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MLOps with Kubeflow

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Saurabh Kaushik
Saurabh Kaushik

MLOps with Kubeflow

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MLOps with Saurabh Kaushik @saurabhkaushik
About Me: Saurabh Kaushik @saurabhkaushik • Director, Product Engineering Management @Eureka.AI • Help Telco's to monetize their data using AI and Data Products • Engineered and deployed about 20+ AI Product Solutions • Experience: 20+ years in various roles (Consultant/Lead/Architect/Manager/Director) • Domain: FinTech, AdTech, MarTech • Industry: Telco, Banking, Financial, Retail, CPG • Tech: Data Science, ML, DL, NLP, Big Data, Java, Python, Full Stack • Org: Products, Enterprise, Service, Tech Startups • Speakers: Product School, NASCOM, World Startup Expo, Institute of Product Leadership, IIMB, TechGigs • Hobbies: Tennis, Piano, Building Bots (Botreload.com)
Why do we need MLOps?
Most Data Scientist View of World… 4 Love to be in this zone!
How can someone take care of all these… 5 How do I auto scale my each stage independently? How can I choose different tool for different stage of pipeline? How can I run my workload seamlessly across environments? How can I deploy my model without bothering too much about Containerization or Cluster Mgmt.? Each stage of pipeline has different needs • Training – Compute Heavy and Memory High • Serving – Compute Fast and Memory Low How to manage this compute and memory allocation dynamically? MLOps… !!!
What is Kubeflow?
Kubeflow is the solution…. • Kubeflow is an open source artificial intelligence/machine learning (AI/ML) tool that helps improve deployment, portability and management of AI/ML models. • Kubeflow allows users to quickly create, train and tune neural networks within Kubernetes for dynamic resource provisioning. • Kubeflow works well with TensorFlow and other modern AI/ML frameworks such as PyTorch, MXNet and Chainer allowing users to enhance their existing code and setup. 7 Machine Learning Toolkit on Kubernetes
Kubeflow – Origin • Kubeflow was originally released in March 2018 by Google as an open source initiative to develop machine learning applications using TensorFlow on top of Kubernetes to minimise MLOps effort. • Google has been using TFX based Pipeline to deploy ML Models in production over Kubernetes based infra. They offered this with combined power of TensorFlow and Kubernetes. 8
Kubeflow – Building Principals Composability Scalability Portability 9
Composability • Allow to choose what is right for project. E.g. Frameworks, Tools, lib, versions in different stages of pipeline. Composability 10
Portability • Allow to run ML workload to run anywhere/any platform seamlessly. E.g. Laptop, Cloud, On-prem, OS. Portability 11
Portability 12
Scalability • Allow to auto scale on given resources with independent configuration for each. Scalability 13
Scalability 14
What are key MLOps capabilities in Kubeflow?
Jupyter - notebooks • Kubeflow comes with support for managing Jupyter notebooks, an open-source application that allows users to blend code, equation-style notation, free text and dynamic visualisations to give data scientists a single point of access to their experimental setup and notes. 16
Katib - hyper-parameter tuning • Hyperparameters are set before the machine learning process takes place. These parameters (e.g. topology or number of layers in a neural network) can be tuned with Katib. 17
Katib - hyper-parameter tuning • Katib supports various ML tools such as TensorFlow, PyTorch and MXNet making it easy to reuse previous experiments results with Katib and Kubeflow. 18
Katib - hyper-parameter tuning • Hyper Parameter Tuning – Pipeline 19
Katib - hyper-parameter tuning • YMAL for Katib 20
Pipelines • Kubeflow pipelines facilitate end-to-end orchestration of ML workflows, management of multiple experiments and approaches as well as easier re-use of previously successful solutions into a new workflow. This helps developers and data scientists save time and effort. 21
Pipelines • Requires to be bit technical to build it. 22
Serving • Kubeflow makes two service systems available, KFServing and Seldon Core. These allow multi-framework model serving and the choice should be made based on the needs of each project. 23
How does MLOps pipeline operate with Kubeflow?
Kubeflow – Architecture 25
Typical ML Process 26
Kubeflow – Experimental Phase 27
Kubeflow – Production Phase 28
Spotify – Case Study
Spotify – Kubeflow Transition 30Standing on the shoulders of giants
How easy is to do MLOps with KubeFlow?
Demo! 32
Thank you Twitter: @saurabhkaushik Linkedin: @saurabhkaushik

