Introduction to MLOps
- What is MLOps
- Machine Learning Life Cycle Overview
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
Setting up an ML Project
- Git and GitHub Setup
- Setting Up Virtual Environments
- Pre-commit Hooks
Data Management Fundamentals
- Understanding Data Lifecycles
- Data Versioning
- Data Governance
- Data Storage Solutions
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.
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
Model Development
- Overview of Model Development Process
- Choosing the Right Algorithm
- Model Training and Validation
- Avoiding Overfitting
- Model Evaluation Metrics
Implementing a Basic ML Pipeline
- Building the Pipeline
- Integrating Preprocessing and Model Development
- Training and Evaluating the Pipeline
- Introduction to Pipeline Automation
Model Development Strategies
- Overview of Model Development Approaches
- Data-Centric vs. Model-Centric Approaches
- Experimentation in Model Development
- Collaborative Development in MLOps
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
Implementing Algorithms
- Selecting an Algorithm
- Implementing the Chosen Algorithm
- Evaluating Algorithm Performance
- Comparing Multiple Algorithms
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
Experiment Tracking and Model Evaluation
- Introduction to Experiment Tracking
- Setting Up Experiment Tracking
- Evaluating Model Performance
- Visualizing Model Performance
Setting Up MLflow for Experiment Tracking
- Introduction to Mlflow
- Tracking Experiments with Mlflow
- Comparing Multiple Runs
- Storing and Retrieving Models
Evaluating Models
- Preparing the Evaluation Environment
- Evaluating Model Performance
- Comparing Models Based on Evaluation
Hyperparameter Tuning Techniques
- Introduction to Hyperparameter Tuning
- Grid Search vs. Random Search
- Bayesian Optimization
- Practical Considerations
Automated Hyperparameter Tuning
- Introduction to Automated Hyperparameter Tuning
- Running Hyperparameter Tuning
- Analyzing the Results
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
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
Containerizing ML Models with Docker
- Introduction to Docker
- Setting Up Docker
- Building a Docker Image
- Deploying Docker Containers on Cloud Platforms
Deploying Models to Cloud Platforms
- Introduction to Cloud Deployment
- Preparing the Model for Deployment
- Setting Up Cloud Infrastructure
- Deploying the Model with Ray Serve
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
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
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
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
Implementing Monitoring Tools
- Introduction to Monitoring Tools
- Instrumenting the ML Model for Monitoring
- Code Implementation - Exposing Metrics for Prometheus
- Visualizing Metrics in Grafana
