The DevOps landscape is well-understood and tools can be categorised by how they support the dev-build-deploy-monitor workflow. By comparison the MLOps landscape is complex and hard to understand. This presentation looks at the ML workflow that MLOps supports so that we can better understand the MLOps landscape.
MLOps Bridging the gap between Data Scientists and Ops.Knoldus Inc.
Through this session we're going to introduce the MLOps lifecycle and discuss the hidden loopholes that can affect the MLProject. Then we are going to discuss the ML Model lifecycle and discuss the problem with training. We're going to introduce the MLFlow Tracking module in order to track the experiments.
How to revamp machine learning pipelines with MLOps
mlflow demo video 1: https://youtu.be/3q6JXcW_lOI
airflow demo video 2: https://youtu.be/bzMn6kN-yWg
tensowflow js model serving demo video 3: https://youtu.be/M_U99Pmfaf4
Deploying and managing machine learning models at scale introduces new complexities. Fortunately, there are tools that simplify this process. In this talk we walk you through an end-to-end hands on example showing how you can go from research to production without much complexity by leveraging the Seldon Core and MLflow frameworks. We will train a set of ML models, and we will showcase a simple way to deploy them to a Kubernetes cluster through sophisticated deployment methods, including canary deployments, shadow deployments and we’ll touch upon richer ML graphs such as explainer deployments.
The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
How to choose correct framework and define your manifesto for technology practices around Machine Learning Journey.
Kubernetes being successor in this space, Seldom Core and Kubeflow is truly winner in this Segment.
MLOps Bridging the gap between Data Scientists and Ops.Knoldus Inc.
Through this session we're going to introduce the MLOps lifecycle and discuss the hidden loopholes that can affect the MLProject. Then we are going to discuss the ML Model lifecycle and discuss the problem with training. We're going to introduce the MLFlow Tracking module in order to track the experiments.
How to revamp machine learning pipelines with MLOps
mlflow demo video 1: https://youtu.be/3q6JXcW_lOI
airflow demo video 2: https://youtu.be/bzMn6kN-yWg
tensowflow js model serving demo video 3: https://youtu.be/M_U99Pmfaf4
Deploying and managing machine learning models at scale introduces new complexities. Fortunately, there are tools that simplify this process. In this talk we walk you through an end-to-end hands on example showing how you can go from research to production without much complexity by leveraging the Seldon Core and MLflow frameworks. We will train a set of ML models, and we will showcase a simple way to deploy them to a Kubernetes cluster through sophisticated deployment methods, including canary deployments, shadow deployments and we’ll touch upon richer ML graphs such as explainer deployments.
The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
How to choose correct framework and define your manifesto for technology practices around Machine Learning Journey.
Kubernetes being successor in this space, Seldom Core and Kubeflow is truly winner in this Segment.
MLOps and Reproducible ML on AWS with Kubeflow and SageMakerProvectus
Looking to implement MLOps using AWS services and Kubeflow? Come and learn about machine learning from the experts of Provectus and Amazon Web Services (AWS)!
Businesses recognize that machine learning projects are important but go beyond just building and deploying models, which is mostly done by organizations. Successful ML projects entail a complete lifecycle involving ML, DevOps, and data engineering and are built on top of ML infrastructure.
AWS and Amazon SageMaker provide a foundation for building infrastructure for machine learning while Kubeflow is a great open source project, which is not given enough credit in the AWS community. In this webinar, we show how to design and build an end-to-end ML infrastructure on AWS.
Agenda
- Introductions
- Case Study: GoCheck Kids
- Overview of AWS Infrastructure for Machine Learning
- Provectus ML Infrastructure on AWS
- Experimentation
- MLOps
- Feature Store
Intended Audience
Technology executives & decision makers, manager-level tech roles, data engineers & data scientists, ML practitioners & ML engineers, and developers
Presenters
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Qingwei Li, ML Specialist Solutions Architect, AWS
Feel free to share this presentation with your colleagues and don't hesitate to reach out to us at info@provectus.com if you have any questions!
REQUEST WEBINAR: https://provectus.com/webinar-mlops-and-reproducible-ml-on-aws-with-kubeflow-and-sagemaker-aug-2020/
MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.
