966572737505 More questions from patients: Saudi Arabia+966572737505 CYTOTEC Misoprostol Tablets. Misoprostol is a medication that can prevent stomach ulcers if you also take NSAID medications. It reduces the amount of acid in your stomach, which protects your stomach lining. The brand name of this medication is Cytotec®.+966573737505) Unwanted Kit is a combination of two medicin

Abortion pills in Jeddah | +966572737505 | Get Cytotec

Abortion pills in Riyadh +966572737505 get cytotec

Vip Mumbai Call Girls Marol Naka Call On 9920725232 With Body to body massage with sex Booking Contact Details :- WhatsApp Chat :- +91-9920725232 4-May-2024(SMW) Call Girls In Model Towh +91-9920725232 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-9920725232 (Any Time. Any Where) Call Girls in , Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —9920725232 We are available 24*7 all days of the year. Call us — 9920725232 Thank you for Visiting.

Vip Mumbai Call Girls Marol Naka Call On 9920725232 With Body to body massage...

amitlee9823

Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure

Pooja Nehwal

Building Real-Time Pipelines With FLaNK Timothy Spann, Principal Developer Advocate, Streaming - Cloudera Future of Data meetup, startup grind, AI Camp The combination of Apache Flink, Apache NiFi, and Apache Kafka for building real-time data processing pipelines is extremely powerful, as demonstrated by this case study using the FLaNK-MTA project. The project leverages these technologies to process and analyze real-time data from the New York City Metropolitan Transportation Authority (MTA). FLaNK-MTA demonstrates how to efficiently collect, transform, and analyze high-volume data streams, enabling timely insights and decision-making. Apache NiFi Apache Kafka Apache Flink Apache Iceberg LLM Generative AI Slack Postgresql

DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

Timothy Spann

Call Girls In Bellandur ☎ 7737669865 🥵 Book Your One night Stand Booking Contact Details :- WhatsApp Chat :- +91-7737669865 Call Girls In Model Towh +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in , Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At #K09 Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls In Bellandur ☎ 7737669865 🥵 Book Your One night Stand

amitlee9823

Digital advertising, or paid media, encompasses the strategic deployment of online advertisements to reach target audiences efficiently and effectively. This includes any digital platform that supports advertising to deliver unique messages for any objective. Understanding the mechanics of digital advertising platforms, along with insights into audience behaviors and preferences, allows marketers to optimize their ad spend and achieve significant engagement and conversion rates. This lecture is for Advanced Digital & Social Media Strategy (MGMTX 466.05) at UCLA Extension.

Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...

Valters Lauzums

5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed

amy56318795

➥🔝 7737669865 🔝▻ Bangalore Call-girls in Women Seeking Men 🔝Bangalore🔝 Escorts Service Booking Contact Details :- WhatsApp Chat :- +91-7737669865 Call Girls In Model Towh +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in , Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At #K09 Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

➥🔝 7737669865 🔝▻ Bangalore Call-girls in Women Seeking Men 🔝Bangalore🔝 Esc...

amitlee9823

➥🔝 7737669865 🔝▻ Dindigul Call-girls in Women Seeking Men 🔝Dindigul🔝 Escorts Service Booking Contact Details :- WhatsApp Chat :- +91-7737669865 Call Girls In Model Towh +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in , Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At #K09 Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

➥🔝 7737669865 🔝▻ Dindigul Call-girls in Women Seeking Men 🔝Dindigul🔝 Escor...

amitlee9823

Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore Escorts Service Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...

amitlee9823

➥🔝 7737669865 🔝▻ Thrissur Call-girls in Women Seeking Men 🔝Thrissur🔝 Escorts Service Booking Contact Details :- WhatsApp Chat :- +91-7737669865 Call Girls In Model Towh +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in , Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At #K09 Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

➥🔝 7737669865 🔝▻ Thrissur Call-girls in Women Seeking Men 🔝Thrissur🔝 Escor...

