1- Introduction to MLOps

• • What is MLOps
• • Machine Learning Life Cycle Overview

2- MLOps Components and Tools

• • Brief overview of MlOps Life Cycle / Components of MLOps and Benefits
• • Brief Overview of MLOps tools (MLFlow, KubeFlow, etc) and their role in automating ML Pipelines

4- Setting up an ML Project

• • Git and GitHub Setup
• • Setting Up Virtual Environments
• • Pre-commit Hooks

5- Data Management Fundamentals

• • Understanding Data Lifecycles
• • Data Versioning
• • Data Governance
• • Data Storage Solutions

6- Demo: EDA, Feature Engineering, and Data Cleaning

• • Hands-on EDA using pandas to summarize the dataset.
• • Visualizing distributions using matplotlib (histograms, scatter plots).
• • Creating new features and cleaning data by removing missing values and outliers.

7- Feature Stores

• • Introduction to Feature Stores
• • Types of Feature Stores
• • How Feature Stores Work
• • Best Practices for Using Feature Stores
• • Challenges in Implementing Feature Stores

8- Model Development

• • Overview of Model Development Process
• • Choosing the Right Algorithm
• • Model Training and Validation
• • Avoiding Overfitting
• • Model Evaluation Metrics

9- Implementing a Basic ML Pipeline

• • Building the Pipeline
• • Integrating Preprocessing and Model Development
• • Training and Evaluating the Pipeline
• • Introduction to Pipeline Automation

10- Model Development Strategies

• • Overview of Model Development Approaches
• • Data-Centric vs. Model-Centric Approaches
• • Experimentation in Model Development
• • Collaborative Development in MLOps

11- ML Model Interpretability and Explainability

• • Introduction to Model Interpretability and Explainability
• • Techniques for Model Interpretability
• • Explainability in Different Model Types
• • Tools for Interpretability
• • Challenges in Explainability

12- Implementing Algorithms

• • Selecting an Algorithm
• • Implementing the Chosen Algorithm
• • Evaluating Algorithm Performance
• • Comparing Multiple Algorithms

13- Demo: Selecting, Implementing, and Evaluating Algorithms

• • Select a dataset, choose two different algorithms (e.g., Decision Tree and SVM)
• • Implement the algorithms using scikit-learn
• • Evaluate the performance of each algorithm
• • Compare the results using metrics like accuracy, precision, etc

14- Experiment Tracking and Model Evaluation

• • Introduction to Experiment Tracking
• • Setting Up Experiment Tracking
• • Evaluating Model Performance
• • Visualizing Model Performance

15- Setting Up MLflow for Experiment Tracking

• • Introduction to Mlflow
• • Tracking Experiments with Mlflow
• • Comparing Multiple Runs
• • Storing and Retrieving Models

16- Evaluating Models

• • Preparing the Evaluation Environment
• • Evaluating Model Performance
• • Comparing Models Based on Evaluation"

17- Hyperparameter Tuning Techniques

• • Introduction to Hyperparameter Tuning
• • Grid Search vs. Random Search
• • Bayesian Optimization
• • Practical Considerations"

18- Automated Hyperparameter Tuning

• • Introduction to Automated Hyperparameter Tuning
• • Running Hyperparameter Tuning
• • Analyzing the Results"

19- Model Serving and Deployment Strategies

• • Introduction to Model Serving
• • Deployment Strategies
• • Containerization of ML Models
• • Serving Models with Docker
• • Model Serving Frameworks
• • Deploying Models on Cloud Platforms

20- Legal and Compliance issues in MLOps

• • Introduction to Legal and Compliance in MLOps
• • Key Regulatory Standards
• • Model Governance and Compliance
• • Challenges in Legal and Compliance Issues

21- Containerizing ML Models with Docker

• • Introduction to Docker
• • Setting Up Docker
• • Building a Docker Image
• • Deploying Docker Containers on Cloud Platforms

22- Deploying Models to Cloud Platforms

• • Introduction to Cloud Deployment
• • Preparing the Model for Deployment
• • Setting Up Cloud Infrastructure
• • Deploying the Model with Ray Serve

23- Federated Training and Edge Deployments

• • Introduction to Federated Learning and Edge Computing
• • Federated Training Architecture
• • Edge Model Deployment
• • Tools and Frameworks
• • Challenges in Federated Learning and Edge Computing

24- CI/CD for ML

• • Introduction to CI/CD for Machine Learning
• • Setting Up CI/CD Pipelines for ML
• • Integrating CI/CD with Experiment Tracking
• • Automating Model Validation and Testing

25- Setting up CI/CD Pipelines for ML

• • Introduction to GitHub Actions for CI/CD
• • Automating Model Training and Deployment
• • Integrating MLflow with CI/CD
• • Testing the CI/CD Pipeline

26- Monitoring and Maintaining ML Systems

• • Introduction to Monitoring ML Systems
• • Tools for Monitoring ML Models
• • Setting Up Alerts for Model Drift
• • Monitoring Model Performance in Real-Time
• • Continuous Feedback Loops
• • Scaling Monitoring for Large-Scale Deployments

27- Implementing Monitoring Tools

• • Introduction to Monitoring Tools
• • Instrumenting the ML Model for Monitoring
• • Code Implementation - Exposing Metrics for Prometheus
• • Visualizing Metrics in Grafana