SlideShare une entreprise Scribd logo

MLOps with Kubeflow

•
•
Saurabh Kaushik
Saurabh Kaushik

MLOps with Kubeflow

Technologie
1  sur  33
Télécharger pour lire hors ligne
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

Recommandé

End to end Machine Learning using Kubeflow - Build, Train, Deploy and Manage
End to end Machine Learning using Kubeflow - Build, Train, Deploy and ManageEnd to end Machine Learning using Kubeflow - Build, Train, Deploy and Manage
End to end Machine Learning using Kubeflow - Build, Train, Deploy and ManageAnimesh Singh
 
Machine Learning using Kubeflow and Kubernetes
Machine Learning using Kubeflow and KubernetesMachine Learning using Kubeflow and Kubernetes
Machine Learning using Kubeflow and KubernetesArun Gupta
 
Kubeflow
KubeflowKubeflow
KubeflowKarane Vieira
 
MLOps in action
MLOps in actionMLOps in action
MLOps in actionPieter de Bruin
 
MLOps - The Assembly Line of ML
MLOps - The Assembly Line of MLMLOps - The Assembly Line of ML
MLOps - The Assembly Line of MLJordan Birdsell
 
Introducing Kubeflow (w. Special Guests Tensorflow and Apache Spark)
Introducing Kubeflow (w. Special Guests Tensorflow and Apache Spark)Introducing Kubeflow (w. Special Guests Tensorflow and Apache Spark)
Introducing Kubeflow (w. Special Guests Tensorflow and Apache Spark)DataWorks Summit
 
MLops workshop AWS
MLops workshop AWSMLops workshop AWS
MLops workshop AWSGili Nachum
 
Apply MLOps at Scale by H&M
Apply MLOps at Scale by H&MApply MLOps at Scale by H&M
Apply MLOps at Scale by H&MDatabricks
 

Recommandé

End to end Machine Learning using Kubeflow - Build, Train, Deploy and Manage
End to end Machine Learning using Kubeflow - Build, Train, Deploy and ManageEnd to end Machine Learning using Kubeflow - Build, Train, Deploy and Manage
End to end Machine Learning using Kubeflow - Build, Train, Deploy and ManageAnimesh Singh
 
Machine Learning using Kubeflow and Kubernetes
Machine Learning using Kubeflow and KubernetesMachine Learning using Kubeflow and Kubernetes
Machine Learning using Kubeflow and KubernetesArun Gupta
 
Kubeflow
KubeflowKubeflow
KubeflowKarane Vieira
 
MLOps in action
MLOps in actionMLOps in action
MLOps in actionPieter de Bruin
 
MLOps - The Assembly Line of ML
MLOps - The Assembly Line of MLMLOps - The Assembly Line of ML
MLOps - The Assembly Line of MLJordan Birdsell
 
Introducing Kubeflow (w. Special Guests Tensorflow and Apache Spark)
Introducing Kubeflow (w. Special Guests Tensorflow and Apache Spark)Introducing Kubeflow (w. Special Guests Tensorflow and Apache Spark)
Introducing Kubeflow (w. Special Guests Tensorflow and Apache Spark)DataWorks Summit
 
MLops workshop AWS
MLops workshop AWSMLops workshop AWS
MLops workshop AWSGili Nachum
 
Apply MLOps at Scale by H&M
Apply MLOps at Scale by H&MApply MLOps at Scale by H&M
Apply MLOps at Scale by H&MDatabricks
 
What is MLOps
What is MLOpsWhat is MLOps
What is MLOpsHenrik Skogström
 
Vertex AI: Pipelines for your MLOps workflows
Vertex AI: Pipelines for your MLOps workflowsVertex AI: Pipelines for your MLOps workflows
Vertex AI: Pipelines for your MLOps workflowsMárton Kodok
 
Databricks Overview for MLOps
Databricks Overview for MLOpsDatabricks Overview for MLOps
Databricks Overview for MLOpsDatabricks
 
MLOps for production-level machine learning
MLOps for production-level machine learningMLOps for production-level machine learning
MLOps for production-level machine learningcnvrg.io AI OS - Hands-on ML Workshops
 
From Data Science to MLOps
From Data Science to MLOpsFrom Data Science to MLOps
From Data Science to MLOpsCarl W. Handlin
 
MLOps Bridging the gap between Data Scientists and Ops.
MLOps Bridging the gap between Data Scientists and Ops.MLOps Bridging the gap between Data Scientists and Ops.
MLOps Bridging the gap between Data Scientists and Ops.Knoldus Inc.
 
