MLOps Virtual Event: Automating ML at Scale
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ML is transforming many industries but operating ML systems at scale is complex as it involves many teams, constant data and model updates, and moving from development to production. ML platforms aim to help with this by providing software to manage the entire ML lifecycle from data to experimentation to production deployment through a consistent interface. Desirable features for an ML platform include ease of use, integration with data infrastructure for governance, and collaboration functions to enable sharing of code, data, models and experiments. Databricks provides an open source ML platform that integrates with data lakes and a data science workspace to help organizations perform MLOps at scale.
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Building a MLOps Platform Around MLflow to Enable Model Productionalization i...Databricks Getting machine learning models to production is notoriously difficult: it involves multiple teams (data scientists, data and machine learning engineers, operations, …), who often does not speak to each other very well; the model can be trained in one environment but then productionalized in completely different environment; it is not just about the code, but also about the data (features) and the model itself… At DataSentics, as a machine learning and cloud engineering studio, we see this struggle firsthand – on our internal projects and client’s projects as well.
Experimentation to Industrialization: Implementing MLOps
Experimentation to Industrialization: Implementing MLOpsDatabricks In this presentation, drawing upon Thorogood’s experience with a customer’s global Data & Analytics division as their MLOps delivery partner, we share important learnings and takeaways from delivering productionized ML solutions and shaping MLOps best practices and organizational standards needed to be successful.
We open by providing high-level context & answering key questions such as “What is MLOps exactly?” & “What are the benefits of establishing MLOps Standards?”
The subsequent presentation focuses on our learnings & best practices. We start by discussing common challenges when refactoring experimentation use-cases & how to best get ahead of these issues in a global organization. We then outline an Engagement Model for MLOps addressing: People, Processes, and Tools. ‘Processes’ highlights how to manage the often siloed data science use case demand pipeline for MLOps & documentation to facilitate seamless integration with an MLOps framework. ‘People’ provides context around the appropriate team structures & roles to be involved in an MLOps initiative. ‘Tools’ addresses key requirements of tools used for MLOps, considering the match of services to use-cases.
Mohamed Sabri: Operationalize machine learning with Kubeflow
Mohamed Sabri: Operationalize machine learning with KubeflowLviv Startup Club This document summarizes a hands-on workshop on Kubeflow Pipeline. The workshop will cover requirements, an introduction to the presenter Mohamed Sabri, and their approach of strategizing, shaping, and spreading knowledge. It then discusses operationalizing machine learning (MLOps) and provides an analysis, design, coaching, and implementation framework. Deliverables include an implemented MLOps environment, training sessions, design documents, and a recommendations roadmap. The rest of the document discusses MLOps architectures, challenges, example technologies and tools, a use case, and deployment workflows from notebooks to production.
Mohamed Sabri: Operationalize machine learning with Kubeflow
Mohamed Sabri: Operationalize machine learning with KubeflowEdunomica Data Science Online Camp 2021
Website - https://dscamp.org/
Youtube - https://www.youtube.com/channel/UCeHtPZ_ZLZ-nHFMUCXY81RQ/featured
FB - https://www.facebook.com/Data-Science-Camp-103012708431833
Databricks for MLOps Presentation (AI/ML)
Databricks for MLOps Presentation (AI/ML)Knoldus Inc. In this session, we will be introducing how we can utilize Databricks to achieve MLflow in Machine learning. The main highlight for this session will be featured in machine learning like MLflow with Databricks for every experiment tracking, how we can do model packaging, and how we can deploy the model of machine learning in Databricks.
Aditya Bhattacharya - Enterprise DL - Accelerating Deep Learning Solutions to...
Aditya Bhattacharya - Enterprise DL - Accelerating Deep Learning Solutions to...Aditya Bhattacharya For this talk, I will be discussing about various approaches to accelerate deep learning solutions from notebooks or research environment to production environment and how these solutions can be transformed as an enterprise level end to end Deep Learning Solution, which can be consumed as a service by any software application, with a practical use-case example.
Building a Scalable and reliable open source ML Platform with MLFlow
Building a Scalable and reliable open source ML Platform with MLFlowGoDataDriven This document discusses building a scalable and open source machine learning platform. It introduces MLOps and describes ING's ML batch platform use case. The machine learning lifecycle is presented, noting that operationalizing machine learning models is difficult due to infrastructure deployment challenges, lack of collaboration and standardization. An ideal MLOps approach is described with flexible, scalable, automated and standardized processes. Benefits of ING's MLOps approach include increased efficiency, speed, quality, security and auditability. Open source tools that could be leveraged are also presented.
Why do the majority of Data Science projects never make it to production?
Why do the majority of Data Science projects never make it to production?Itai Yaffe María de la Fuente (Solutions Architect Manager for IMEA) @ Databricks
While most companies understand the value creation of leveraging data and are taking on board an AI strategy, only 13% of the data science projects make it to production successfully.
