Spark AI Summit Europe 2019 talk: Asynchronous Hyperparameter Search with Spark on Hopsworks and Maggy. How can you do directed search efficiently with Spark? The answer is Maggy - asynchronous directed search on PySpark.
Hopsworks - The Platform for Data-Intensive AIQAware GmbH
Cloud Native Night July 2019, Munich: Talk by Steffen Srohsschmiedt (@grohsschmiedt, Head of Cloud at LogicalClocks)
=== Please download slides if blurred! ===
Abstract: Machine Learning (ML) pipelines are the fundamental building block for productionizing ML code. Building such pipelines with Big Data is a complex process. The different stages in ML pipelines also need to be orchestrated, from data ingestion and data transformation, to feature engineering, to model training, serving and monitoring.
Hopsworks is an open-source data platform that can be used to both develop and operate horizontally scalable machine learning (ML) pipelines. A key part of our pipelines is the world's first open-source Feature Store, that acts as a data warehouse for features, providing a natural API between data engineers - who write feature engineering code - and Data Scientists, who select features from the feature store to generate training/test data for models.
Join us next time: https://www.meetup.com/Cloud-Native-muc/events
Hopsworks - The Platform for Data-Intensive AIQAware GmbH
Cloud Native Night July 2019, Munich: Talk by Steffen Srohsschmiedt (@grohsschmiedt, Head of Cloud at LogicalClocks)
=== Please download slides if blurred! ===
Abstract: Machine Learning (ML) pipelines are the fundamental building block for productionizing ML code. Building such pipelines with Big Data is a complex process. The different stages in ML pipelines also need to be orchestrated, from data ingestion and data transformation, to feature engineering, to model training, serving and monitoring.
Hopsworks is an open-source data platform that can be used to both develop and operate horizontally scalable machine learning (ML) pipelines. A key part of our pipelines is the world's first open-source Feature Store, that acts as a data warehouse for features, providing a natural API between data engineers - who write feature engineering code - and Data Scientists, who select features from the feature store to generate training/test data for models.
Join us next time: https://www.meetup.com/Cloud-Native-muc/events
Hopsworks at Google AI Huddle, SunnyvaleJim Dowling
Hopsworks is a platform for designing and operating End to End Machine Learning using PySpark and TensorFlow/PyTorch. Early access is now available on GCP. Hopsworks includes the industry's first Feature Store. Hopsworks is open-source.
Managed Feature Store for Machine LearningLogical Clocks
All hyperscale AI companies build their machine learning platforms around a Feature Store.
A feature is a measurable property of some data-sample. It could be for example an image-pixel, a word from a piece of text, the age of a person, a coordinate emitted from a sensor, or an aggregate value like the average number of purchases within the last hour. A Feature Store is a central place to store curated features within an organization.
Feature Stores are a fuel for AI systems as we use them to train machine learning models so that we can make predictions for feature values that we have never seen before.
During this presentation you learn:
- About the concept of a Feature Store and how it can help manage feature data for Enterprises and ease the path of data from backend systems and data-lakes to Data Scientists.
- Our take on Feature Stores, including best practices and use cases and:
- How to ensure Consistent Features in both Training and Serving
Governance, Access-Control, and Versioning
- To create Training Data in the File Format of your Choice
Eliminate Inconsistency between Features in Training and Inferencing
Watch the webinar with a demo: https://www.logicalclocks.com/webinars
StreamSQL Feature Store (Apache Pulsar Summit)Simba Khadder
Input features are the building blocks for machine learning models. You cannot have a great model without great features. By building on top of Apache Pulsar's infinite retention of events, we built infrastructure to serve features in production and to generate training datasets. It allowed our machine learning teams to change, test, and deploy personalization features at an extraordinary rate to 10s of millions of end-users.
This talk will discuss:
- What event-sourcing is and why it's so powerful for machine learning infrastructure.
- How we built the StreamSQL feature store on top of Pulsar, Flink, and Cassandra.
- How a feature store accelerates ML development.
Erin LeDell, H2O.ai - Scalable Automatic Machine Learning - H2O World San Fra...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/ndUtKRzVUCo
In this presentation, Erin LeDell (Chief Machine Learning Scientist, H2O.ai), will provide an overview of the field of "Automatic Machine Learning" and introduce the new AutoML functionality in H2O. H2O's AutoML provides an easy-to-use interface which automates the process of training a large, comprehensive selection of candidate models and a stacked ensemble model which, in most cases, will be the top performing model in the AutoML Leaderboard.