MLOps with serverless architectures (October 2018)Julien SIMON
Talk @ AWS Loft Stockholm, 23/10/2018
But why?
A quick recap on Amazon SageMaker
A quick recap on serverless architectures
Open Source tools: AWS Chalice, Serverless Framework
Demos
Resources
Apache Liminal (Incubating)—Orchestrate the Machine Learning PipelineDatabricks
Apache Liminal is an end-to-end platform for data engineers & scientists, allowing them to build, train and deploy machine learning models in a robust and agile way. The platform provides the abstractions and declarative capabilities for data extraction & feature engineering followed by model training and serving; using standard tools and libraries (e.g. Airflow, K8S, Spark, scikit-learn, etc.).
Given at the MLOps. Summit 2020 - I cover the origins of MLOps in 2018, how MLOps has evolved from 2018 to 2020, and what I expect for the future of MLOps
MLflow is aiming to stabilize its API in version 1.0 this spring and add a number of other new features. In this talk, we'll share some of the features we have in mind for the rest of the year. These include ongoing work such as a database store for the tracking server and Docker project packaging, as well as new improvements in multi-step workflows, model management, and production monitoring.
MLOps (a compound of “machine learning” and “operations”) is a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Similar to the DevOps term in the software development world, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire ML lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics.
To watch the full presentation click here: https://info.cnvrg.io/mlopsformachinelearning
In this webinar, we’ll discuss core practices in MLOps that will help data science teams scale to the enterprise level. You’ll learn the primary functions of MLOps, and what tasks are suggested to accelerate your teams machine learning pipeline. Join us in a discussion with cnvrg.io Solutions Architect, Aaron Schneider, and learn how teams use MLOps for more productive machine learning workflows.
- Reduce friction between science and engineering
- Deploy your models to production faster
- Health, diagnostics and governance of ML models
- Kubernetes as a core platform for MLOps
- Support advanced use-cases like continual learning with MLOps
Robust MLOps with Open-Source: ModelDB, Docker, Jenkins, and PrometheusManasi Vartak
These are slides from Manasi Vartak's Strata Talk in March 2020 on Robust MLOps with Open-Source.
* Introduction to talk
* What is MLOps?
* Building an MLOps Pipeline
* Real-world Simulations
* Let’s fix the pipeline
* Wrap-up
MLOps and Data Quality: Deploying Reliable ML Models in ProductionProvectus
Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure.
For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).
Agenda
- Data Quality and why it matters
- Challenges and solutions of Data Testing
- Challenges and solutions of Model Testing
- MLOps pipelines and why they matter
- How to expand validation pipelines for Data Quality
Machine Learning operations brings data science to the world of devops. Data scientists create models on their workstations. MLOps adds automation, validation and monitoring to any environment including machine learning on kubernetes. In this session you hear about latest developments and see it in action.
Hamburg Data Science Meetup - MLOps with a Feature StoreMoritz Meister
MLOps is a trend in machine learning (ML) engineering that unifies ML system development (Dev) and ML system operation (Ops). Some ML lifecycle frameworks, such as TensorFlow Extended, are based around end-to-end pipelines that start with raw data and end in production models. During this talk we will introduce the concept of a feature store as the missing piece of ML infrastructure that enables faster lower cost deployment of models. We will show how the Hopsworks Feature Store - factors monolithic end-to-end ML pipelines into feature and model training pipelines that can each run at different cadences. We will show examples of ingestion and training pipelines including hyperparameter optimization and model deployment.
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2S7lDiS.
Sasha Rosenbaum shows how a CI/CD pipeline for Machine Learning can greatly improve both productivity and reliability. Filmed at qconsf.com.
Sasha Rosenbaum is a Program Manager on the Azure DevOps engineering team, focused on improving the alignment of the product with open source software. She is a co-organizer of the DevOps Days Chicago and the DeliveryConf conferences, and recently published a book on Serverless computing in Azure with .NET.
