MLOps Fundamentals Training in Germany

Who Should Attend?

Proficiency in Python; strong beginner/intermediate grasp of ML, familiarity with Git and version control, experience with cloud platforms

• Explain the core principles and challenges of MLOps in the context of the ML project lifecycle
• Design and structure ML projects using best practices and industry-standard tools
• Analyze and prepare datasets for machine learning applications using advanced data management techniques
• Develop and evaluate machine learning models using appropriate metrics and experiment tracking tools
• Apply version control and containerization techniques to ensure reproducibility in ML projects
• Implement comprehensive testing strategies for ML components and pipelines
• Deploy ML models using RESTful APIs and containerization technologies
• Construct automated CI/CD pipelines for ML projects
• Design and implement monitoring systems for deployed ML models
• Evaluate and address model drift and performance degradation in production environments
• Integrate data engineering practices into MLOps workflows
• Create an end-to-end MLOps pipeline incorporating all learned concepts

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