Bio: Erin is the Chief Machine Learning Scientist at H2O.ai. Erin has a Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from University of California, Berkeley. Her research focuses on automatic machine learning, ensemble machine learning and statistical computing. She also holds a B.S. and M.A. in Mathematics.
Before joining H2O.ai, she was the Principal Data Scientist at Wise.io (acquired by GE Digital in 2016) and Marvin Mobile Security (acquired by Veracode in 2012), and the founder of DataScientific, Inc.
Scaling and Unifying SciKit Learn and Apache Spark PipelinesDatabricks
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.
Bridging the Gap Between Data Scientists and Software Engineers – Deploying L...Databricks
GE Aviation has hundreds of data scientists and engineers developing algorithms. The majority of these people do not have the time to learn Apache Spark and continue to develop on local machines in Python or R. We also have lots of historical code that was not developed for Spark. However, the business wanted to deploy to a Spark environment for scalability, as quickly as possible. So how did we bridge the gap? A data scientist and software engineer will co-present to share how we approached the problem of building, unifying and scaling these algorithms.
Distributed Deep Learning with Hadoop and TensorFlowJan Wiegelmann
Training deep neural nets can take long time and heavy resources. By leveraging an existing distributed versions of TensorFlow and Hadoop can train neural nets quickly and efficiently.
Building Intelligent Applications, Experimental ML with Uber’s Data Science W...Databricks
In this talk, we will explore how Uber enables rapid experimentation of machine learning models and optimization algorithms through the Uber’s Data Science Workbench (DSW). DSW covers a series of stages in data scientists’ workflow including data exploration, feature engineering, machine learning model training, testing and production deployment. DSW provides interactive notebooks for multiple languages with on-demand resource allocation and share their works through community features.
It also has support for notebooks and intelligent applications backed by spark job servers. Deep learning applications based on TensorFlow and Torch can be brought into DSW smoothly where resources management is taken care of by the system. The environment in DSW is customizable where users can bring their own libraries and frameworks. Moreover, DSW provides support for Shiny and Python dashboards as well as many other in-house visualization and mapping tools.
In the second part of this talk, we will explore the use cases where custom machine learning models developed in DSW are productionized within the platform. Uber applies Machine learning extensively to solve some hard problems. Some use cases include calculating the right prices for rides in over 600 cities and applying NLP technologies to customer feedbacks to offer safe rides and reduce support costs. We will look at various options evaluated for productionizing custom models (server based and serverless). We will also look at how DSW integrates into the larger Uber’s ML ecosystem, e.g. model/feature stores and other ML tools, to realize the vision of a complete ML platform for Uber.
Managing the Complete Machine Learning Lifecycle with MLflowDatabricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models.
To solve for these challenges, Databricks unveiled last year MLflow, an open source project that aims at simplifying the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
In the past year, the MLflow community has grown quickly: over 120 contributors from over 40 companies have contributed code to the project, and over 200 companies are using MLflow.
In this tutorial, we will show you how using MLflow can help you:
Keep track of experiments runs and results across frameworks.
Execute projects remotely on to a Databricks cluster, and quickly reproduce your runs.
Quickly productionize models using Databricks production jobs, Docker containers, Azure ML, or Amazon SageMaker.
We will demo the building blocks of MLflow as well as the most recent additions since the 1.0 release.
What you will learn:
Understand the three main components of open source MLflow (MLflow Tracking, MLflow Projects, MLflow Models) and how each help address challenges of the ML lifecycle.
How to use MLflow Tracking to record and query experiments: code, data, config, and results.
How to use MLflow Projects packaging format to reproduce runs on any platform.
How to use MLflow Models general format to send models to diverse deployment tools.
Prerequisites:
A fully-charged laptop (8-16GB memory) with Chrome or Firefox
Python 3 and pip pre-installed
Pre-Register for a Databricks Standard Trial
Basic knowledge of Python programming language
Basic understanding of Machine Learning Concepts
Asynchronous Hyperparameter Optimization with Apache SparkDatabricks
For the past two years, the open-source Hopsworks platform has used Spark to distribute hyperparameter optimization tasks for Machine Learning. Hopsworks provides some basic optimizers (gridsearch, randomsearch, differential evolution) to propose combinations of hyperparameters (trials) that are run synchronously in parallel on executors as map functions. However, many such trials perform poorly, and we waste a lot of CPU and harware accelerator cycles on trials that could be stopped early, freeing up the resources for other trials.