amitlee9823

Klinik_ Apotek Onlin 085657271886 Solusi Menggugurkan Masalah Kehamilan Anda Jual Obat Aborsi Asli KLINIK ABORSI TERPEECAYA _ Jual Obat Aborsi Cytotec Misoprostol Asli 100% Ampuh Hanya 3 Jam Langsung Gugur || OBAT PENGGUGUR KANDUNGAN AMPUH MANJUR OBAT ABORSI OLINE" APOTIK Jual Obat Cytotec, Gastrul, Gynecoside Asli Ampuh. JUAL ” Obat Aborsi Tuntas | Obat Aborsi Manjur | Obat Aborsi Ampuh | Obat Penggugur Janin | Obat Pencegah Kehamilan | Obat Pelancar Haid | Obat terlambat Bulan | Ciri Obat Aborsi Asli | Obat Telat Bulan | Pil Aborsi Asli | Cara Menggugurkan Konten | Cara Aborsi Tuntas | Harga Obat Aborsi Asli | Pil Aborsi | Jual Obat Aborsi Cytotec | Cara Aborsi Sendiri | Cara Aborsi Usia 1 Bulan | Cara Aborsi Usia 2 Tahun | Cara Aborsi Usia 3 Bulan | Obat Aborsi Usia 4 Bulan | Cara Abrasi Usia 5 Bulan | Cara Menggugurkan Konten | Kandungan Obat Penggugur | Cara Menghitung Usia Konten | Cara Mengatasi Terlambat Bulan | Penjual Obat Aborsi Asli | Obat Aborsi Garansi | Kandungan Obat Peluntur | Obat Telat Datang Bulan | Obat Telat Haid | Obat Aborsi Paling Murah | Klinik Jual Obat Aborsi | Jual Pil Cytotec | Apotik Jual Obat Aborsi | Kandungan Dokter Abrasi | Cara Aborsi Cepat | Jual Obat Aborsi Bergaransi | Jual Obat Cytotec Asli | Obat Aborsi Aman Manjur | Obat Misoprostol Cytotec Asli. "APA ITU ABORSI" “Aborsi Adalah dengan membendung hormon yang di perlukan untuk mempertahankan kehamilan yaitu hormon progesteron, karena hormon ini dibendung, maka jalur kehamilan mulai membuka dan leher rahim menjadi melunak,sehingga mengeluarkan darah yang merupakan tanda bahwa obat telah bekerja || maksimal 1 jam obat diminum || PENJELASAN OBAT ABORSI USIA 1 _7 BULAN Pada usia kandungan ini, pasien akan merasakan sakit yang sedikit tidak berlebihan || sekitar 1 jam ||. namun hanya akan terjadi pada saatdarah keluar merupakan pertanda menstruasi. Hal ini dikarenakan pada usiakandungan 3 bulan,janin sudah terbentuk sebesar kepalan tangan orang dewasa. Cara kerja obat aborsi : JUAL OBAT ABORSI AMPUH dosis 3 bulan secara umum sama dengan cara kerja || DOSIS OBAT ABORSI 2 bulan”, hanya berbedanya selain mengisolasijanin juga menghancurkan janin dengan formula methotrexate dikandungdidalamnya. Formula methotrexate ini sangat ampuh untuk menghancurkan janinmenjadi serpihan-serpihan kecil akan sangat berguna pada saat dikeluarkan nanti. APA ALASAN WANITA MELAKUKAN ABORSI? Aborsi di lakukan wanita hamil baik yang sudah menikah maupun belum menikah dengan berbagai alasan , akan tetapi alasan yang utama adalah alasan-alasan non medis (termasuk aborsi sendiri / di sengaja/ buatan] MELAYANI PEMESANAN OBAT ABORSI SETIAP HARI, SIAP KIRIM KESELURUH KOTA BESAR DI INDONESIA DAN LUAR NEGERI. HUBUNGI PEMESANAN LEBIH NYAMAN VIA WA/: 085657271886

Jual Obat Aborsi Surabaya ( Asli No.1 ) 085657271886 Obat Penggugur Kandungan...

