Phase 4 • Advanced ML & MLOps

Phase 4 covers state-of-the-art ML techniques and production engineering. Learners explore advanced unsupervised learning, time series forecasting, transformers, generative models, and complete MLOps pipelines for enterprise deployment.

What you will practice

• Advanced clustering and dimensionality reduction techniques.
• Time series forecasting with ARIMA, Prophet, and deep learning.
• Building recommendation systems for personalization.
• Working with transformer architectures and attention mechanisms.
• Training generative models (VAEs, GANs, diffusion).
• Graph neural networks for relational data.
• Reinforcement learning and offline learning strategies.
• Model interpretability and fairness auditing.
• Causal inference and uplift modeling.
• Production NLP pipelines with modern transformers.
• CI/CD for ML with automated testing and deployment.
• Model serving patterns and infrastructure.
• Monitoring model performance and data drift.

Lesson sprint

• :material-tag: Day 55 – Advanced Unsupervised Learning: DBSCAN, hierarchical clustering, and autoencoders.
• :material-tag: Day 56 – Time Series and Forecasting: Forecast demand and trends with statistical and ML methods.
• :material-tag: Day 57 – Recommender Systems: Build collaborative filtering and matrix factorization systems.
• :material-tag: Day 58 – Transformers and Attention: Master attention mechanisms and transformer architectures.
• :material-tag: Day 59 – Generative Models: Create new data with VAEs, GANs, and diffusion models.
• :material-tag: Day 60 – Graph and Geometric Learning: Apply GNNs to network and relational data.
• :material-tag: Day 61 – Reinforcement and Offline Learning: Learn optimal policies from interaction and historical data.
• :material-tag: Day 62 – Model Interpretability and Fairness: Explain models and audit for bias.
• :material-tag: Day 63 – Causal Inference and Uplift Modeling: Measure true causal effects for decision-making.
• :material-tag: Day 64 – Modern NLP Pipelines: Build production NLP with BERT, GPT, and transformers.
• :material-tag: Day 65 – MLOps Pipelines and CI/CD Automation: Automate ML workflows with orchestration.
• :material-tag: Day 66 – Model Deployment and Serving Patterns: Deploy models with REST, gRPC, batch, and streaming.
• :material-tag: Day 67 – Model Monitoring and Reliability Engineering: Detect drift and maintain model health.

Learning outcomes

By completing Phase 4, you will be able to:

• Apply cutting-edge ML techniques to complex business problems.
• Build and deploy transformer-based models for NLP and vision.
• Create recommendation engines that personalize user experiences.
• Forecast time series with state-of-the-art methods.
• Ensure model fairness, interpretability, and reliability.
• Build complete MLOps pipelines with CI/CD automation.
• Monitor and maintain ML systems in production.

Ready to continue? Advance to Phase 5 – Business Intelligence to translate analytics into strategic impact.