Understanding MLOps

• Definition and scope of MLOps
• Key components: Collaboration, Continuous Integration, Continuous Deployment, Continuous Training, Continuous Monitoring
• Benefits and challenges of implementing MLOps

Basic Concepts

• Overview of the machine learning lifecycle
• Key differences between traditional DevOps and MLOps
• Roles and responsibilities in an MLOps team (Data Scientist, ML Engineer, DevOps Engineer, Data Engineer)

 
Getting Started with Python

• Installing Python and setting up the development environment
• Python basics: Data types, control structures, functions, modules
• Overview of Python libraries for machine learning: NumPy, pandas, scikit-learn, matplotlib

Data Collection

• Identifying data sources: Databases, APIs, web scraping
• Data collection techniques and tools
• Handling different data formats: CSV, JSON, XML

Data Cleaning and Preprocessing

• Identifying and handling missing values
• Data normalization and standardization techniques
• Handling categorical data: One-hot encoding, label encoding
• Feature engineering: Creating new features, feature selection

Data Versioning

• Importance of data versioning in MLOps
• Tools for data versioning: Data Version Control (DVC), Git
• Integrating data versioning with ML pipelines

Introduction to Machine Learning

• Types of machine learning: Supervised, unsupervised, semi-supervised, reinforcement learning
• Common machine learning algorithms: Linear regression, logistic regression, decision trees, k-means clustering

Model Training

• Preparing data for training
• Training a model using scikit-learn
• Hyperparameter tuning: Grid search, random search

Model Evaluation

• Evaluation metrics for classification: Accuracy, precision, recall, F1 score, ROC-AUC
• Evaluation metrics for regression: Mean Absolute Error (MAE), Mean Squared Error (MSE), R-squared
• Cross-validation techniques: K-fold, stratified K-fold, leave-one-out

Deep Learning Basics

• Introduction to neural networks: Architecture, activation functions, forward and backward propagation
• Overview of deep learning frameworks: TensorFlow, Keras, PyTorch
• Building and training a simple neural network using TensorFlow/Keras

Model Optimization

• Techniques for improving model performance: Regularization, dropout, early stopping
• Hyperparameter tuning using libraries: Optuna, Hyperopt

Ensemble Methods

• Introduction to ensemble learning: Bagging, boosting, stacking
• Implementing Random Forest, Gradient Boosting, AdaBoost using scikit-learn
• Using XGBoost and LightGBM for model training and evaluation

Introduction to MLOps Tools

• Overview of popular MLOps tools: Kubeflow, MLflow, TFX (TensorFlow Extended)
• Comparison of features and use cases

Docker for MLOps

• Basics of containerization: What is Docker, benefits of using Docker
• Creating and managing Docker containers: Dockerfile, docker-compose
• Best practices for containerizing ML models

Kubernetes for MLOps

• Introduction to Kubernetes: Concepts, architecture, components
• Deploying ML models on Kubernetes: Creating a Kubernetes cluster, deploying pods, services, and deployments
• Scaling and managing ML workloads with Kubernetes

Deployment Strategies

• Batch vs online deployment: Pros and cons
• A/B testing and canary releases for ML models
• Rolling updates and blue-green deployments

Serving Models

• Using Flask for building simple web APIs to serve ML models
• Introduction to model serving platforms: TensorFlow Serving, TorchServe, BentoML
• Best practices for scalable and reliable model serving

Deployment on Cloud

• Overview of cloud platforms for ML: AWS, Azure, Google Cloud
• Deploying models on AWS SageMaker, Azure ML, and Google AI Platform
• Introduction to serverless deployment with AWS Lambda, Google Cloud Functions

Introduction to CI/CD

• Importance of CI/CD in MLOps
• Principles of CI/CD: Automated testing, continuous integration, continuous delivery, continuous deployment
• Overview of CI/CD tools: Jenkins, GitLab CI, CircleCI, GitHub Actions

Setting up CI/CD Pipelines

• Creating CI/CD pipelines for ML projects
• Automating model training, testing, and deployment processes
• Integrating CI/CD pipelines with version control systems (e.g., Git)

Monitoring CI/CD Pipelines

• Ensuring pipeline reliability and performance
• Implementing logging and alerting mechanisms
• Handling pipeline failures and rollbacks

Model Monitoring

• Importance of monitoring models in production
• Tools for model monitoring: Prometheus, Grafana, ELK Stack (Elasticsearch, Logstash, Kibana)
• Setting up dashboards and alerts

Handling Data Drift

• Understanding data drift and its impact on model performance
• Techniques for detecting data drift: Statistical tests, monitoring distributions
• Strategies for mitigating data drift: Retraining models, updating features

Model Retraining

• Importance of continuous model retraining
• Setting up automated retraining pipelines
• Best practices for managing retrained models

Case Studies

• Case studies from different industries: Healthcare, finance, retail, manufacturing
• Analysis of successful MLOps implementations
• Key lessons learned from real-world deployments

Best Practices

• Industry standards and guidelines for MLOps
• Ethical considerations in MLOps: Fairness, transparency, accountability
• Ensuring security and compliance in ML operations

Capstone Project

• End-to-end project to apply all learned concepts
• Defining project scope and objectives
• Building, deploying, and monitoring a machine learning model
• Presentation and peer review of the capstone project

Advanced MLOps Concepts

• Introduction to federated learning: Concepts, use cases, challenges
• Overview of edge AI: Deploying models on edge devices, use cases, challenges

Future Trends in MLOps

• Emerging technologies and tools in MLOps
• Predictions for the future of MLOps: Automation, AI-driven operations, advanced monitoring

Preparing for the Future

• Lifelong learning and upskilling in MLOps
• Resources for continued education: Online courses, books, communities
• Building a professional network in the MLOps field

Final Review and Certification

• Recap of key concepts and skills learned.
• Preparing for the certification exam
• Presentation and feedback on the capstone project
• Awarding of certificate of completion
• Networking and community building opportunities