MLOps Using MLflow
MLOps Using MLflowMLOps Using MLflow
MLOps Using MLflowDatabricks
 
MLOps and Data Quality: Deploying Reliable ML Models in Production
MLOps and Data Quality: Deploying Reliable ML Models in ProductionMLOps and Data Quality: Deploying Reliable ML Models in Production
MLOps and Data Quality: Deploying Reliable ML Models in ProductionProvectus
 
MLOps with serverless architectures (October 2018)
MLOps with serverless architectures (October 2018)MLOps with serverless architectures (October 2018)
MLOps with serverless architectures (October 2018)Julien SIMON
 
“Houston, we have a model...” Introduction to MLOps
“Houston, we have a model...” Introduction to MLOps“Houston, we have a model...” Introduction to MLOps
“Houston, we have a model...” Introduction to MLOpsRui Quintino
 
Using MLOps to Bring ML to Production/The Promise of MLOps
Using MLOps to Bring ML to Production/The Promise of MLOpsUsing MLOps to Bring ML to Production/The Promise of MLOps
Using MLOps to Bring ML to Production/The Promise of MLOpsWeaveworks
 
MLOps journey at Swisscom: AI Use Cases, Architecture and Future Vision
MLOps journey at Swisscom: AI Use Cases, Architecture and Future VisionMLOps journey at Swisscom: AI Use Cases, Architecture and Future Vision
MLOps journey at Swisscom: AI Use Cases, Architecture and Future VisionBATbern
 
Seamless MLOps with Seldon and MLflow
Seamless MLOps with Seldon and MLflowSeamless MLOps with Seldon and MLflow
Seamless MLOps with Seldon and MLflowDatabricks
 
Ml ops intro session
Ml ops intro sessionMl ops intro session
Ml ops intro sessionAvinash Patil
 
Unified MLOps: Feature Stores & Model Deployment
Unified MLOps: Feature Stores & Model DeploymentUnified MLOps: Feature Stores & Model Deployment
Unified MLOps: Feature Stores & Model DeploymentDatabricks
 
MLOps Virtual Event: Automating ML at Scale
MLOps Virtual Event: Automating ML at ScaleMLOps Virtual Event: Automating ML at Scale
MLOps Virtual Event: Automating ML at ScaleDatabricks
 
MLOps with Azure DevOps
MLOps with Azure DevOpsMLOps with Azure DevOps
MLOps with Azure DevOpsMarco Parenzan
 
Ml ops past_present_future
Ml ops past_present_futureMl ops past_present_future
Ml ops past_present_futureNisha Talagala
 
Pythonsevilla2019 - Introduction to MLFlow
Pythonsevilla2019 - Introduction to MLFlowPythonsevilla2019 - Introduction to MLFlow
Pythonsevilla2019 - Introduction to MLFlowFernando Ortega Gallego
 
Kubeflow Pipelines (with Tekton)
Kubeflow Pipelines (with Tekton)Kubeflow Pipelines (with Tekton)
Kubeflow Pipelines (with Tekton)Animesh Singh
 
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...Akash Tandon
 
Containerized architectures for deep learning
Containerized architectures for deep learningContainerized architectures for deep learning
Containerized architectures for deep learningAntje Barth
 

Contenu connexe

Tendances

Vertex AI: Pipelines for your MLOps workflows
Vertex AI: Pipelines for your MLOps workflowsVertex AI: Pipelines for your MLOps workflows
Vertex AI: Pipelines for your MLOps workflowsMárton Kodok
 
Databricks Overview for MLOps
Databricks Overview for MLOpsDatabricks Overview for MLOps
Databricks Overview for MLOpsDatabricks
 
From Data Science to MLOps
From Data Science to MLOpsFrom Data Science to MLOps
From Data Science to MLOpsCarl W. Handlin
 
MLOps Bridging the gap between Data Scientists and Ops.
MLOps Bridging the gap between Data Scientists and Ops.MLOps Bridging the gap between Data Scientists and Ops.
MLOps Bridging the gap between Data Scientists and Ops.Knoldus Inc.
 