Besides the well-known skills gap in the market, we need to level up our end-to-end approach and cover all aspects involved when working with AI.
In this session, we will discuss the main obstacles to overcome and how we can avoid the major pitfalls to ensure our data science journey becomes successful.
Scaling up Machine Learning Development
Scaling up Machine Learning DevelopmentMatei Zaharia An update on the open source machine learning platform, MLflow, given by Matei Zaharia at ScaledML 2020. Details on the new autologging and model registry features, and large scale use cases.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
MLflow: Infrastructure for a Complete Machine Learning Life CycleDatabricks 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 address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but these platforms are limited to each company’s internal infrastructure.
In this talk, we will present MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. 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.
Feature Store as a Data Foundation for Machine Learning
Feature Store as a Data Foundation for Machine LearningProvectus This document discusses feature stores and their role in modern machine learning infrastructure. It begins with an introduction and agenda. It then covers challenges with modern data platforms and emerging architectural shifts towards things like data meshes and feature stores. The remainder discusses what a feature store is, reference architectures, and recommendations for adopting feature stores including leveraging existing AWS services for storage, catalog, query, and more.
Mykola Mykytenko: MLOps: your way from nonsense to valuable effect (approache...
Mykola Mykytenko: MLOps: your way from nonsense to valuable effect (approache...Lviv Startup Club Mykola Mykytenko: MLOps: your way from nonsense to valuable effect (approaches, cases, tools)
AI & BigData Online Day 2021
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Open, Secure & Transparent AI Pipelines
Open, Secure & Transparent AI PipelinesNick Pentreath AI algorithms offer great promise in criminal justice, credit scoring, hiring and other domains. However, algorithmic fairness is a legitimate concern. Possible bias and adversarial contamination can come from training data, inappropriate data handling/model selection or incorrect algorithm design. This talk discusses how to build an open, transparent, secure and fair pipeline that fully integrates into the AI lifecycle — leveraging open-source projects such as AI Fairness 360 (AIF360), Adversarial Robustness Toolbox (ART), the Fabric for Deep Learning (FfDL) and the Model Asset eXchange (MAX).
Python + MPP Database = Large Scale AI/ML Projects in Production Faster
Python + MPP Database = Large Scale AI/ML Projects in Production FasterPaige_Roberts ODSC East virtual presentation - The best machine learning, and advanced analytics projects are often stopped when it comes time to move into large scale production, preventing them from ever impacting the business in a meaningful way. Hundreds of hours of work may never get put to use.
Python is rapidly becoming the language of choice for scientists and researchers of many types to build, test, train and score models. But when data science models need to go into production, challenges of performance and scale can be a huge roadblock.
By combining a Python application with an underlying massively parallel (MPP) database, Python users can achieve a simplified path to production. An MPP database also allows you to do data preparation and data analysis at far greater speeds, accelerating development and testing as well as production performance. It also allows greater numbers of concurrent jobs to run, while also continuously loading data for IoT or other streaming use cases.
Analyze data in the database where it sits, rather than first moving it to another framework, then analyzing it, then moving the results, taking multiple performance hits from both CPU and IO for every move and transformation.
In this talk, you will learn about combination architectures that can get your work into production, shorten development time, and provide the performance and scale advantages of an MPP database with the convenience and power of Python. Use case examples use the open source Vertica-Python project created by Uber with contributions from Twitter, Palantir, Etsy, Vertica, Kayak and Gooddata.
Software engineering practices for the data science and machine learning life...
Software engineering practices for the data science and machine learning life...DataWorks Summit With the advent of newer frameworks and toolkits, data scientists are now more productive than ever and starting to prove indispensable to enterprises. Typical organizations have large teams of data scientists who build out key analytics assets that are used on a daily basis and an integral part of live transactions. However, there is also quite a lot of chaos and complexities that get introduced because of the state of the industry. Many packages used by data scientists are from open source, and even if they are well curated, there is a growing tendency to pick out the cutting-edge or unstable packages and frameworks to accelerate analytics. Different data scientists may use different versions of runtimes, different Python or R versions, or even different versions of the same packages. Predominantly data scientists work on their laptops and it becomes difficult to reproduce their environments for use by others. Since data science is now a team sport across multiple personas, involving non-practitioners, traditional application developers, execs, and IT operators, how does an enterprise create a platform for productive cross-role collaboration?
Enterprises need a very reliable and repeatable process, especially when it results in something that affects their production environments. They also require a well managed approach that enables the graduation of an asset from development through a testing and staging process to production. Given the pace of businesses nowadays, the process needs to be quite agile and flexible too—even enabling an easy path to reversing a change. Compliance and audit processes require clear lineage and history as well as approval chains.