Команда Data Phoenix Events приглашает всех, 17 августа в 19:00, на первый вебинар из серии "The A-Z of Data", который будет посвящен MLOps. В рамках вводного вебинара, мы рассмотрим, что такое MLOps, основные принципы и практики, лучшие инструменты и возможные архитектуры. Мы начнем с простого жизненного цикла разработки ML решений и закончим сложным, максимально автоматизированным, циклом, который нам позволяет реализовать MLOps.
https://dataphoenix.info/the-a-z-of-data/
https://dataphoenix.info/the-a-z-of-data-introduction-to-mlops/
Managers guide to effective building of machine learning productsGianmario Spacagna
Part 1/2 (Managers)
Data and Machine Learning (ML) technologies are now widespread and adopted by literally all industries. Although recent advancements in the field have reached an unthinkable level of maturity, many organizations still struggle with turning these advances into tangible profits. Unfortunately, many ML projects get stuck in a proof-of-concept stage without ever reaching customers and generating revenue. In order to effectively adopt ML technologies, enterprises need to build the right business cases as well as to be ready to face the inevitable challenges. In this talk, we will share common pitfalls, lessons learned, and best practices, while building different enterprise products. In particular, we will focus on the generic use case of ML as the core technology enabling customer-facing products regardless of the specific industry or application.
You will:
Understand if ML is the right solution for your business and set the right expectations;
Deal with the additional uncertainty of ML projects with respect to traditional software;
Build a balanced ML team and cover the broad spectrum of skills;
Know how to apply the scientific workflow in an agile development framework;
Learn how to turn research into production systems including engineering practices and tools;
Be able to leverage modern cloud and serverless architecture for scalable, autonomous and cheaper deployments.
MLOps Virtual Event | Building Machine Learning Platforms for the Full LifecycleDatabricks
Successfully building a machine learning model is hard enough. Reproducing your results at scale — enabling others to reproduce pipelines, comparing results from other versions, moving models into production, redeploying and rolling out updated models — is exponentially harder. To address these challenges and accelerate innovation, many companies are building custom “ML platforms” to automate the end-to-end ML lifecycle.
Watch a replay of this MLOps Virtual Event to hear more about the latest developments and best practices for managing the full ML lifecycle on Databricks with MLflow. We covered a checklist of capabilities you’ll need, common pitfalls, technological and organizational challenges, and how to overcome them.
https://www.youtube.com/playlist?list=PLTPXxbhUt-YUFNBwBsSIlknoNbS7GExZw
Why is dev ops for machine learning so differentRyan Dawson
DevOps instincts tend to be shaped by what has worked well before. Instincts derived from mainstream software development projects get challenged when we turn to enabling machine learning projects. The key reasons are that the development/delivery workflow is different and the kind of software artefacts involved are different. We will explore the differences and look at emerging open source projects in order to appreciate why the DevOps for machine learning space is growing and the needs that it addresses.
ODSC webinar "Kubeflow, MLFlow and Beyond — augmenting ML delivery" Stepan Pu...Provectus
What's a machine learning workflow? What open source tools can you use to automate ML workflow?
Reproducible ML pipelines in research and production with monitoring insights from live inference clusters could enable and accelerate the delivery of AI solutions for enterprises. There is a growing ecosystem of tools that augment researchers and machine learning engineers in their day to day operations.
Still, there are big gaps in the machine learning workflow when it comes to training dataset versioning, training performance and metadata tracking, integration testing, inferencing quality monitoring, bias detection, concept drift detection and other aspects that prevent the adoption of AI in organizations of all sizes.
MLOps and Reproducible ML on AWS with Kubeflow and SageMakerProvectus
Looking to implement MLOps using AWS services and Kubeflow? Come and learn about machine learning from the experts of Provectus and Amazon Web Services (AWS)!
Businesses recognize that machine learning projects are important but go beyond just building and deploying models, which is mostly done by organizations. Successful ML projects entail a complete lifecycle involving ML, DevOps, and data engineering and are built on top of ML infrastructure.
AWS and Amazon SageMaker provide a foundation for building infrastructure for machine learning while Kubeflow is a great open source project, which is not given enough credit in the AWS community. In this webinar, we show how to design and build an end-to-end ML infrastructure on AWS.