MLOps Using MLflow
MLOps Using MLflowMLOps Using MLflow
MLOps Using MLflowDatabricks
 
MLOps and Data Quality: Deploying Reliable ML Models in Production
MLOps and Data Quality: Deploying Reliable ML Models in ProductionMLOps and Data Quality: Deploying Reliable ML Models in Production
MLOps and Data Quality: Deploying Reliable ML Models in ProductionProvectus
 
MLOps with serverless architectures (October 2018)
MLOps with serverless architectures (October 2018)MLOps with serverless architectures (October 2018)
MLOps with serverless architectures (October 2018)Julien SIMON
 
“Houston, we have a model...” Introduction to MLOps
“Houston, we have a model...” Introduction to MLOps“Houston, we have a model...” Introduction to MLOps
“Houston, we have a model...” Introduction to MLOpsRui Quintino
 
Using MLOps to Bring ML to Production/The Promise of MLOps
Using MLOps to Bring ML to Production/The Promise of MLOpsUsing MLOps to Bring ML to Production/The Promise of MLOps
Using MLOps to Bring ML to Production/The Promise of MLOpsWeaveworks
 
MLOps journey at Swisscom: AI Use Cases, Architecture and Future Vision
MLOps journey at Swisscom: AI Use Cases, Architecture and Future VisionMLOps journey at Swisscom: AI Use Cases, Architecture and Future Vision
MLOps journey at Swisscom: AI Use Cases, Architecture and Future VisionBATbern
 
Seamless MLOps with Seldon and MLflow
Seamless MLOps with Seldon and MLflowSeamless MLOps with Seldon and MLflow
Seamless MLOps with Seldon and MLflowDatabricks
 
Ml ops intro session
Ml ops intro sessionMl ops intro session
Ml ops intro sessionAvinash Patil
 
Unified MLOps: Feature Stores & Model Deployment
Unified MLOps: Feature Stores & Model DeploymentUnified MLOps: Feature Stores & Model Deployment
Unified MLOps: Feature Stores & Model DeploymentDatabricks
 
MLOps Virtual Event: Automating ML at Scale
MLOps Virtual Event: Automating ML at ScaleMLOps Virtual Event: Automating ML at Scale
MLOps Virtual Event: Automating ML at ScaleDatabricks
 
MLOps with Azure DevOps
MLOps with Azure DevOpsMLOps with Azure DevOps
MLOps with Azure DevOpsMarco Parenzan
 
Ml ops past_present_future
Ml ops past_present_futureMl ops past_present_future
Ml ops past_present_futureNisha Talagala
 
Pythonsevilla2019 - Introduction to MLFlow
Pythonsevilla2019 - Introduction to MLFlowPythonsevilla2019 - Introduction to MLFlow
Pythonsevilla2019 - Introduction to MLFlowFernando Ortega Gallego
 
Kubeflow Pipelines (with Tekton)
Kubeflow Pipelines (with Tekton)Kubeflow Pipelines (with Tekton)
Kubeflow Pipelines (with Tekton)Animesh Singh
 

Tendances (20)

What is MLOps
What is MLOpsWhat is MLOps
What is MLOps
 
Vertex AI: Pipelines for your MLOps workflows
Vertex AI: Pipelines for your MLOps workflowsVertex AI: Pipelines for your MLOps workflows
Vertex AI: Pipelines for your MLOps workflows
 
Databricks Overview for MLOps
Databricks Overview for MLOpsDatabricks Overview for MLOps
Databricks Overview for MLOps
 
MLOps for production-level machine learning
MLOps for production-level machine learningMLOps for production-level machine learning
MLOps for production-level machine learning
 
From Data Science to MLOps
From Data Science to MLOpsFrom Data Science to MLOps
From Data Science to MLOps
 
MLOps Bridging the gap between Data Scientists and Ops.
MLOps Bridging the gap between Data Scientists and Ops.MLOps Bridging the gap between Data Scientists and Ops.
MLOps Bridging the gap between Data Scientists and Ops.
 
