The Machine Learning Engineering track is designed for professionals who want to bridge the gap between data science and production systems. This comprehensive 12-week program focuses on building, deploying, and maintaining machine learning systems in production environments using industry best practices.

Program Structure

This track emphasizes the engineering aspects of machine learning across three intensive months, covering everything from foundational ML concepts to advanced MLOps practices and production deployment strategies.

Month 1: ML Engineering Foundations

• Week 1-2: SQL for Machine Learning Workflows

  • Advanced SQL for feature engineering and data preparation
  • Time-based features and window functions for ML
  • Data pipeline design for machine learning workflows
  • Optimizing queries for large-scale feature generation

• Week 3: Database Programming for ML Automation

  • PL/SQL for automated data preprocessing pipelines
  • Stored procedures for feature engineering and data validation
  • Database-driven ML workflow automation
  • Data quality checks and monitoring in databases

• Week 4: Python & Mathematical Foundations

  • Advanced Python programming for ML engineering
  • Mathematical foundations: linear algebra, calculus, statistics
  • Software engineering principles for ML projects
  • GitHub workflows and collaborative development for ML teams

Month 2: Core ML & Business Applications

• Week 5: Machine Learning Algorithm Mastery

  • Comprehensive coverage of ML algorithms: regression, classification, clustering
  • Time series forecasting and anomaly detection techniques
  • Algorithm selection and hyperparameter optimization
  • Cross-validation strategies and model evaluation metrics

• Week 6: Real-World Business Applications

  • Fraud detection systems and anomaly detection
  • Customer churn prediction and retention modeling
  • Demand forecasting and inventory optimization
  • Recommendation systems and personalization engines

• Week 7: Advanced ML Models & Techniques

  • Ensemble methods: XGBoost, LightGBM, Random Forest
  • Introduction to deep learning with TensorFlow/PyTorch
  • Feature selection and engineering best practices
  • Handling imbalanced datasets and edge cases

• Week 8: Model Evaluation & Optimization

  • Advanced model evaluation techniques and metrics
  • A/B testing for machine learning models
  • Model interpretability and explainable AI (SHAP, LIME)
  • Performance optimization and computational efficiency

Month 3: MLOps & Production Deployment

• Week 9: MLOps Lifecycle & Experiment Tracking

  • Complete MLOps lifecycle understanding and implementation
  • Experiment tracking with MLflow, Weights & Biases
  • Model versioning and artifact management
  • Reproducible ML workflows and data lineage

• Week 10: CI/CD for Machine Learning

  • CI/CD pipelines specifically designed for ML projects
  • GitHub Actions for automated testing and deployment
  • Docker containerization for ML applications
  • Infrastructure as Code for ML environments

• Week 11: Production Deployment Strategies

  • Model deployment options: REST APIs, batch processing, streaming
  • Google Cloud Vertex AI and cloud ML platforms
  • Microservices architecture for ML systems
  • Load balancing and scaling strategies for ML APIs

• Week 12: Monitoring & Maintenance

  • Production monitoring and alerting for ML systems
  • Data drift and model drift detection
  • Automated retraining and model updates
  • Performance monitoring and system optimization

Technology Stack

Machine Learning Frameworks

• Core ML: scikit-learn, XGBoost, LightGBM, CatBoost
• Deep Learning: TensorFlow 2.x, PyTorch, Keras
• Specialized: Prophet (time series), SHAP (explainability)
• AutoML: AutoML tools for rapid prototyping

MLOps & Production Tools

• Experiment Tracking: MLflow, Weights & Biases, Neptune
• Model Serving: TensorFlow Serving, TorchServe, Seldon
• Workflow Orchestration: Apache Airflow, Kubeflow Pipelines
• Feature Stores: Feast, Tecton, cloud-native solutions

Cloud & Infrastructure

• Primary: Google Cloud Platform (Vertex AI, Cloud Functions)
• Containerization: Docker, Kubernetes for model deployment
• CI/CD: GitHub Actions, Jenkins, GitLab CI
• Monitoring: Prometheus, Grafana, cloud monitoring tools