Agenda
- Introductions
- Case Study: GoCheck Kids
- Overview of AWS Infrastructure for Machine Learning
- Provectus ML Infrastructure on AWS
- Experimentation
- MLOps
- Feature Store
Intended Audience
Technology executives & decision makers, manager-level tech roles, data engineers & data scientists, ML practitioners & ML engineers, and developers
Presenters
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Qingwei Li, ML Specialist Solutions Architect, AWS
Feel free to share this presentation with your colleagues and don't hesitate to reach out to us at info@provectus.com if you have any questions!
REQUEST WEBINAR: https://provectus.com/webinar-mlops-and-reproducible-ml-on-aws-with-kubeflow-and-sagemaker-aug-2020/
MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.
MLOps with serverless architectures (October 2018)Julien SIMON
Talk @ AWS Loft Stockholm, 23/10/2018
But why?
A quick recap on Amazon SageMaker
A quick recap on serverless architectures
Open Source tools: AWS Chalice, Serverless Framework
Demos
Resources
Apache Liminal (Incubating)—Orchestrate the Machine Learning PipelineDatabricks
Apache Liminal is an end-to-end platform for data engineers & scientists, allowing them to build, train and deploy machine learning models in a robust and agile way. The platform provides the abstractions and declarative capabilities for data extraction & feature engineering followed by model training and serving; using standard tools and libraries (e.g. Airflow, K8S, Spark, scikit-learn, etc.).
Given at the MLOps. Summit 2020 - I cover the origins of MLOps in 2018, how MLOps has evolved from 2018 to 2020, and what I expect for the future of MLOps
MLflow is aiming to stabilize its API in version 1.0 this spring and add a number of other new features. In this talk, we'll share some of the features we have in mind for the rest of the year. These include ongoing work such as a database store for the tracking server and Docker project packaging, as well as new improvements in multi-step workflows, model management, and production monitoring.
MLOps (a compound of “machine learning” and “operations”) is a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Similar to the DevOps term in the software development world, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire ML lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics.
To watch the full presentation click here: https://info.cnvrg.io/mlopsformachinelearning
In this webinar, we’ll discuss core practices in MLOps that will help data science teams scale to the enterprise level. You’ll learn the primary functions of MLOps, and what tasks are suggested to accelerate your teams machine learning pipeline. Join us in a discussion with cnvrg.io Solutions Architect, Aaron Schneider, and learn how teams use MLOps for more productive machine learning workflows.
- Reduce friction between science and engineering
- Deploy your models to production faster
- Health, diagnostics and governance of ML models
- Kubernetes as a core platform for MLOps
- Support advanced use-cases like continual learning with MLOps
Robust MLOps with Open-Source: ModelDB, Docker, Jenkins, and PrometheusManasi Vartak
These are slides from Manasi Vartak's Strata Talk in March 2020 on Robust MLOps with Open-Source.
* Introduction to talk
* What is MLOps?
* Building an MLOps Pipeline
* Real-world Simulations
* Let’s fix the pipeline
* Wrap-up
MLOps and Data Quality: Deploying Reliable ML Models in ProductionProvectus
Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure.
For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).
Agenda
- Data Quality and why it matters
- Challenges and solutions of Data Testing
- Challenges and solutions of Model Testing
- MLOps pipelines and why they matter
- How to expand validation pipelines for Data Quality
Machine Learning operations brings data science to the world of devops. Data scientists create models on their workstations. MLOps adds automation, validation and monitoring to any environment including machine learning on kubernetes. In this session you hear about latest developments and see it in action.
Hamburg Data Science Meetup - MLOps with a Feature StoreMoritz Meister
MLOps is a trend in machine learning (ML) engineering that unifies ML system development (Dev) and ML system operation (Ops). Some ML lifecycle frameworks, such as TensorFlow Extended, are based around end-to-end pipelines that start with raw data and end in production models. During this talk we will introduce the concept of a feature store as the missing piece of ML infrastructure that enables faster lower cost deployment of models. We will show how the Hopsworks Feature Store - factors monolithic end-to-end ML pipelines into feature and model training pipelines that can each run at different cadences. We will show examples of ingestion and training pipelines including hyperparameter optimization and model deployment.
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2S7lDiS.
Sasha Rosenbaum shows how a CI/CD pipeline for Machine Learning can greatly improve both productivity and reliability. Filmed at qconsf.com.