MLOps Using MLflow
MLOps Using MLflowMLOps Using MLflow
MLOps Using MLflow
 
MLOps and Data Quality: Deploying Reliable ML Models in Production
MLOps and Data Quality: Deploying Reliable ML Models in ProductionMLOps and Data Quality: Deploying Reliable ML Models in Production
MLOps and Data Quality: Deploying Reliable ML Models in Production
 
MLOps with serverless architectures (October 2018)
MLOps with serverless architectures (October 2018)MLOps with serverless architectures (October 2018)
MLOps with serverless architectures (October 2018)
 
“Houston, we have a model...” Introduction to MLOps
“Houston, we have a model...” Introduction to MLOps“Houston, we have a model...” Introduction to MLOps
“Houston, we have a model...” Introduction to MLOps
 
Using MLOps to Bring ML to Production/The Promise of MLOps
Using MLOps to Bring ML to Production/The Promise of MLOpsUsing MLOps to Bring ML to Production/The Promise of MLOps
Using MLOps to Bring ML to Production/The Promise of MLOps
 
MLOps journey at Swisscom: AI Use Cases, Architecture and Future Vision
MLOps journey at Swisscom: AI Use Cases, Architecture and Future VisionMLOps journey at Swisscom: AI Use Cases, Architecture and Future Vision
MLOps journey at Swisscom: AI Use Cases, Architecture and Future Vision
 
Seamless MLOps with Seldon and MLflow
Seamless MLOps with Seldon and MLflowSeamless MLOps with Seldon and MLflow
Seamless MLOps with Seldon and MLflow
 
Ml ops intro session
Ml ops intro sessionMl ops intro session
Ml ops intro session
 
Unified MLOps: Feature Stores & Model Deployment
Unified MLOps: Feature Stores & Model DeploymentUnified MLOps: Feature Stores & Model Deployment
Unified MLOps: Feature Stores & Model Deployment
 
MLOps Virtual Event: Automating ML at Scale
MLOps Virtual Event: Automating ML at ScaleMLOps Virtual Event: Automating ML at Scale
MLOps Virtual Event: Automating ML at Scale
 
MLOps with Azure DevOps
MLOps with Azure DevOpsMLOps with Azure DevOps
MLOps with Azure DevOps
 
Ml ops past_present_future
Ml ops past_present_futureMl ops past_present_future
Ml ops past_present_future
 
Pythonsevilla2019 - Introduction to MLFlow
Pythonsevilla2019 - Introduction to MLFlowPythonsevilla2019 - Introduction to MLFlow
Pythonsevilla2019 - Introduction to MLFlow
 
Kubeflow Pipelines (with Tekton)
Kubeflow Pipelines (with Tekton)Kubeflow Pipelines (with Tekton)
Kubeflow Pipelines (with Tekton)
 

Similaire à MLOps with Kubeflow

Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...Akash Tandon
 
Containerized architectures for deep learning
Containerized architectures for deep learningContainerized architectures for deep learning
Containerized architectures for deep learningAntje Barth
 
Going deep (learning) with tensor flow and quarkus
Going deep (learning) with tensor flow and quarkusGoing deep (learning) with tensor flow and quarkus
Going deep (learning) with tensor flow and quarkusRed Hat Developers
 
Bodywork - GitOps for Machine Learning
Bodywork - GitOps for Machine LearningBodywork - GitOps for Machine Learning
Bodywork - GitOps for Machine LearningAlex Ioannides
 
Scaling AI/ML with Containers and Kubernetes
Scaling AI/ML with Containers and Kubernetes Scaling AI/ML with Containers and Kubernetes
Scaling AI/ML with Containers and Kubernetes Tushar Katarki
 
Democratizing machine learning on kubernetes
Democratizing machine learning on kubernetesDemocratizing machine learning on kubernetes
Democratizing machine learning on kubernetesDocker, Inc.
 