In the traditional software engineering world, this lifecycle has been well understood and best practices have been followed for ages. But what does it mean when you have non-programmers or users who are not really trained in software engineering philosophies or who perceive all of this as "big process" roadblocks in their daily work ? How do you we engage them in a productive manner and yet support enterprise requirements for reliability, tracking, and a clear continuous integration and delivery practice? The presenters, in this session, will bring up interesting techniques based on their user research, real life customer interviews, and productized best practices. The presenters also invite the audience to share their stories and best practices to make this a lively conversation.
Speaker
Sriram Srinivasan, Senior Technical Staff Member, Analytics Platform Architect, IBM
Machine Learning Operations Cababilities
Machine Learning Operations Cababilitiesdavidsh11 With Machine Learning Model Operationalization Management (MLOps), we want to provide an end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable ML-powered software.
DevOps for Machine Learning overview en-us
DevOps for Machine Learning overview en-useltonrodriguez11 This document discusses applying DevOps practices and principles to machine learning model development and deployment. It outlines how continuous integration (CI), continuous delivery (CD), and continuous monitoring can be used to safely deliver ML features to customers. The benefits of this approach include continuous value delivery, end-to-end ownership by data science teams, consistent processes, quality/cadence improvements, and regulatory compliance. Key aspects covered are experiment tracking, model versioning, packaging and deployment, and monitoring models in production.
Integrating Machine Learning Capabilities into your team
Integrating Machine Learning Capabilities into your teamCameron Vetter Machine Learning is here today and is quickly becoming an expected skill of development teams. As a technical leader on your team, you need to not only help your team learn how to do machine learning, but also select the right tools, integrate the tools into your tool chain, and understand how to deploy and version machine learning models.
This talk answers these questions using the Microsoft stack as an example. We will walk through my approach to integrating Machine Learning into a team. The topics covered include:
• Where to start, while minimizing investment and risk.
• The spectrum of tools from off the shelf to handcrafted.
• Packaging and deploying your model.
• Integrating your model into your system.
• Other considerations and risks.
You'll leave with my perspective on how to introduce a team to machine learning and how I recommend integrating machine learning into your software development toolkit.
TARGET AUDIENCE: Senior Developers, Architects, Technical Leaders
Strata parallel m-ml-ops_sept_2017
Strata parallel m-ml-ops_sept_2017Nisha Talagala Machine Learning in Production
The era of big data generation is upon us. Devices ranging from sensors to robots and sophisticated applications are generating increasing amounts of rich data (time series, text, images, sound, video, etc.). For such data to benefit a business’s bottom line, insights must be extracted, a process that increasingly requires machine learning (ML) and deep learning (DL) approaches deployed in production applications use cases.
Production ML is complicated by several challenges, including the need for two very distinct skill sets (operations and data science) to collaborate, the inherent complexity and uniqueness of ML itself, when compared to other apps, and the varied array of analytic engines that need to be combined for a practical deployment, often across physically distributed infrastructure. Nisha Talagala shares solutions and techniques for effectively managing machine learning and deep learning in production with popular analytic engines such as Apache Spark, TensorFlow, and Apache Flink.
Data Production Pipelines: Legacy, practices, and innovation
Data Production Pipelines: Legacy, practices, and innovationNatalino Busa Modern engineering requires machine learning engineers, who are needed to monitor and implement ETL and machine learning models in production. Natalino Busa shares technologies, techniques, and blueprints on how to robustly and reliably manage data science and ETL flows from inception to production.
In particular, Natalino explains how to solve one of the most annoying problems in modern data pipelines—migrating and managing legacy ETL—by generating Spark jobs from a textual representation (NLP and SQL). Natalino also demonstrates an open source web UI implemented in React that transforms high-level representations to Spark code and shows how users are able to capture and discover data in the organization by accessing a metadata service. Natalino also introduces the datalabframework, a Jupyter-powered lightweight framework that allows machine learning scientists and engineers to build a robust production ML system only using notebooks.
https://conferences.oreilly.com/strata/strata-sg/public/schedule/detail/64481
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DW Migration Webinar-March 2022.pptx
DW Migration Webinar-March 2022.pptxDatabricks The document discusses migrating a data warehouse to the Databricks Lakehouse Platform. It outlines why legacy data warehouses are struggling, how the Databricks Platform addresses these issues, and key considerations for modern analytics and data warehousing. The document then provides an overview of the migration methodology, approach, strategies, and key takeaways for moving to a lakehouse on Databricks.