Sasha Rosenbaum is a Program Manager on the Azure DevOps engineering team, focused on improving the alignment of the product with open source software. She is a co-organizer of the DevOps Days Chicago and the DeliveryConf conferences, and recently published a book on Serverless computing in Azure with .NET.
Команда Data Phoenix Events приглашает всех, 17 августа в 19:00, на первый вебинар из серии "The A-Z of Data", который будет посвящен MLOps. В рамках вводного вебинара, мы рассмотрим, что такое MLOps, основные принципы и практики, лучшие инструменты и возможные архитектуры. Мы начнем с простого жизненного цикла разработки ML решений и закончим сложным, максимально автоматизированным, циклом, который нам позволяет реализовать MLOps.
https://dataphoenix.info/the-a-z-of-data/
https://dataphoenix.info/the-a-z-of-data-introduction-to-mlops/
Managers guide to effective building of machine learning productsGianmario Spacagna
Part 1/2 (Managers)
Data and Machine Learning (ML) technologies are now widespread and adopted by literally all industries. Although recent advancements in the field have reached an unthinkable level of maturity, many organizations still struggle with turning these advances into tangible profits. Unfortunately, many ML projects get stuck in a proof-of-concept stage without ever reaching customers and generating revenue. In order to effectively adopt ML technologies, enterprises need to build the right business cases as well as to be ready to face the inevitable challenges. In this talk, we will share common pitfalls, lessons learned, and best practices, while building different enterprise products. In particular, we will focus on the generic use case of ML as the core technology enabling customer-facing products regardless of the specific industry or application.
You will:
Understand if ML is the right solution for your business and set the right expectations;
Deal with the additional uncertainty of ML projects with respect to traditional software;
Build a balanced ML team and cover the broad spectrum of skills;
Know how to apply the scientific workflow in an agile development framework;
Learn how to turn research into production systems including engineering practices and tools;
Be able to leverage modern cloud and serverless architecture for scalable, autonomous and cheaper deployments.
MLOps Virtual Event | Building Machine Learning Platforms for the Full LifecycleDatabricks
Successfully building a machine learning model is hard enough. Reproducing your results at scale — enabling others to reproduce pipelines, comparing results from other versions, moving models into production, redeploying and rolling out updated models — is exponentially harder. To address these challenges and accelerate innovation, many companies are building custom “ML platforms” to automate the end-to-end ML lifecycle.
Watch a replay of this MLOps Virtual Event to hear more about the latest developments and best practices for managing the full ML lifecycle on Databricks with MLflow. We covered a checklist of capabilities you’ll need, common pitfalls, technological and organizational challenges, and how to overcome them.
https://www.youtube.com/playlist?list=PLTPXxbhUt-YUFNBwBsSIlknoNbS7GExZw
Why is dev ops for machine learning so differentRyan Dawson
DevOps instincts tend to be shaped by what has worked well before. Instincts derived from mainstream software development projects get challenged when we turn to enabling machine learning projects. The key reasons are that the development/delivery workflow is different and the kind of software artefacts involved are different. We will explore the differences and look at emerging open source projects in order to appreciate why the DevOps for machine learning space is growing and the needs that it addresses.
ODSC webinar "Kubeflow, MLFlow and Beyond — augmenting ML delivery" Stepan Pu...Provectus
What's a machine learning workflow? What open source tools can you use to automate ML workflow?
Reproducible ML pipelines in research and production with monitoring insights from live inference clusters could enable and accelerate the delivery of AI solutions for enterprises. There is a growing ecosystem of tools that augment researchers and machine learning engineers in their day to day operations.
Still, there are big gaps in the machine learning workflow when it comes to training dataset versioning, training performance and metadata tracking, integration testing, inferencing quality monitoring, bias detection, concept drift detection and other aspects that prevent the adoption of AI in organizations of all sizes.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...DataWorks Summit
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
Pitfalls of machine learning in productionAntoine Sauray
Going from POC to production with Machine Learning can lead to many unexpected problems. We explore some of them in this presentation at the Nantes Machine Learning Meetup.