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MLOps with Kubeflow

  • 2. About Me: Saurabh Kaushik @saurabhkaushik • Director, Product Engineering Management @Eureka.AI • Help Telco's to monetize their data using AI and Data Products • Engineered and deployed about 20+ AI Product Solutions • Experience: 20+ years in various roles (Consultant/Lead/Architect/Manager/Director) • Domain: FinTech, AdTech, MarTech • Industry: Telco, Banking, Financial, Retail, CPG • Tech: Data Science, ML, DL, NLP, Big Data, Java, Python, Full Stack • Org: Products, Enterprise, Service, Tech Startups • Speakers: Product School, NASCOM, World Startup Expo, Institute of Product Leadership, IIMB, TechGigs • Hobbies: Tennis, Piano, Building Bots (Botreload.com)
  • 3. Why do we need MLOps?
  • 4. Most Data Scientist View of World… 4 Love to be in this zone!
  • 5. How can someone take care of all these… 5 How do I auto scale my each stage independently? How can I choose different tool for different stage of pipeline? How can I run my workload seamlessly across environments? How can I deploy my model without bothering too much about Containerization or Cluster Mgmt.? Each stage of pipeline has different needs • Training – Compute Heavy and Memory High • Serving – Compute Fast and Memory Low How to manage this compute and memory allocation dynamically? MLOps… !!!
  • 7. Kubeflow is the solution…. • Kubeflow is an open source artificial intelligence/machine learning (AI/ML) tool that helps improve deployment, portability and management of AI/ML models. • Kubeflow allows users to quickly create, train and tune neural networks within Kubernetes for dynamic resource provisioning. • Kubeflow works well with TensorFlow and other modern AI/ML frameworks such as PyTorch, MXNet and Chainer allowing users to enhance their existing code and setup. 7 Machine Learning Toolkit on Kubernetes
  • 8. Kubeflow – Origin • Kubeflow was originally released in March 2018 by Google as an open source initiative to develop machine learning applications using TensorFlow on top of Kubernetes to minimise MLOps effort. • Google has been using TFX based Pipeline to deploy ML Models in production over Kubernetes based infra. They offered this with combined power of TensorFlow and Kubernetes. 8
  • 9. Kubeflow – Building Principals Composability Scalability Portability 9
  • 10. Composability • Allow to choose what is right for project. E.g. Frameworks, Tools, lib, versions in different stages of pipeline. Composability 10
  • 11. Portability • Allow to run ML workload to run anywhere/any platform seamlessly. E.g. Laptop, Cloud, On-prem, OS. Portability 11
  • 13. Scalability • Allow to auto scale on given resources with independent configuration for each. Scalability 13
  • 15. What are key MLOps capabilities in Kubeflow?
  • 16. Jupyter - notebooks • Kubeflow comes with support for managing Jupyter notebooks, an open-source application that allows users to blend code, equation-style notation, free text and dynamic visualisations to give data scientists a single point of access to their experimental setup and notes. 16
  • 17. Katib - hyper-parameter tuning • Hyperparameters are set before the machine learning process takes place. These parameters (e.g. topology or number of layers in a neural network) can be tuned with Katib. 17
  • 18. Katib - hyper-parameter tuning • Katib supports various ML tools such as TensorFlow, PyTorch and MXNet making it easy to reuse previous experiments results with Katib and Kubeflow. 18
  • 19. Katib - hyper-parameter tuning • Hyper Parameter Tuning – Pipeline 19
  • 20. Katib - hyper-parameter tuning • YMAL for Katib 20
  • 21. Pipelines • Kubeflow pipelines facilitate end-to-end orchestration of ML workflows, management of multiple experiments and approaches as well as easier re-use of previously successful solutions into a new workflow. This helps developers and data scientists save time and effort. 21
  • 22. Pipelines • Requires to be bit technical to build it. 22
  • 23. Serving • Kubeflow makes two service systems available, KFServing and Seldon Core. These allow multi-framework model serving and the choice should be made based on the needs of each project. 23
  • 24. How does MLOps pipeline operate with Kubeflow?
  • 30. Spotify – Kubeflow Transition 30Standing on the shoulders of giants
  • 31. How easy is to do MLOps with KubeFlow?