Development & Collaboration

• Programming: Python 3.x, SQL, Bash scripting
• Version Control: Git, DVC (Data Version Control)
• APIs: FastAPI, Flask for model serving
• Testing: pytest, unittest for ML code testing

Hands-On Projects

Project 1: End-to-End ML Pipeline

• Build complete ML pipeline from data ingestion to deployment
• Implement feature engineering and model training automation
• Create comprehensive testing suite for ML components
• Deploy model as REST API with monitoring and logging

Project 2: Real-Time ML System

• Develop real-time fraud detection or recommendation system
• Implement streaming data processing and feature computation
• Deploy scalable model serving infrastructure
• Create monitoring dashboard for system performance

Project 3: MLOps Platform

• Build comprehensive MLOps platform with experiment tracking
• Implement automated model training and deployment pipelines
• Create model registry and version management system
• Develop automated testing and quality assurance processes

Capstone Project: Production ML System

• Complete production-ready ML system for business use case
• Implement full MLOps lifecycle with CI/CD automation
• Include monitoring, alerting, and automated retraining
• Deploy on cloud infrastructure with scaling capabilities
• Present business impact and technical architecture to stakeholders

Prerequisites

Required: Completion of Data Foundations track or equivalent experience including:

• Strong SQL and Python programming skills
• Basic understanding of machine learning concepts
• Experience with data analysis and statistical thinking
• Familiarity with software development practices

Recommended:

• Mathematics: Linear algebra, calculus, and statistics
• Cloud Computing: Basic understanding of cloud platforms
• Software Engineering: Object-oriented programming and design patterns
• DevOps: Basic familiarity with CI/CD and containerization

Career Outcomes

Graduates will be ready for ML engineer, MLOps engineer, and senior data scientist roles with expertise in production machine learning systems and modern deployment practices.

Target Roles & Compensation

• ML Engineer: $100,000 - $160,000+ annually
• Senior ML Engineer: $130,000 - $200,000+ annually
• MLOps Engineer: $120,000 - $180,000+ annually
• Principal ML Engineer: $150,000 - $230,000+ annually
• ML Engineering Manager: $160,000 - $250,000+ annually

High-Demand Skills

• Production ML: Deploy and maintain ML systems at scale
• MLOps Expertise: Automation and lifecycle management
• Cloud Platforms: GCP, AWS, Azure ML services
• System Design: Scalable ML architecture and infrastructure
• Business Impact: Translate ML capabilities into business value

Industry Applications

• Technology: Search, recommendation, and personalization systems
• Finance: Risk modeling, algorithmic trading, and fraud detection
• Healthcare: Diagnostic systems and drug discovery platforms
• Autonomous Systems: Self-driving cars and robotics
• E-commerce: Dynamic pricing and inventory optimization
• Entertainment: Content recommendation and generation systems

Professional Development

Industry Certifications

• Google Cloud: Professional ML Engineer
• AWS: Machine Learning Specialty
• Microsoft: Azure Data Scientist Associate
• MLOps: Kubeflow and MLflow certifications

Technical Leadership

• Lead ML engineering teams and architect enterprise solutions
• Contribute to open-source ML and MLOps projects
• Speak at ML conferences and write technical publications
• Mentor junior engineers and establish best practices

Specialization Areas

• Computer Vision: Image and video processing applications
• NLP: Language models and text processing systems
• Time Series: Forecasting and anomaly detection systems
• Reinforcement Learning: Game AI and optimization systems

Next Steps

Advanced Tracks

• Generative AI Hero Track: For large language models and generative AI
• Agentic AI GCP Track: For autonomous AI systems and agents
• Data Engineering GCP Track: For ML infrastructure and data platforms

Career Advancement

• Principal/Staff ML Engineer roles at top tech companies
• Founding ML Engineer at high-growth startups
• ML Consultant for enterprise digital transformation
• Research Engineer at AI labs and research institutions

Emerging Technologies

• Edge ML and mobile deployment optimization
• Quantum machine learning and quantum computing
• Federated learning and privacy-preserving ML
• AutoML and neural architecture search platforms