Advanced Model Inferencing leveraging Kubeflow Serving, KNative and IstioAnimesh Singh
Model Inferencing use cases are becoming a requirement for models moving into the next phase of production deployments. More and more users are now encountering use cases around canary deployments, scale-to-zero or serverless characteristics. And then there are also advanced use cases coming around model explainability, including A/B tests, ensemble models, multi-armed bandits, etc.
In this talk, the speakers are going to detail how to handle these use cases using Kubeflow Serving and the native Kubernetes stack which is Istio and Knative. Knative and Istio help with autoscaling, scale-to-zero, canary deployments to be implemented, and scenarios where traffic is optimized to the best performing models. This can be combined with KNative eventing, Istio observability stack, KFServing Transformer to handle pre/post-processing and payload logging which consequentially can enable drift and outlier detection to be deployed. We will demonstrate where currently KFServing is, and where it's heading towards.
Continuous Delivery of Deep Transformer-Based NLP Models Using MLflow and AWS...Databricks
Transformer-based pretrained language models such as BERT, XLNet, Roberta and Albert significantly advance the state-of-the-art of NLP and open doors for solving practical business problems with high performance transfer learning. However, operationalizing these models with production-quality continuous integration/ delivery (CI/CD) end-to-end pipelines that cover the full machine learning life cycle stages of train, test, deploy and serve while managing associated data and code repositories is still a challenging task.
Continuous Delivery of Deep Transformer-Based NLP Models Using MLflow and AWS...Databricks
Transformer-based pre-trained language models such as BERT, XLNet, Roberta and Albert significantly advance the state-of-the-art of NLP and open doors for solving practical business problems with high-performance transfer learning. However, operationalizing these models with production-quality continuous integration/ delivery (CI/CD) end-to-end pipelines that cover the full machine learning life cycle stages of train, test, deploy and serve while managing associated data and code repositories is still a challenging task. In this presentation, we will demonstrate how we use MLflow and AWS Sagemaker to productionize deep transformer-based NLP models for guided sales engagement scenarios at the leading sales engagement platform, Outreach.io.
Trenowanie i wdrażanie modeli uczenia maszynowego z wykorzystaniem Google Clo...Sotrender
Okej, mam już mój świetny model w Notebooku, co dalej? Większość kursów i źródeł dotyczących uczenia maszynowego dobrze przygotowuje nas do implementacji algorytmów uczenia maszynowego i budowy mniej lub bardziej skomplikowanych modeli. Jednak w większości przypadków model jest jedynie małym fragmentem większego systemu, a jego wdrożenie i utrzymywanie okazuje się w praktyce procesem czasochłonnym i generującym rozmaite błędy. Problem potęguje się kiedy mamy do sproduktyzowania nie jeden, a więcej modeli. Choć z roku na rok powstaje coraz więcej narzędzi i platform do usprawnienia tego procesu, jest to zagadnienie któremu wciąż poświęca się stosunkowo mało uwagi.
W mojej prezentacji przedstawię jakich podejść, dobrych praktyk oraz narzędzi i usług Google Cloud Platform używamy w Sotrender do efektywnego trenowania i produktyzacji naszych modeli ML, służących do analizy danych z mediów społecznościowych. Omówię na które aspekty DevOps zwracamy uwagę w kontekście wytwarzania produktów opartych o modele ML (MLOps) i jak z wykorzystaniem Google Cloud Platform można je w łatwy sposób wdrożyć w swoim startupie lub firmie.
Prezentacja Macieja Pieńkosza z Sotrendera poczas Data Science Summit 2020
What MLflow is; what problem it solves for machine learning lifecycle; and how it solves; How it will be used with Databricks; and CI/CD pipeline with Databricks.
While the adoption of machine learning and deep learning techniques continue to grow, many organizations find it difficult to actually deploy these sophisticated models into production. It is common to see data scientists build powerful models, yet these models are not deployed because of the complexity of the technology used or lack of understanding related to the process of pushing these models into production.
As part of this talk, I will review several deployment design patterns for both real-time and batch use cases. I’ll show how these models can be deployed as scalable, distributed deployments within the cloud, scaled across hadoop clusters, as APIs, and deployed within streaming analytics pipelines. I will also touch on topics related to security, end-to-end governance, pitfalls, challenges, and useful tools across a variety of platforms. This presentation will involve demos and sample code for the the deployment design patterns.