ODSC East 2020 Accelerate ML Lifecycle with Kubernetes and Containerized Da...
ODSC East 2020 Accelerate ML Lifecycle with Kubernetes and Containerized Da...ODSC East 2020 Accelerate ML Lifecycle with Kubernetes and Containerized Da...
ODSC East 2020 Accelerate ML Lifecycle with Kubernetes and Containerized Da...Abhinav Joshi
 
Deploying ML models in the enterprise
Deploying ML models in the enterpriseDeploying ML models in the enterprise
Deploying ML models in the enterprisedoppenhe
 
The Download: Tech Talks by the HPCC Systems Community, Episode 11
The Download: Tech Talks by the HPCC Systems Community, Episode 11The Download: Tech Talks by the HPCC Systems Community, Episode 11
The Download: Tech Talks by the HPCC Systems Community, Episode 11HPCC Systems
 
AI & Machine Learning Pipelines with Knative
AI & Machine Learning Pipelines with KnativeAI & Machine Learning Pipelines with Knative
AI & Machine Learning Pipelines with KnativeAnimesh Singh
 
[DSC Europe 23] Milos Grubjesic Empowering Business with Pepsico s Advanced M...
[DSC Europe 23] Milos Grubjesic Empowering Business with Pepsico s Advanced M...[DSC Europe 23] Milos Grubjesic Empowering Business with Pepsico s Advanced M...
[DSC Europe 23] Milos Grubjesic Empowering Business with Pepsico s Advanced M...DataScienceConferenc1
 
Machine learning from software developers point of view
Machine learning from software developers point of viewMachine learning from software developers point of view
Machine learning from software developers point of viewPierre Paci
 
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...Building a MLOps Platform Around MLflow to Enable Model Productionalization i...
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...Databricks
 
Building a Scalable and reliable open source ML Platform with MLFlow
Building a Scalable and reliable open source ML Platform with MLFlowBuilding a Scalable and reliable open source ML Platform with MLFlow
Building a Scalable and reliable open source ML Platform with MLFlowGoDataDriven
 
Openstack - Enterprise cloud management platform
Openstack - Enterprise cloud management platformOpenstack - Enterprise cloud management platform
Openstack - Enterprise cloud management platformNagaraj Shenoy
 
Intro to the CNCF Research User Group
Intro to the CNCF Research User GroupIntro to the CNCF Research User Group
Intro to the CNCF Research User GroupBob Killen
 
Keynote at Converge 2019
Keynote at Converge 2019Keynote at Converge 2019
Keynote at Converge 2019Travis Oliphant
 
Apache Mesos Overview and Integration
Apache Mesos Overview and IntegrationApache Mesos Overview and Integration
Apache Mesos Overview and IntegrationAlex Baretto
 

Similaire à MLOps with Kubeflow (20)

Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...
 
Containerized architectures for deep learning
Containerized architectures for deep learningContainerized architectures for deep learning
Containerized architectures for deep learning
 
Going deep (learning) with tensor flow and quarkus
Going deep (learning) with tensor flow and quarkusGoing deep (learning) with tensor flow and quarkus
Going deep (learning) with tensor flow and quarkus
 
Bodywork - GitOps for Machine Learning
Bodywork - GitOps for Machine LearningBodywork - GitOps for Machine Learning
Bodywork - GitOps for Machine Learning
 
Kubeflow.pptx
Kubeflow.pptxKubeflow.pptx
Kubeflow.pptx
 
Scaling AI/ML with Containers and Kubernetes
Scaling AI/ML with Containers and Kubernetes Scaling AI/ML with Containers and Kubernetes
Scaling AI/ML with Containers and Kubernetes
 
Democratizing machine learning on kubernetes
Democratizing machine learning on kubernetesDemocratizing machine learning on kubernetes
Democratizing machine learning on kubernetes
 
ODSC East 2020 Accelerate ML Lifecycle with Kubernetes and Containerized Da...
ODSC East 2020 Accelerate ML Lifecycle with Kubernetes and Containerized Da...ODSC East 2020 Accelerate ML Lifecycle with Kubernetes and Containerized Da...
ODSC East 2020 Accelerate ML Lifecycle with Kubernetes and Containerized Da...
 