In this talk, we present our work on Maggy, an open-source asynchronous hyperparameter optimization framework built on Spark that transparently schedules and manages hyperparameter trials, increasing resource utilization, and massively increasing the number of trials that can be performed in a given period of time on a fixed amount of resources. Maggy is also used to support parallel ablation studies using Spark. We have commercial users evaluating Maggy and we will report on the gains they have seen in reduced time to find good hyperparameters and improved utilization of GPU hardware. Finally, we will perform a live demo on a Jupyter notebook, showing how to integrate maggy in existing PySpark applications.
Continuous Evaluation of Deployed Models in Production Many high-tech industr...Databricks
Many high-tech industries rely on machine-learning systems in production environments to automatically classify and respond to vast amounts of incoming data. Despite their critical roles, these systems are often not actively monitored. When a problem first arises, it may go unnoticed for some time. Once it is noticed, investigating its underlying cause is a time-consuming, manual process. Wouldn’t it be great if the model’s output were automatically monitored? If they could be visualized, sliced by different dimensions? If the system could automatically detect performance degradation and trigger alerts? In this presentation, we describe our experience from building such a core machine-learning services: Model Evaluation.
Our service provides automated, continuous evaluation of the performance of a deployed model over commonly-used metrics like the area-under-the-curve (AUC), root-mean-square-error (RMSE) etc. In addition, summary statistics about the model’s output, their distributions are also computed. The service also provides a dashboard to visualize the performance metrics, summary statistics and distributions of a model over time along with REST APIs to retrieve these metrics programmatically.
These metrics can be sliced by input features (e.g. Geography, Product type) to provide insights into model performance over different segments. The talk will describe various components that are required in building such a service and metrics of interest. Our system has a backend component built with spark on Azure Databricks. The backend can scale to analyze TBs of data to generate model evaluation metrics.
We will talk about how we modified Spark MLLib for computing AUC sliced by different dimensions and other optimizations in Spark to improve compute and performance. Our front-end and middle-tier, built with Docker and Azure Webapp provides visuals and REST APIs to retrieve the above metrics. This talk will cover various aspects of building, deploying and using the above system.
Bridging the Gap Between Datasets and DataFramesDatabricks
Apple leverages Apache Spark for processing large datasets to power key components of Apple's production services. The majority of users rely on Spark SQL to benefit from state-of-the-art optimizations in Catalyst and Tungsten. As there are multiple APIs to interact with Spark SQL, users have to make a wise decision which one to pick. While DataFrames and SQL are widely used, they lack type safety so that the analysis errors will not be detected during the compile time such as invalid column names or types. Also, the ability to apply the same functional constructions as on RDDs is missing in DataFrames. Datasets expose a type-safe API and support for user-defined closures at the cost of performance. This talk will explain cases when Spark SQL cannot optimize typed Datasets as much as it can optimize DataFrames. We will also present an effort to use bytecode analysis to convert user-defined closures into native Catalyst expressions. This helps Spark to avoid the expensive conversion between the internal format and JVM objects as well as to leverage more Catalyst optimizations. A consequence, we can bridge the gap in performance between Datasets and DataFrames, so that users do not have to sacrifice the benefits of Datasets for performance reasons.
Tiny Batches, in the wine: Shiny New Bits in Spark StreamingPaco Nathan
London Spark Meetup 2014-11-11 @Skimlinks
http://www.meetup.com/Spark-London/events/217362972/
To paraphrase the immortal crooner Don Ho: "Tiny Batches, in the wine, make me happy, make me feel fine." http://youtu.be/mlCiDEXuxxA
Apache Spark provides support for streaming use cases, such as real-time analytics on log files, by leveraging a model called discretized streams (D-Streams). These "micro batch" computations operated on small time intervals, generally from 500 milliseconds up. One major innovation of Spark Streaming is that it leverages a unified engine. In other words, the same business logic can be used across multiple uses cases: streaming, but also interactive, iterative, machine learning, etc.
This talk will compare case studies for production deployments of Spark Streaming, emerging design patterns for integration with popular complementary OSS frameworks, plus some of the more advanced features such as approximation algorithms, and take a look at what's ahead — including the new Python support for Spark Streaming that will be in the upcoming 1.2 release.