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GDG Cloud Southlake #16: Priyanka Vergadia: Scalable Data Analytics in Google...James Anderson
Do you know The Cloud Girl? She makes the cloud come alive with pictures and storytelling.
The Cloud Girl, Priyanka Vergadia, Chief Content Officer @Google, joins us to tell us about Scaleable Data Analytics in Google Cloud.
Maybe, with her explanation, we'll finally understand it!
Priyanka is a technical storyteller and content creator who has created over 300 videos, articles, podcasts, courses and tutorials which help developers learn Google Cloud fundamentals, solve their business challenges and pass certifications! Checkout her content on Google Cloud Tech Youtube channel.
Priyanka enjoys drawing and painting which she tries to bring to her advocacy.
Check out her website The Cloud Girl: https://thecloudgirl.dev/ and her new book: https://www.amazon.com/Visualizing-Google-Cloud-Illustrated-References/dp/1119816327
Strata CA 2019: From Jupyter to Production Manu MukerjiManu Mukerji
Proposed title
From Jupyter to production
Description of the presentation
Jupyter is very popular for data science, data exploration and visualization, this talk is about how to use it in for AI/ML in a production environment.
General Flow of talk:
How things can go wrong with QA, Production releases when using a notebook
Common Jupyter ML examples
Standard ML flow
Training in production
Model creation
Testing in production
Papermill and Jupyter
Production workflows with Sagemaker
Speaker
Manu Mukerji is senior director of data, machine learning, and analytics at 8×8. Manu’s background lies in cloud computing and big data, working on systems handling billions of transactions per day in real time. He enjoys building and architecting scalable, highly available data solutions and has extensive experience working in online advertising and social media.
MLFlow: Platform for Complete Machine Learning Lifecycle Databricks
Description
Data Science and ML development bring 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.
MLflow addresses some of these challenges during an ML model development cycle.
Abstract
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 even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this session, we introduce 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.
With a short demo, you see a complete ML model life-cycle example, you will walk away with: MLflow concepts and abstractions for models, experiments, and projects How to get started with MLFlow Using tracking Python APIs during model training Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
Similar to Why is dev ops for machine learning so different - dataxdays (20)
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Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
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The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
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Understanding Globus Data Transfers with NetSageGlobus
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2. Outline
1. MLOps Landscape
2. Data Science vs Programming
3. Traditional Programming E2E Workflow
4. Intro to ML E2E Workflow
5. MLOps Topics
a. Training
b. Serving
c. Monitoring
6. Advanced MLOps Challenges
7. Review
6. Running software performs actions in response to inputs.
Traditional programming codifies actions as explicit rules
ML does not codify explicitly.
Instead rules are indirectly set by capturing patterns from
data.
Different problem domains - ML more applicable to focused
numerical problems.
Why So Different?
7. Traditional programming
Think of old terminal systems
Start with hello world and add control
structures
Examples
Data Science
Classification problems (e.g. cat or not cat)
Regression problems (e.g. sales from ad
spend)
Start with MNIST or kaggle
12. Dev Build Journey
Compilation
Calculator user story
As a lazy person, I want to put numerical
operations into a screen so that I don’t have
to work out the answers.
13. ML Build Journey
Training Prediction
Training
Tracking
Data
Serving
Batch
E2E
Data Science Quesstion
Can we estimate/set/banchmark
employee pay from this data? Frameworks
14. ML is Different - Key Points
Training data and code together drive fitting
Closest thing to executable is a trained/weighted model (can
vary with toolkit)
Retraining can be necessary (e.g. online shop and fashion
trends)
Lots of data, long-running jobs
15. 1. User Story
2. Write code
3. Submit PR
4. Tests run automatically
5. Review and merge
6. New version builds
7. Built executable deployed to environment
8. Further tests
9. Promote to next environment
10. More tests etc.
11. PROD
12. Monitor - stacktraces or error codes
Docker as packaging. Driver is a code change (git)
Traditional Dev Workflow
16. Driver might be a code change. Or new data.
Data not in git.
More experimental - data driven and you’ve only a sample
of data.
Testing for quantifiable performance, not pass/fail.
Let’s focus on offline learning to simplify.
