Training Course on Scalable ML Serving with Kubeflow/Sagemaker

Course Title: Training Course on Scalable ML Serving with Kubeflow/Sagemaker

Executive Summary

This intensive two-week course provides participants with the knowledge and skills to deploy and scale machine learning models using Kubeflow and Sagemaker. It covers the entire ML serving lifecycle, from model packaging and deployment to monitoring and scaling. Participants will learn to leverage Kubeflow’s components for building portable and reproducible ML pipelines and Sagemaker’s managed services for efficient model hosting. Through hands-on labs and real-world case studies, they’ll gain practical experience in designing and implementing scalable ML serving architectures. The course emphasizes best practices for optimizing model performance, ensuring reliability, and managing costs. Graduates will be equipped to build and maintain robust, scalable ML systems that drive business value.

Introduction

In today’s data-driven world, organizations are increasingly relying on machine learning (ML) to gain insights, automate processes, and improve decision-making. However, deploying and scaling ML models in production can be a complex and challenging task. This course addresses these challenges by providing a comprehensive overview of scalable ML serving using Kubeflow and Sagemaker. Participants will learn how to leverage these powerful platforms to build and deploy ML models at scale, ensuring high availability, performance, and cost-effectiveness. The course covers key concepts such as model packaging, deployment strategies, monitoring, and scaling. It also emphasizes best practices for optimizing model performance, managing infrastructure, and ensuring data privacy and security. By the end of this course, participants will be able to design, implement, and maintain scalable ML serving architectures that meet the needs of their organizations.

Course Outcomes

• Understand the key concepts and challenges of ML serving.
• Package and deploy ML models using Kubeflow and Sagemaker.
• Build scalable and reliable ML serving architectures.
• Monitor model performance and identify areas for improvement.
• Optimize model serving costs.
• Automate the ML serving lifecycle.
• Implement best practices for data privacy and security.

Training Methodologies

• Interactive lectures and discussions.
• Hands-on labs and coding exercises.
• Real-world case studies.
• Group projects and presentations.
• Guest lectures from industry experts.
• Online resources and documentation.
• Q&A sessions and office hours.

Benefits to Participants

• Gain in-demand skills in ML serving with Kubeflow and Sagemaker.
• Learn best practices for building scalable and reliable ML systems.
• Improve your ability to deploy ML models in production.
• Enhance your understanding of ML infrastructure and operations.
• Expand your professional network.
• Increase your career opportunities.
• Earn a certificate of completion.

Benefits to Sending Organization

• Accelerate the deployment of ML models into production.
• Improve the scalability and reliability of ML systems.
• Reduce the cost of ML infrastructure and operations.
• Increase the efficiency of ML teams.
• Gain a competitive advantage through faster innovation.
• Attract and retain top ML talent.
• Drive business value through data-driven insights.

Target Participants

• Data Scientists
• Machine Learning Engineers
• DevOps Engineers
• Software Engineers
• MLOps Engineers
• Cloud Architects
• Technical Leads

Week 1: Foundations of ML Serving and Kubeflow

Module 1: Introduction to ML Serving

1. Overview of ML serving and its importance.
2. Challenges of deploying ML models in production.
3. Different ML serving frameworks and platforms.
4. Key concepts: model packaging, deployment, monitoring, and scaling.
5. MLOps principles and practices.
6. Introduction to Kubeflow and Sagemaker.
7. Setting up the development environment.

Module 2: Kubeflow Fundamentals

1. Kubernetes overview and its role in ML serving.
2. Kubeflow architecture and components.
3. Installing and configuring Kubeflow.
4. Kubeflow Pipelines for building ML workflows.
5. Using Kubeflow’s model serving capabilities.
6. Deploying models with KFServing.
7. Hands-on lab: Deploying a simple model with KFServing.

Module 3: Model Packaging and Deployment with Kubeflow

1. Model serialization formats: TensorFlow SavedModel, ONNX, etc.
2. Building Docker containers for ML models.
3. Creating Kubeflow deployment manifests.
4. Using Kubeflow’s CLI and API.
5. Implementing canary deployments and A/B testing.
6. Managing model versions and rollbacks.
7. Hands-on lab: Packaging and deploying a TensorFlow model with Kubeflow.

Module 4: Monitoring and Logging with Kubeflow

1. Monitoring model performance metrics.
2. Collecting and analyzing logs.
3. Using Kubeflow’s built-in monitoring tools.
4. Integrating with external monitoring systems (e.g., Prometheus, Grafana).
5. Setting up alerts and notifications.
6. Troubleshooting deployment issues.
7. Hands-on lab: Monitoring model performance with Kubeflow and Prometheus.

Module 5: Scaling ML Serving with Kubeflow

1. Horizontal pod autoscaling (HPA).
2. Vertical pod autoscaling (VPA).
3. Resource management and optimization.
4. Load balancing and traffic management.
5. Using Kubeflow’s Knative integration for serverless serving.
6. Scaling strategies for different ML workloads.
7. Hands-on lab: Scaling a Kubeflow deployment with HPA.

Week 2: Sagemaker for Scalable ML Serving

Module 6: Introduction to Sagemaker

1. Overview of Amazon Sagemaker and its services.
2. Sagemaker architecture and components.
3. Setting up a Sagemaker environment.
4. Using Sagemaker’s built-in algorithms and frameworks.
5. Deploying models with Sagemaker hosting services.
6. Managing Sagemaker resources.
7. Hands-on lab: Creating a Sagemaker notebook instance.

Module 7: Model Deployment with Sagemaker

1. Preparing models for deployment on Sagemaker.
2. Using Sagemaker’s model deployment APIs.
3. Deploying models with Sagemaker Endpoints.
4. Configuring endpoint settings and scaling options.
5. Implementing canary deployments and A/B testing.
6. Managing model versions and rollbacks in Sagemaker.
7. Hands-on lab: Deploying a Scikit-learn model with Sagemaker.

Module 8: Sagemaker Inference Pipelines

1. Chaining multiple models together with Sagemaker inference pipelines.
2. Building pre-processing and post-processing steps.
3. Using Sagemaker’s built-in data transformation tools.
4. Implementing custom inference logic.
5. Optimizing inference performance.
6. Managing complex ML workflows.
7. Hands-on lab: Building an inference pipeline with Sagemaker.

Module 9: Monitoring and Logging with Sagemaker

1. Monitoring model performance metrics in Sagemaker.
2. Collecting and analyzing logs with CloudWatch.
3. Using Sagemaker’s model monitoring features.
4. Setting up alerts and notifications.
5. Detecting model drift and data quality issues.
6. Troubleshooting deployment problems.
7. Hands-on lab: Monitoring model performance with Sagemaker and CloudWatch.

Module 10: Advanced Sagemaker Features and Best Practices

1. Sagemaker Autopilot for automated model building.
2. Sagemaker Debugger for identifying training issues.
3. Sagemaker Model Monitor for detecting model drift.
4. Sagemaker Neo for optimizing model performance.
5. Security best practices for Sagemaker.
6. Cost optimization strategies for Sagemaker.
7. Case studies of real-world ML serving deployments with Sagemaker.

Action Plan for Implementation

1. Assess current ML serving infrastructure and identify areas for improvement.
2. Develop a roadmap for adopting Kubeflow or Sagemaker.
3. Prioritize use cases based on business impact and technical feasibility.
4. Train ML teams on Kubeflow or Sagemaker.
5. Implement monitoring and alerting systems.
6. Establish a continuous integration and continuous delivery (CI/CD) pipeline for ML models.
7. Regularly review and optimize ML serving infrastructure.