Deploying ML models in the enterprise
Deploying ML models in the enterpriseDeploying ML models in the enterprise
Deploying ML models in the enterprise
 
The Download: Tech Talks by the HPCC Systems Community, Episode 11
The Download: Tech Talks by the HPCC Systems Community, Episode 11The Download: Tech Talks by the HPCC Systems Community, Episode 11
The Download: Tech Talks by the HPCC Systems Community, Episode 11
 
AI & Machine Learning Pipelines with Knative
AI & Machine Learning Pipelines with KnativeAI & Machine Learning Pipelines with Knative
AI & Machine Learning Pipelines with Knative
 
[DSC Europe 23] Milos Grubjesic Empowering Business with Pepsico s Advanced M...
[DSC Europe 23] Milos Grubjesic Empowering Business with Pepsico s Advanced M...[DSC Europe 23] Milos Grubjesic Empowering Business with Pepsico s Advanced M...
[DSC Europe 23] Milos Grubjesic Empowering Business with Pepsico s Advanced M...
 
NextGenML
NextGenML NextGenML
NextGenML
 
Machine learning from software developers point of view
Machine learning from software developers point of viewMachine learning from software developers point of view
Machine learning from software developers point of view
 
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...Building a MLOps Platform Around MLflow to Enable Model Productionalization i...
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...
 
Building a Scalable and reliable open source ML Platform with MLFlow
Building a Scalable and reliable open source ML Platform with MLFlowBuilding a Scalable and reliable open source ML Platform with MLFlow
Building a Scalable and reliable open source ML Platform with MLFlow
 
Openstack - Enterprise cloud management platform
Openstack - Enterprise cloud management platformOpenstack - Enterprise cloud management platform
Openstack - Enterprise cloud management platform
 
Intro to the CNCF Research User Group
Intro to the CNCF Research User GroupIntro to the CNCF Research User Group
Intro to the CNCF Research User Group
 
Keynote at Converge 2019
Keynote at Converge 2019Keynote at Converge 2019
Keynote at Converge 2019
 
Apache Mesos Overview and Integration
Apache Mesos Overview and IntegrationApache Mesos Overview and Integration
Apache Mesos Overview and Integration
 

Plus de Saurabh Kaushik

Building AI Product using AI Product Thinking
Building AI Product using AI Product Thinking Building AI Product using AI Product Thinking
Building AI Product using AI Product Thinking Saurabh Kaushik
 
AI Product Thinking for Product Managers
AI Product Thinking for Product Managers AI Product Thinking for Product Managers
AI Product Thinking for Product Managers Saurabh Kaushik
 
Explainable AI (XAI) - A Perspective
Explainable AI (XAI) - A Perspective Explainable AI (XAI) - A Perspective
Explainable AI (XAI) - A Perspective Saurabh Kaushik
 
Engineering Intelligent NLP Applications Using Deep Learning – Part 2
Engineering Intelligent NLP Applications Using Deep Learning – Part 2 Engineering Intelligent NLP Applications Using Deep Learning – Part 2
Engineering Intelligent NLP Applications Using Deep Learning – Part 2 Saurabh Kaushik
 
Engineering Intelligent NLP Applications Using Deep Learning – Part 1
Engineering Intelligent NLP Applications Using Deep Learning – Part 1Engineering Intelligent NLP Applications Using Deep Learning – Part 1
Engineering Intelligent NLP Applications Using Deep Learning – Part 1Saurabh Kaushik
 
Engineering Intelligent Systems using Machine Learning
Engineering Intelligent Systems using Machine Learning Engineering Intelligent Systems using Machine Learning
Engineering Intelligent Systems using Machine Learning Saurabh Kaushik
 
Winning Deals with Design Thinking
Winning Deals with Design Thinking Winning Deals with Design Thinking
Winning Deals with Design Thinking Saurabh Kaushik
 