ZurliaSoop

Ashok Vihar Call Girls in Delhi (–9953330565) Escort Service In Delhi NCR PROVIDE 100% REAL GIRLS ALL ARE GIRLS LOOKING MODELS AND RAM MODELS ALL GIRLS” INDIAN , RUSSIAN ,KASMARI ,PUNJABI HOT GIRLS AND MATURED HOUSE WIFE BOOKING ONLY DECENT GUYS AND GENTLEMAN NO FAKE PERSON FREE HOME SERVICE IN CALL FULL AC ROOM SERVICE IN SOUTH DELHI Ultimate Destination for finding a High Profile Independent Escorts in Delhi.Gurgaon.Noida..!.Like You Feel 100% Real Girl Friend Experience. We are High Class Delhi Escort Agency offering quality services with discretion. We only offer services to gentlemen people. We have lots of girls working with us like students, Russian, models, house wife, and much More We Provide Short Time and Full Night Service Call ☎☎+91–9953330565 ❤꧂ • In Call and Out Call Service in Delhi NCR • 3* 5* 7* Hotels Service in Delhi NCR • 24 Hours Available in Delhi NCR • Indian, Russian, Punjabi, Kashmiri Escorts • Real Models, College Girls, House Wife, Also Available • Short Time and Full Time Service Available • Hygienic Full AC Neat and Clean Rooms Avail. In Hotel 24 hours • Daily New Escorts Staff Available • Minimum to Maximum Range Available. Location;- Delhi, Gurgaon, NCR, Noida, and All Over in Delhi Hotel and Home Services HOTEL SERVICE AVAILABLE :-REDDISSON BLU,ITC WELCOM DWARKA,HOTEL-JW MERRIOTT,HOLIDAY INN MAHIPALPUR AIROCTY,CROWNE PLAZA OKHALA,EROSH NEHRU PLACE,SURYAA KALKAJI,CROWEN PLAZA ROHINI,SHERATON PAHARGANJ,THE AMBIENC,VIVANTA,SURAJKUND,ASHOKA CONTINENTAL , LEELA CHANKYAPURI,_ALL 3* 5* 7* STARTS HOTEL SERVICE BOOKING CALL Call WHATSAPP Call ☎+91–9953330565❤꧂ NIGHT SHORT TIME BOTH ARE AVAILABLE

Call Girls In Shalimar Bagh ( Delhi) 9953330565 Escorts Service

9953056974 Low Rate Call Girls In Saket, Delhi NCR

👉 Amritsar Call Girl 👉📞 6367187148 👉📞 Just📲 Call Ruhi Call Girl Phone No Amritsar No💰Advance Cash On Delivery Service Escorts Service Available Whatsapp Chaya ☎️ : [+91-6367187148] Escorts Service Amritsar are always ready to make their clients happy. Their exotic looks and sexy personalities are sure to turn heads. You can enjoy with them, including massages and erotic encounters.#P12Our area Escorts are young and sexy, so you can expect to have an exotic time with them. They are trained to satiate your naughty nerves and they can handle anything that you want. They are also intelligent, so they know how to make you feel comfortable and relaxed SERVICE ✅ ❣️ ⭐➡️HOT & SEXY MODELS // COLLEGE GIRLS HOUSE WIFE RUSSIAN , AIR HOSTES ,VIP MODELS . AVAILABLE FOR COMPLETE ENJOYMENT WITH HIGH PROFILE INDIAN MODEL AVAILABLE HOTEL & HOME ★ SAFE AND SECURE HIGH CLASS SERVICE AFFORDABLE RATE ★ SATISFACTION,UNLIMITED ENJOYMENT. ★ All Meetings are confidential and no information is provided to any one at any cost. ★ EXCLUSIVE PROFILes Are Safe and Consensual with Most Limits Respected ★ Service Available In: - HOME & HOTEL Star Hotel Service .In Call & Out call SeRvIcEs : ★ A-Level (star escort) ★ Strip-tease ★ BBBJ (Bareback Blowjob)Receive advanced sexual techniques in different mode make their life more pleasurable. ★ Spending time in hotel rooms ★ BJ (Blowjob Without a Condom) ★ Completion (Oral to completion) ★ Covered (Covered blowjob Without condom ★ANAL SERVICES.