Also, let's chat a bit about the new Databricks + O'Reilly developer certification for Apache Spark…
Hopsworks at Google AI Huddle, SunnyvaleJim Dowling
Hopsworks is a platform for designing and operating End to End Machine Learning using PySpark and TensorFlow/PyTorch. Early access is now available on GCP. Hopsworks includes the industry's first Feature Store. Hopsworks is open-source.
Managed Feature Store for Machine LearningLogical Clocks
All hyperscale AI companies build their machine learning platforms around a Feature Store.
A feature is a measurable property of some data-sample. It could be for example an image-pixel, a word from a piece of text, the age of a person, a coordinate emitted from a sensor, or an aggregate value like the average number of purchases within the last hour. A Feature Store is a central place to store curated features within an organization.
Feature Stores are a fuel for AI systems as we use them to train machine learning models so that we can make predictions for feature values that we have never seen before.
During this presentation you learn:
- About the concept of a Feature Store and how it can help manage feature data for Enterprises and ease the path of data from backend systems and data-lakes to Data Scientists.
- Our take on Feature Stores, including best practices and use cases and:
- How to ensure Consistent Features in both Training and Serving
Governance, Access-Control, and Versioning
- To create Training Data in the File Format of your Choice
Eliminate Inconsistency between Features in Training and Inferencing
Watch the webinar with a demo: https://www.logicalclocks.com/webinars
StreamSQL Feature Store (Apache Pulsar Summit)Simba Khadder
Input features are the building blocks for machine learning models. You cannot have a great model without great features. By building on top of Apache Pulsar's infinite retention of events, we built infrastructure to serve features in production and to generate training datasets. It allowed our machine learning teams to change, test, and deploy personalization features at an extraordinary rate to 10s of millions of end-users.
This talk will discuss:
- What event-sourcing is and why it's so powerful for machine learning infrastructure.
- How we built the StreamSQL feature store on top of Pulsar, Flink, and Cassandra.
- How a feature store accelerates ML development.
Erin LeDell, H2O.ai - Scalable Automatic Machine Learning - H2O World San Fra...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/ndUtKRzVUCo
In this presentation, Erin LeDell (Chief Machine Learning Scientist, H2O.ai), will provide an overview of the field of "Automatic Machine Learning" and introduce the new AutoML functionality in H2O. H2O's AutoML provides an easy-to-use interface which automates the process of training a large, comprehensive selection of candidate models and a stacked ensemble model which, in most cases, will be the top performing model in the AutoML Leaderboard.
Bio: Erin is the Chief Machine Learning Scientist at H2O.ai. Erin has a Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from University of California, Berkeley. Her research focuses on automatic machine learning, ensemble machine learning and statistical computing. She also holds a B.S. and M.A. in Mathematics.
Before joining H2O.ai, she was the Principal Data Scientist at Wise.io (acquired by GE Digital in 2016) and Marvin Mobile Security (acquired by Veracode in 2012), and the founder of DataScientific, Inc.
Scaling and Unifying SciKit Learn and Apache Spark PipelinesDatabricks
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.
Bridging the Gap Between Data Scientists and Software Engineers – Deploying L...Databricks
GE Aviation has hundreds of data scientists and engineers developing algorithms. The majority of these people do not have the time to learn Apache Spark and continue to develop on local machines in Python or R. We also have lots of historical code that was not developed for Spark. However, the business wanted to deploy to a Spark environment for scalability, as quickly as possible. So how did we bridge the gap? A data scientist and software engineer will co-present to share how we approached the problem of building, unifying and scaling these algorithms.
Distributed Deep Learning with Hadoop and TensorFlowJan Wiegelmann
Training deep neural nets can take long time and heavy resources. By leveraging an existing distributed versions of TensorFlow and Hadoop can train neural nets quickly and efficiently.
Building Intelligent Applications, Experimental ML with Uber’s Data Science W...Databricks
In this talk, we will explore how Uber enables rapid experimentation of machine learning models and optimization algorithms through the Uber’s Data Science Workbench (DSW). DSW covers a series of stages in data scientists’ workflow including data exploration, feature engineering, machine learning model training, testing and production deployment. DSW provides interactive notebooks for multiple languages with on-demand resource allocation and share their works through community features.
