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ODSC webinar "Kubeflow, MLFlow and Beyond — augmenting ML delivery" Stepan Pushkarev, Provectus
The document covers the integration and functionalities of Kubeflow and Hydrosphere.io in machine learning delivery. It outlines the components and workflows of Kubeflow, emphasizing its strengths and limitations, while also introducing Hydrosphere.io as a platform for managing ML models. Additionally, it provides a workshop plan for practical implementation of these tools in a production environment.
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ODSC webinar "Kubeflow, MLFlow and Beyond — augmenting ML delivery" Stepan Pushkarev, Provectus
- 1. Kubeflow, MLFlow andbeyond - augmenting ML delivery STEPAN PUSHKAREV ILNUR GARIFULLIN
- 2. Outline 1. Intro 2. Whyyet another Flow 3. Kubeflow overview 4. Hydrosphere.io overview Practice 1. Defining a pipeline and underlying worker containers 2. Run train-test-deploy ML pipeline with Kubeflow 3. Simulating production traffic 4. Monitoring data and domain drift 5. Closing the loop with automated re-training
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- 6. Machine Learning 5years ago Business Problem Data High hopes
- 7. Then somebody openeda black box…. High hopes
- 8. Machine Learning Workflow- whitening the box 1. Research 2. Data Preparation 3. Model Training 4. Model Cataloguing 5. Model Deployment 6. Model Integration Testing 7. Production Inferencing 8. Model Performance Monitoring 9. Model Maintenance
- 9. f(x) ML Workflow asa pure function Immutable Raw Dataset ML Service in prod Repeatable | Scalable | Observable Machine Learning Operations
- 10. What is Kubeflow? ●Began as Kubernetes template / blueprint for running Tensorflow ● Evolved into “Toolkit” - loosely coupled tools and blueprints for ML on Kubernetes
- 11. Kubeflow Pipelines -the very first original contribution Main components: 1. Python SDK 2. UI 3. Orchestrator 4. ML Metadata Service 5. Argo under the hood
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- 13. Kubeflow brings MLAwareness into CD 1. METADATA store and visualization components 2. Kubernetes native - enforcement of Docker-only pipelines 3. Strong believe in Kubernetes community Cons: 1. Still Low level. Needs Ops team to make it work. 2. No ML Workflow adapters, domain abstractions
- 14. Alternatives 1. Jenkins /Gitlab may close the gap with ML plugins 2. AirFlow may close the gap with ML Operators 3. AWS Step Functions - do it yourself 1. ML Flow - simple architecture, easier to start, local mode, experiments tracking is better. Could be run in Kube not not as native as Kubeflow. Databricks / Azure community.
- 15. What is Hydrosphere.io? Hydrosphere.iois a platform for ML Models Management. - Started as an open source Microservices native ML Serving - An exact value-add “tool” - a part of the Kubeflow toolkit - Now covers model Cataloguing, Deployment, Inferencing, Monitoring and Maintenance
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- 17. Data Prep TrainingCataloguing Deployment Integration Testing Production Inferencing Performance Monitoring Model Maintenance Tools Landscape Orchestrate
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- 19. Workshop plan -Part 1 - Forward path Data Prep Model Training Model Release Deploy to Stage Integration Testing Deploy to Prod
- 20. Class plan -Part 2 - Production Flow Simulate Prod Load Production Inferencing Accuracy Monitoring Performance Monitoring Predictions Tracing
- 21. Class plan -Part 3 - Maintenance Flow Data Prep Model Training Model Release Deploy to Stage Integration Testing Deploy to Prod Production Inferencing Production Metrics feedback loop
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- 25. Contact Us GENERAL INQUIRIES hydrosphere.io info@hydrosphere.io linkedin.com/company/hydrospherebigdata twitter.com/hydrospheredata facebook.com/hydrosphere.io ADDRESS 125University Avenue, Suite 290 Palo Alto, CA, 94301 tel: 650-521-7875 BUSINESS AND TECHNICAL Stepan Pushkarev spushkarev@hydrosphere.io Ilnur Garifullin igarifullin@provectus.com
Editor's Notes
- #3 Hi everyone, thank you for coming, we are happy to welcome you on our webinar dedicated to leveraging KubeFlow ML Workflow automation. Let’s take a look at the scope we will cover today. In the first section we would like to align everybody on terminology and ML workflow best practices to get on the same page. In the second part we’ll revise tools (in particular Kubeflow and Hydrosphere) that will help us to automate the steps from the first part. After that we will make a deep dive into that topic and demonstrate internal parts of automation process. In conclusion we will be happy to answer your questions.
- #4 We will start with the general machine learning workflow.
- #5 We will start with the general machine learning workflow.
- #6 We will start with the general machine learning workflow.
- #7 We will start with the general machine learning workflow.
- #8 We will start with the general machine learning workflow.
- #9 We will start with the general machine learning workflow.
- #10 We will start with the general machine learning workflow.
- #11 So, what is a Kubeflow? Kubeflow began as Kubernetes template or blueprint for running Tensorflow. Over time it evolved into a “Toolkit” - loosely coupled tools and blueprints for ML on Kubernetes The “toolkit” and “loosely coupled” are keywords here. TensorFlow, Katib and other tools are independently developed tools deployed on Kubernetes and orchestrated by Argo. Kubeflow Pipelines is the latest addition to the ecosystem - a nice Python DSL on top of Argo yaml configs.
- #19 Hi everyone, thank you for coming, we are happy to welcome you on our webinar dedicated to leveraging KubeFlow ML Workflow automation. Let’s take a look at the scope we will cover today. In the first section we would like to align everybody on terminology and ML workflow best practices to get on the same page. In the second part we’ll revise tools (in particular Kubeflow and Hydrosphere) that will help us to automate the steps from the first part. After that we will make a deep dive into that topic and demonstrate internal parts of automation process. In conclusion we will be happy to answer your questions.
- #23 Hi everyone, thank you for coming, we are happy to welcome you on our webinar dedicated to leveraging KubeFlow ML Workflow automation. Let’s take a look at the scope we will cover today. In the first section we would like to align everybody on terminology and ML workflow best practices to get on the same page. In the second part we’ll revise tools (in particular Kubeflow and Hydrosphere) that will help us to automate the steps from the first part. After that we will make a deep dive into that topic and demonstrate internal parts of automation process. In conclusion we will be happy to answer your questions.