Topics:

• Containerization
• Docker
• Docker Architecture
• Docker for Machine Learning
• Docker Desktop Installation
• Working with Docker
• Running Docker Container
• Dockerfile
• Pushing Docker Image to DockerHub
• Dockerize the ML Model
• Container Orchestration
• Kubernetes Core Concepts
• Pod
• Deployment
• Replica
• Service
• Volumes (PVC)
• Monitoring
• Liveness and Readiness Probes
• Labels and Selectors

Hands-On:

• Docker CLI Commands
• Writing a Dockerfile Deployment
• DaemonSets
• Deploying Services

Skills:

• Continuous Deployment
• Writing a Dockerfile to Create an Image
• Installing Docker Compose
• Configuring Local Registry
• Container Orchestration
• Application Deployment

Topics:

• Model Monitoring Importance
• Tools for Model Monitoring
• Understanding ML Model Monitoring
• Challenges in Monitoring ML Models
• Exploring Model Drift Phenomenon
• Operational Level Monitoring Insights
• Introduction to Prometheus Monitoring System
• WhyLabs Setup Process
• Exploring WhyLogs Features: Drift Detection, Input/Output Monitoring, Bias Detection
• WhyLogs Usage: Constraints and Drift Reports

Hands-On:

• ML Model Monitoring
• Monitoring using WhyLogs

Skills:

• Model Monitoring and Debugging

Topics:

• Kubeflow Introduction
• Features
• Kubeflow Fairing
• Kubeflow Pipelines
• Working with Kubeflow

Hands-On:

• Building ML Pipeline with Kubeflow

Skills:

• Building end-to-end ML pipelines with Kubeflow

Topics:

• Getting Started with Amazon Sagemaker
• Amazon SageMaker Notebooks Overview
• Configuration of Notebook Instance
• Creating, Training, and Deploying ML Models with Sagemaker
• Setting up Endpoints and Endpoint Configurations
• Executing Inference from Deployed Models
• Exploring SageMaker Studio & Domain Features
• Introduction to SageMaker Projects
• Understanding Repositories in SageMaker
• Utilizing Pipelines and Graphs in SageMaker
• Conducting Experiments with SageMaker
• Managing Model Groups in SageMaker
• Configuring Endpoints in SageMaker
• Introduction to Azure Machine Learning Studio
• Exploring Azure MLOps
• Understanding Azure ML Components
• Integration of Azure MLOps with DevOps
• Setting up Fully Automated End-to-End CI/CD ML Pipelines

Hands-On:

• Building an end-to-end MLOps pipeline using AWS SageMaker

Skills:

• Using Amazon SageMaker for ML development and deployment
• Management of ML Models and Experiments using Azure Machine Learning

Topics:

• Ensuring Model Integrity
• Counteracting Adversarial Attacks
• Guarding Against Data Poisoning
• Preventing Distributed Denial of Service (DDOS)
• Safeguarding Data Privacy
• Strategies for Mitigating Model Attack Risks
• Implementing A/B Testing Methodologies
• Outlook on the Future of MLOps

Hands-On:

• Implementing A/B Testing Methodologies

Skills:

• Implementation of A/B testing methodologies for model evaluation
• Strategies for Ensuring model Integrity and Security