It also has support for notebooks and intelligent applications backed by spark job servers. Deep learning applications based on TensorFlow and Torch can be brought into DSW smoothly where resources management is taken care of by the system. The environment in DSW is customizable where users can bring their own libraries and frameworks. Moreover, DSW provides support for Shiny and Python dashboards as well as many other in-house visualization and mapping tools.
In the second part of this talk, we will explore the use cases where custom machine learning models developed in DSW are productionized within the platform. Uber applies Machine learning extensively to solve some hard problems. Some use cases include calculating the right prices for rides in over 600 cities and applying NLP technologies to customer feedbacks to offer safe rides and reduce support costs. We will look at various options evaluated for productionizing custom models (server based and serverless). We will also look at how DSW integrates into the larger Uber’s ML ecosystem, e.g. model/feature stores and other ML tools, to realize the vision of a complete ML platform for Uber.
Managing the Complete Machine Learning Lifecycle with MLflowDatabricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models.
To solve for these challenges, Databricks unveiled last year MLflow, an open source project that aims at simplifying the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
In the past year, the MLflow community has grown quickly: over 120 contributors from over 40 companies have contributed code to the project, and over 200 companies are using MLflow.
In this tutorial, we will show you how using MLflow can help you:
Keep track of experiments runs and results across frameworks.
Execute projects remotely on to a Databricks cluster, and quickly reproduce your runs.
Quickly productionize models using Databricks production jobs, Docker containers, Azure ML, or Amazon SageMaker.
We will demo the building blocks of MLflow as well as the most recent additions since the 1.0 release.
What you will learn:
Understand the three main components of open source MLflow (MLflow Tracking, MLflow Projects, MLflow Models) and how each help address challenges of the ML lifecycle.
How to use MLflow Tracking to record and query experiments: code, data, config, and results.
How to use MLflow Projects packaging format to reproduce runs on any platform.
How to use MLflow Models general format to send models to diverse deployment tools.
Prerequisites:
A fully-charged laptop (8-16GB memory) with Chrome or Firefox
Python 3 and pip pre-installed
Pre-Register for a Databricks Standard Trial
Basic knowledge of Python programming language
Basic understanding of Machine Learning Concepts
Asynchronous Hyperparameter Optimization with Apache SparkDatabricks
For the past two years, the open-source Hopsworks platform has used Spark to distribute hyperparameter optimization tasks for Machine Learning. Hopsworks provides some basic optimizers (gridsearch, randomsearch, differential evolution) to propose combinations of hyperparameters (trials) that are run synchronously in parallel on executors as map functions. However, many such trials perform poorly, and we waste a lot of CPU and harware accelerator cycles on trials that could be stopped early, freeing up the resources for other trials.
In this talk, we present our work on Maggy, an open-source asynchronous hyperparameter optimization framework built on Spark that transparently schedules and manages hyperparameter trials, increasing resource utilization, and massively increasing the number of trials that can be performed in a given period of time on a fixed amount of resources. Maggy is also used to support parallel ablation studies using Spark. We have commercial users evaluating Maggy and we will report on the gains they have seen in reduced time to find good hyperparameters and improved utilization of GPU hardware. Finally, we will perform a live demo on a Jupyter notebook, showing how to integrate maggy in existing PySpark applications.
Continuous Evaluation of Deployed Models in Production Many high-tech industr...Databricks
Many high-tech industries rely on machine-learning systems in production environments to automatically classify and respond to vast amounts of incoming data. Despite their critical roles, these systems are often not actively monitored. When a problem first arises, it may go unnoticed for some time. Once it is noticed, investigating its underlying cause is a time-consuming, manual process. Wouldn’t it be great if the model’s output were automatically monitored? If they could be visualized, sliced by different dimensions? If the system could automatically detect performance degradation and trigger alerts? In this presentation, we describe our experience from building such a core machine-learning services: Model Evaluation.
Our service provides automated, continuous evaluation of the performance of a deployed model over commonly-used metrics like the area-under-the-curve (AUC), root-mean-square-error (RMSE) etc. In addition, summary statistics about the model’s output, their distributions are also computed. The service also provides a dashboard to visualize the performance metrics, summary statistics and distributions of a model over time along with REST APIs to retrieve these metrics programmatically.
These metrics can be sliced by input features (e.g. Geography, Product type) to provide insights into model performance over different segments. The talk will describe various components that are required in building such a service and metrics of interest. Our system has a backend component built with spark on Azure Databricks. The backend can scale to analyze TBs of data to generate model evaluation metrics.