Data Lakehouse Symposium | Day 1 | Part 1
Data Lakehouse Symposium | Day 1 | Part 1Databricks 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 2
Data Lakehouse Symposium | Day 1 | Part 2Databricks 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 2
Data Lakehouse Symposium | Day 2Databricks 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 4
Data Lakehouse Symposium | Day 4Databricks The document discusses the challenges of modern data, analytics, and AI workloads. Most enterprises struggle with siloed data systems that make integration and productivity difficult. The future of data lies with a data lakehouse platform that can unify data engineering, analytics, data warehousing, and machine learning workloads on a single open platform. The Databricks Lakehouse platform aims to address these challenges with its open data lake approach and capabilities for data engineering, SQL analytics, governance, and machine learning.
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
5 Critical Steps to Clean Your Data Swamp When Migrating Off of HadoopDatabricks 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.
Democratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized PlatformDatabricks 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
Learn to Use Databricks for Data Science
Learn to Use Databricks for Data ScienceDatabricks 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.
Why APM Is Not the Same As ML Monitoring
Why APM Is Not the Same As ML MonitoringDatabricks 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.
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch FixDatabricks 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.
Stage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI IntegrationDatabricks 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.
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorchDatabricks 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.
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on KubernetesDatabricks 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 and Unifying SciKit Learn and Apache Spark Pipelines
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.
Sawtooth Windows for Feature Aggregations
Sawtooth Windows for Feature AggregationsDatabricks 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.
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen SinkDatabricks We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark.
Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue
· Why?
o Custom queries on top a table; We load the data once and query N times
· Why not Structured Streaming
· Working Solution using Redis
Niche 2 : Distributed Counters
· Problems with Spark Accumulators
· Utilize Redis Hashes as distributed counters
· Precautions for retries and speculative execution
· Pipelining to improve performance
Re-imagine Data Monitoring with whylogs and Spark
Re-imagine Data Monitoring with whylogs and SparkDatabricks 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.
Raven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction QueriesDatabricks 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.
Processing Large Datasets for ADAS Applications using Apache Spark
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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.
Massive Data Processing in Adobe Using Delta Lake
Massive Data Processing in Adobe Using Delta LakeDatabricks 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
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(String FTW)
Data Manipulation using UDFs
Writer Worries and How to Wipe them Away
Staging Tables FTW
Datalake Replication Lag Tracking
Performance Time!
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MLOps Virtual Event: Automating ML at Scale
- 1. MLOps Virtual Event: Automating ML at Scale Matei Zaharia Chief Technologist, Databricks @matei_zaharia
- 2. ML is Transforming All Major Industries Healthcare Logistics Telecom Government Banking High Tech Oil & Gas Agriculture Retail Travel
- 3. But ML is Different from Traditional Software Traditional Software Goal: meet a functional specification Quality depends only on application code Pick one software stack Machine Learning Goal: optimize a metric (e.g. prediction accuracy) Quality depends on training data and tuning parameters Constantly evaluate and combine new libraries for the same task
- 4. So Operating ML is Complex! § Many teams and systems involved § Constantly update data & metrics § Hard to move from development to production environments Data Prep Training Deployment Raw Data ML ENGINEER APPLICATION DEVELOPER DATA ENGINEER
- 5. So Operating ML is Complex! § Many teams and systems involved § Constantly update data & metrics § Hard to move from development to production environments Data Prep Training Deployment Raw Data ML ENGINEER APPLICATION DEVELOPER DATA ENGINEER ML teams often spend >50% of time maintaining existing models
- 6. Response: ML Platforms Software to manage the ML development and operations process, from data to experimentation to production Examples: Google TFX, Facebook FBLearner, Uber Michelangelo, MLflow Typical functionality: ▪ Data management ▪ Experiment management ▪ Model management ▪ Deployment for inference ▪ Reproducibility ▪ Testing & monitoring All through a consistent interface!
- 7. Desirable Features for an ML Platform
- 8. Desirable Features for an ML Platform 1. Ease of adoption by data scientists, engineers, and model users ▪ How much work does it take to use? What ML libraries are supported? Etc.
- 9. Desirable Features for an ML Platform 1. Ease of adoption by data scientists, engineers, and model users ▪ How much work does it take to use? What ML libraries are supported? Etc. 2. Integration with data infrastructure to support data versioning, monitoring, and governance across data pipeline & ML steps
- 10. Desirable Features for an ML Platform 1. Ease of adoption by data scientists, engineers, and model users ▪ How much work does it take to use? What ML libraries are supported? Etc. 2. Integration with data infrastructure to support data versioning, monitoring, and governance across data pipeline & ML steps 3. Collaboration functions to enable sharing code, data, features, experiments and models in a central place (securely!)
- 11. Our MLOps Approach in Databricks § Every org’s requirements will be different, and will change over time § Provide a general platform that is easy to integrate with diverse tools Open source machine learning platform Transactional, versioned data lake storage Data science & ML workspace
- 12. In This Webinar § How we and other organizations perform MLOps at scale § Demos and experience from two customers § Live Q&A with presenters