👉 Amritsar Call Girl 👉📞 6367187148 👉📞 Just📲 Call Ruhi Call Girl Phone No Amri...

karishmasinghjnh

(Vivek)Call Us, 8448380779,Call girls in Delhi NCr – We Offer best in class call girls. escort Service At Affordable Price At low Rate with Space Night 8000 We Are One Of The Oldest Escort and Call girls Agencies in Delhi. You Will Find That Our Female Escorts Are Full Of Fun, Sexy And They Would Love Enjoy Your Company. We Have A Fantastic Selection Of Escort Ladies Available For In-Calls As Well As Out-Calls. Our Escorts Are Not Only Beautiful But All Have Great Personalities Making Them The Perfect Companion For Any Occasion. In-Call:- You Can Come At Our Place in Delhi Our place Which Is Very Clean Hygienic 100% safe Accommodation. Out-Call:- You have To Come Pick The Girl From My Place We Are Also Provide Door Step Services (Delhi Ncr, Noida, Gurgaon, Faridabad, Ghaziabad Note:- Pic Collectors Time Passers Bargainers Stay Away As We Respect The Value For Your Money Time And Expect The Same From You Hygienic:- Full Ac room And Clean Rooms Available In Hotel 24 * 7 Hourly In Delhi NCR More Details, With WhatsApp Number, +91-8448380779

BDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort Service

Delhi Call girls

➥🔝 7737669865 🔝▻ mahisagar Call-girls in Women Seeking Men 🔝mahisagar🔝 Escorts Service Booking Contact Details :- WhatsApp Chat :- +91-7737669865 Call Girls In Model Towh +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in , India,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At #K09 Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

➥🔝 7737669865 🔝▻ mahisagar Call-girls in Women Seeking Men 🔝mahisagar🔝 Esc...

amitlee9823

Model Call Girl Services in Delhi reach out to us at 🔝 9953056974 🔝✔️✔️ Our agency presents a selection of young, charming call girls available for bookings at Oyo Hotels. Experience high-class escort services at pocket-friendly rates, with our female escorts exuding both beauty and a delightful personality, ready to meet your desires. Whether it's Housewives, College girls, Russian girls, Muslim girls, or any other preference, we offer a diverse range of options to cater to your tastes. We provide both in-call and out-call services for your convenience. Our in-call location in Delhi ensures cleanliness, hygiene, and 100% safety, while our out-call services offer doorstep delivery for added ease. We value your time and money, hence we kindly request pic collectors, time-passers, and bargain hunters to refrain from contacting us. Our services feature various packages at competitive rates: One shot: ₹2000/in-call, ₹5000/out-call Two shots with one girl: ₹3500/in-call, ₹6000/out-call Body to body massage with sex: ₹3000/in-call Full night for one person: ₹7000/in-call, ₹10000/out-call Full night for more than 1 person: Contact us at 🔝 9953056974 🔝. for details Operating 24/7, we serve various locations in Delhi, including Green Park, Lajpat Nagar, Saket, and Hauz Khas near metro stations. For premium call girl services in Delhi 🔝 9953056974 🔝. Thank you for considering us!

CHEAP Call Girls in Rabindra Nagar (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE

9953056974 Low Rate Call Girls In Saket, Delhi NCR

Chintamani Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore Escorts Service Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Chintamani Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore ...

amitlee9823

Call Girls Bommasandra Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls Bommasandra Just Call 👗 7737669865 👗 Top Class Call Girl Service B...

amitlee9823

Kürzlich hochgeladen (20)