We will talk about how we modified Spark MLLib for computing AUC sliced by different dimensions and other optimizations in Spark to improve compute and performance. Our front-end and middle-tier, built with Docker and Azure Webapp provides visuals and REST APIs to retrieve the above metrics. This talk will cover various aspects of building, deploying and using the above system.
Bridging the Gap Between Datasets and DataFramesDatabricks
Apple leverages Apache Spark for processing large datasets to power key components of Apple's production services. The majority of users rely on Spark SQL to benefit from state-of-the-art optimizations in Catalyst and Tungsten. As there are multiple APIs to interact with Spark SQL, users have to make a wise decision which one to pick. While DataFrames and SQL are widely used, they lack type safety so that the analysis errors will not be detected during the compile time such as invalid column names or types. Also, the ability to apply the same functional constructions as on RDDs is missing in DataFrames. Datasets expose a type-safe API and support for user-defined closures at the cost of performance. This talk will explain cases when Spark SQL cannot optimize typed Datasets as much as it can optimize DataFrames. We will also present an effort to use bytecode analysis to convert user-defined closures into native Catalyst expressions. This helps Spark to avoid the expensive conversion between the internal format and JVM objects as well as to leverage more Catalyst optimizations. A consequence, we can bridge the gap in performance between Datasets and DataFrames, so that users do not have to sacrifice the benefits of Datasets for performance reasons.
Tiny Batches, in the wine: Shiny New Bits in Spark StreamingPaco Nathan
London Spark Meetup 2014-11-11 @Skimlinks
http://www.meetup.com/Spark-London/events/217362972/
To paraphrase the immortal crooner Don Ho: "Tiny Batches, in the wine, make me happy, make me feel fine." http://youtu.be/mlCiDEXuxxA
Apache Spark provides support for streaming use cases, such as real-time analytics on log files, by leveraging a model called discretized streams (D-Streams). These "micro batch" computations operated on small time intervals, generally from 500 milliseconds up. One major innovation of Spark Streaming is that it leverages a unified engine. In other words, the same business logic can be used across multiple uses cases: streaming, but also interactive, iterative, machine learning, etc.
This talk will compare case studies for production deployments of Spark Streaming, emerging design patterns for integration with popular complementary OSS frameworks, plus some of the more advanced features such as approximation algorithms, and take a look at what's ahead — including the new Python support for Spark Streaming that will be in the upcoming 1.2 release.
Also, let's chat a bit about the new Databricks + O'Reilly developer certification for Apache Spark…
Emiliano Martinez | Deep learning in Spark Slides | Codemotion Madrid 2018Codemotion
En esta charla se presentará como se puede afrontar el reto de implantar el Deep Learning sobre la estructura de cómputo de Spark. Se hablará de como construir un proyecto utilizando la infraestructura de Spark ML y BigDL de Intel y su puesta en producción.
Find out more at https://madrid2018.codemotionworld.com/speakers/
Apache® Spark™ MLlib: From Quick Start to Scikit-LearnDatabricks
These are the slides to support the Apache® Spark™ MLlib: From Quick Start to Scikit-Learn webinar.
In this webcast, Joseph Bradley from Databricks will be speaking about Apache Spark’s distributed Machine Learning Library - MLlib.
We will start off with a quick primer on machine learning, Spark MLlib, and a quick overview of some Spark machine learning use cases. We will continue with multiple Spark MLlib quick start demos. Afterwards, the talk will transition toward the integration of common data science tools like Python pandas, scikit-learn, and R with MLlib
My talk at Data Science Labs conference in Odessa.
Training a model in Apache Spark while having it automatically available for real-time serving is an essential feature for end-to-end solutions.
There is an option to export the model into PMML and then import it into a separated scoring engine. The idea of interoperability is great but it has multiple challenges, such as code duplication, limited extensibility, inconsistency, extra moving parts. In this talk we discussed an alternative solution that does not introduce custom model formats and new standards, not based on export/import workflow and shares Apache Spark API.
Databricks Meetup @ Los Angeles Apache Spark User GroupPaco Nathan
Los Angeles Apache Spark Users Group 2014-12-11 http://meetup.com/Los-Angeles-Apache-Spark-Users-Group/events/218748643/
A look ahead at Spark Streaming in Spark 1.2 and beyond, with case studies, demos, plus an overview of approximation algorithms that are useful for real-time analytics.