Project Management Using Design Thinking
Project Management Using Design Thinking Project Management Using Design Thinking
Project Management Using Design Thinking Saurabh Kaushik
 
Design Thinking - Case Studies
Design Thinking - Case Studies Design Thinking - Case Studies
Design Thinking - Case Studies Saurabh Kaushik
 
An Assessment Framework for Strategic Digital Marketing Effectiveness
An Assessment Framework for Strategic Digital Marketing EffectivenessAn Assessment Framework for Strategic Digital Marketing Effectiveness
An Assessment Framework for Strategic Digital Marketing EffectivenessSaurabh Kaushik
 
A Consulting Model - Strategic Digital marketing
A Consulting Model - Strategic Digital marketingA Consulting Model - Strategic Digital marketing
A Consulting Model - Strategic Digital marketingSaurabh Kaushik
 
Air Pollution Control by Tax and Subsidies
Air Pollution Control by Tax and Subsidies Air Pollution Control by Tax and Subsidies
Air Pollution Control by Tax and Subsidies Saurabh Kaushik
 

Plus de Saurabh Kaushik (12)

Building AI Product using AI Product Thinking
Building AI Product using AI Product Thinking Building AI Product using AI Product Thinking
Building AI Product using AI Product Thinking
 
AI Product Thinking for Product Managers
AI Product Thinking for Product Managers AI Product Thinking for Product Managers
AI Product Thinking for Product Managers
 
Explainable AI (XAI) - A Perspective
Explainable AI (XAI) - A Perspective Explainable AI (XAI) - A Perspective
Explainable AI (XAI) - A Perspective
 
Engineering Intelligent NLP Applications Using Deep Learning – Part 2
Engineering Intelligent NLP Applications Using Deep Learning – Part 2 Engineering Intelligent NLP Applications Using Deep Learning – Part 2
Engineering Intelligent NLP Applications Using Deep Learning – Part 2
 
Engineering Intelligent NLP Applications Using Deep Learning – Part 1
Engineering Intelligent NLP Applications Using Deep Learning – Part 1Engineering Intelligent NLP Applications Using Deep Learning – Part 1
Engineering Intelligent NLP Applications Using Deep Learning – Part 1
 
Engineering Intelligent Systems using Machine Learning
Engineering Intelligent Systems using Machine Learning Engineering Intelligent Systems using Machine Learning
Engineering Intelligent Systems using Machine Learning
 
Winning Deals with Design Thinking
Winning Deals with Design Thinking Winning Deals with Design Thinking
Winning Deals with Design Thinking
 
Project Management Using Design Thinking
Project Management Using Design Thinking Project Management Using Design Thinking
Project Management Using Design Thinking
 
Design Thinking - Case Studies
Design Thinking - Case Studies Design Thinking - Case Studies
Design Thinking - Case Studies
 
An Assessment Framework for Strategic Digital Marketing Effectiveness
An Assessment Framework for Strategic Digital Marketing EffectivenessAn Assessment Framework for Strategic Digital Marketing Effectiveness
An Assessment Framework for Strategic Digital Marketing Effectiveness
 
A Consulting Model - Strategic Digital marketing
A Consulting Model - Strategic Digital marketingA Consulting Model - Strategic Digital marketing
A Consulting Model - Strategic Digital marketing
 
Air Pollution Control by Tax and Subsidies
Air Pollution Control by Tax and Subsidies Air Pollution Control by Tax and Subsidies
Air Pollution Control by Tax and Subsidies
 

Dernier

FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024The Digital Insurer
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistandanishmna97
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Angeliki Cooney
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherRemote DBA Services
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...Zilliz
 
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Jeffrey Haguewood
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoffsammart93
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsNanddeep Nachan
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontologyjohnbeverley2021
 
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Orbitshub
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Zilliz
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businesspanagenda
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...apidays
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfOrbitshub
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMECloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxRemote DBA Services
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxRustici Software
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodJuan lago vázquez
 

Dernier (20)

FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
 
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontology
 
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMECloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptx
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 

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?