ML Workflows - Primer
17. ML E2E Workflow Intro
1. Data inputs and outputs. Preprocessed. Large.
2. Try stuff locally with a slice.
3. Try with more data as long-running experiments.
4. Collaboration - often in jupyter & git
5. Model may be pickled/serialized
6. Integrate into a running app e.g. add REST API
(serving)
7. Integration test with app.
8. Rollout & monitor performance metrics
18. Metrics Example
Online store example
A/B test
A leads to more conversions
But…
More negative reviews? Bounce-rate?
Interaction-level? Latency?
Krishen Siew - quora
20. Role of MLOps
Empower teams and break down silos
Provide ways to collaborate/self-serve
21. New Territory
Special challenges for ML.
No clear standards yet. We’ll drill into:
1. Training - slice of data, train a weighted model to
make predictions on unseen data.
2. Serving - call with HTTP.
3. Rollout and Monitoring - making sure it performs.
22. For long-running, intensive training jobs there’s
kubeflow pipelines, polyaxon, mlflow…
Broken into steps incl. cleaning and transformation (pre-
processing).
1 Training/Experimentation
23. Model Training
Each step can be long-running
Continuous Delivery for Machine Learning - martinfowler.com
27. Training and CI
Some training platforms have CI integration.
Result of a run could be a model. So
analogous to a CI build of an executable.
But how to say that the new version is
‘good’?
28. 2 Serving
Serving = use model via HTTP. Offline/batch is different.
Some platforms have serving or there’s dedicated solutions.
Seldon, Tensorflow Serving, AzureML, SageMaker
Often package the model and host (bucket) so the serving
solution can run it.
Serving can support rollout & monitoring.
29. Seldon ML Serving
apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
name: sklearn
spec:
name: iris
predictors:
- graph:
children: []
implementation: SKLEARN_SERVER
modelUri: gs://seldon-models/sklearn/iris
name: classifier
name: default
replicas: 1
Open Source
K8s custom resource
Pods created to serve http
Docker option too
Data scientists like pickles
30. 3 Rollout and Monitoring
ML model trained on sample - need to keep checking with new data coming in
Rollout strategies:
Canary = % of traffic to new version as check
A/B Test = % split between versions for longer to monitor performance
Shadowing = All traffic to old and new model. Only the live model’s responses are used
31. Canary with Seldon
kind: SeldonDeployment
apiVersion: machinelearning.seldon.io/v1alpha2
metadata:
name: skiris
namespace: default
creationTimestamp:
spec:
name: skiris
predictors:
- name: default
graph:
name: skiris-default
implementation: SKLEARN_SERVER
modelUri: gs://seldon-models/sklearn/iris
replicas: 1
- name: canary
graph:
name: skiris-canary
implementation: XGBOOST_SERVER
modelUri: gs://seldon-models/xgboost/iris
replicas: 1
Traffic-splitting more typically
defined in gateway config.
Very common in ML.
In serving not gateway so data
scientist can define rollout.
36. Advanced Topics - Governance
● Explainability - why did it predict that?
○ Some orgs sticking to whitebox techniques - not neural nets
○ Blackbox is possible
● Provenance & Reproducibility (associating models to training runs to data to triggers)
○ Data versioning adds complexity
○ Competing tools for metadata
○ No agreed standards yet
● Bias & ethics
● Adversarial attacks
37. Summary
MLOps is new terrain.
ML workflows exploratory & data-driven.
MLOps enables ML workflows with:
● Data and compute-intensive experiments and training
● Artifact tracking
● Rollout strategies to work with monitoring
● Monitoring tools
Expand on metrics. Perhaps you’re recommending really controversial productions. Or maybe you’re using annoying pop-ups for suggestions.
So we’re seeing that this MLOps stuff is complicated and different from traditional DevOps. One challenge of this is that Data Science and DevOps can be different silos in many organisations and sometimes with a filter in between. So you get situations where a python pickle file ends up being passed to the DevOps team without any context. So naturally the team that is meant to run the model in production is like ‘what is this?’ For that situation this cartoon depicts a pretty reasonable reaction.
Other companies have a more mature setup. Here see more particular specialisms in play. In the bottom left we’ve got the data engineers