Since its debut in 2010, Apache Spark has become one of the most popular Big Data technologies in the Apache open source ecosystem. In addition to enabling processing of large data sets through its distributed computing architecture, Spark provides out-of-the-box support for machine learning, streaming and graph processing in a single framework. Spark has been supported by companies like Microsoft, Google, Amazon and IBM and in financial services, companies like Blackrock (http://bit.ly/1Q1DVJH ) and Bloomberg (http://bit.ly/29LXbPv ) have started to integrate Apache Spark into their tool chain and the interest is growing. Unlike other big-data technologies which require intensive programming using Java etc., Spark enables data scientists to work with a big-data technology using higher level languages like Python and R making it accessible to conduct experiments and for rapid prototyping.
In this talk, we will introduce Apache Spark and discuss the key features that differentiate Apache Spark from other technologies. We will provide examples on how Apache Spark can help scale analytics and discuss how the machine learning API could be used to solve large-scale machine learning problems using Spark’s distributed computing framework. We will also illustrate enterprise use cases for scaling analytics with Apache Spark.
Splice Machine's use of Apache Spark and MLflowDatabricks
Splice Machine is an ANSI-SQL Relational Database Management System (RDBMS) on Apache Spark. It has proven low-latency transactional processing (OLTP) as well as analytical processing (OLAP) at petabyte scale. It uses Spark for all analytical computations and leverages HBase for persistence. This talk highlights a new Native Spark Datasource - which enables seamless data movement between Spark Data Frames and Splice Machine tables without serialization and deserialization. This Spark Datasource makes machine learning libraries such as MLlib native to the Splice RDBMS . Splice Machine has now integrated MLflow into its data platform, creating a flexible Data Science Workbench with an RDBMS at its core. The transactional capabilities of Splice Machine integrated with the plethora of DataFrame-compatible libraries and MLflow capabilities manages a complete, real-time workflow of data-to-insights-to-action. In this presentation we will demonstrate Splice Machine's Data Science Workbench and how it leverages Spark and MLflow to create powerful, full-cycle machine learning capabilities on an integrated platform, from transactional updates to data wrangling, experimentation, and deployment, and back again.
End-to-End Data Pipelines with Apache SparkBurak Yavuz
This presentation is about building a data product backed by Apache Spark. The source code for the demo can be found at http://brkyvz.github.io/spark-pipeline
As presented at the CloudBrew 2019 conference in Dec 14, 2019.
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• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
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Length: 30 minutes
Session Overview
-------------------------------------------
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- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
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2. Jim Dowling, Logical Clocks AB and KTH
Moritz Meister, Logical Clocks AB
Asynchronous Hyperparameter
Optimization with Apache Spark
#UnifiedDataAnalytics #SparkAISummit
@jim_dowling
@morimeister
3. The Bitter Lesson (of AI)*
“Methods that scale with computation
are the future of AI”**
3
** https://www.youtube.com/watch?v=EeMCEQa85tw
Rich Sutton
(Father of Reinforcement Learning)
* http://www.incompleteideas.net/IncIdeas/BitterLesson.html
“The two (general purpose) methods that seem to scale
... are search and learning.”*
4. Spark scales with
available compute
=>
Spark is the answer!
This talk is about why bulk-
synchronous parallel compute
(Spark) does not scale efficiently
for search and how we made
Spark efficient for directed
search (task-based
asynchronous parallel compute).
4
5. Inner and Outer Loop of Deep Learning
Inner Loop
Outer Loop
Training Data
worker1 worker2 workerN
…
∆
1
∆
2
∆
N
Synchronization
Metric
Search
Method
HParams
http://tiny.cc/51yjdz
6. Inner and Outer Loop of Deep Learning
Inner Loop
Outer Loop
Training Data
worker1 worker2 workerN
…
∆
1
∆
2
∆
N
Synchronization
Metric
Search
Method
HParams
http://tiny.cc/51yjdz
LEARNINGSEARCH
8. Hopsworks Technical Milestones
8
World’s first Hadoop
platform to support
GPUs-as-a-Resource
World’s fastest
HDFS Published at
USENIX FAST with
Oracle and Spotify
World’s First
Open Source Feature
Store for Machine
Learning
World’s First
Distributed Filesystem to
store small files in
metadata on NVMe disks
Winner of IEEE
Scale Challenge
2017
with HopsFS - 1.2m
ops/sec
2017
World’s most scalable
POSIX-like Hierarchical
Filesystem with
Multi Data Center Availability
with 1.6m ops/sec on GCP
2018 2019
First non-Google ML
Platform with
TensorFlow Extended
(TFX) support through
Beam/Flink
World’s first
Unified
Hyperparam and
Ablation Study
Framework
9. The Complexity of Deep
Learning
9
Data validation
Distributed
Training
Model
Serving
A/B
Testing
Monitoring
Pipeline
Management
HyperParameter
Tuning
Feature Engineering
Data
Collection
Hardware
Management
Data Model Prediction
φ(x)
Hopsworks
Feature Store
Hopsworks
REST API
[Adapted from Schulley et al “Technical Debt of ML” ]
16. Hopsworks Engine: ML Pipelines
17
Data
Pipelines
Ingest & Prep
Feature
Store
Machine Learning Experiments
Data Parallel
Training
Model
Serving
Ablation
Studies
Hyperparameter
Optimization
Bottleneck, due to
• iterative nature
• human interaction
Horizontal Scalability at all Stages
17. Iterative Model Development
• Trial and Error is slow
• Iterative approach is greedy
• Search spaces are usually large
• Sensitivity and interaction of
hyperparameters
18
Set Hyper-
parameters
Train Model
Evaluate
Performance
19. Parallel Black Box Optimization
20
Which algorithm to use for search? How to monitor progress?
Fault Tolerance?How to aggregate results?
Learning
Black Box
Metric
Meta-level
learning &
optimization Parallel
WorkersQueue
Trial
Trial
Search space
This should be managed with platform support!
20. Maggy
A flexible framework for
running different black-box
optimization algorithms
on Hopsworks:
ASHA, Bayesian
Optimization, Random
Search, Grid Search and
more to come…
21
24. 25
Synchronous Successive Halving
Kevin G. Jamieson et al. “Non-stochastic Best Arm Identification and Hyperparameter Optimization” (2015).
Animation: https://blog.ml.cmu.edu/2018/12/12/massively-parallel-hyperparameter-optimization/
25. 26
Asynchronous Successive Halving
Liam Li et al. “Massively Parallel Hyperparameter Tuning” (2018).
Animation: https://blog.ml.cmu.edu/2018/12/12/massively-parallel-hyperparameter-optimization/
26. Challenge
How can we fit this into the bulk synchronous execution model of Spark?
Mismatch: Spark Tasks and Stages vs. Trials
27
Databricks’ approach: Project Hydrogen (barrier execution mode) & SparkTrials in Hyperopt
32. Ablation
34
PClassname survivesex sexname survive
Replacing the Maggy Optimizer with
an Ablator:
• Feature Ablation using
the Feature Store
• Leave-One-Layer-Out Ablation
• Leave-One-Component-Out
(LOCO)
35. Conclusion
● Avoid iterative Hyperparameter Optimization
● Black box optimization is hard
● State-of-the-art algorithms can be deployed asynchronously
● Maggy: platform support for automated hyperparameter
optimization and ablation studies
● Save resources with asynchronism
● Early stopping for sensible models
37
36. What next?
38
• More algorithms
• Comparability of experiments
• Implicit Provenance
• Support for PyTorch
37. Thank you!
Box 1263, Isafjordsgatan 22
Kista, Stockholm
https://www.logicalclocks.com
Register for a free account at
www.hops.site
Twitter
@logicalclocks
@hopsworks
GitHub
https://github.com/hopshadoop/maggy
https://maggy.readthedocs.io/en/latest/
https://github.com/logicalclocks/hopsworks
38. Acknowledgements and References
Thanks to the entire Logical Clocks Team ☺
Contributions from colleagues:
Robin Andersson @robzor92
Sina Sheikholeslami @cutlash
Kim Hammar @KimHammar1
Alex Ormenisan @alex_ormenisan
• Maggy
https://github.com/logicalclocks/maggy or https://maggy.readthedocs.io/en/latest/
• Feature Store: the missing data layer in ML pipelines?
https://www.logicalclocks.com/feature-store/
• Hopsworks white paper.
https://www.logicalclocks.com/whitepapers/hopsworks
• ePipe: Near Real-Time Polyglot Persistence of HopsFS Metadata, CCGrid, 2019
39. DON’T FORGET TO RATE
AND REVIEW THE SESSIONS
SEARCH SPARK + AI SUMMIT