Build, deploy, and scale production ML systems
Machine Learning Engineering bridges the gap between data science and software engineering, focusing on building production-ready ML systems that scale. As an ML engineer, you will not only build models but also deploy them, monitor their performance, and maintain them in production environments. This comprehensive roadmap covers software engineering fundamentals, ML algorithms, deep learning, model optimization, deployment strategies, and MLOps practices. You will learn to work with frameworks like TensorFlow, PyTorch, and tools like Docker, Kubernetes, and cloud platforms. ML engineers are highly valued in tech companies, working on recommendation systems, search engines, fraud detection, autonomous vehicles, and more. The role requires strong programming skills, understanding of ML theory, system design knowledge, and DevOps expertise.
8-10 weeks
OOP, design patterns, testing, debugging
Arrays, trees, graphs, hash tables
Sorting, searching, dynamic programming
Version control, automated testing, deployment
10-12 weeks
Regression, classification, ensemble methods
Clustering, dimensionality reduction
Hyperparameter tuning, regularization
Selection, extraction, transformation
10-12 weeks
Architecture, training, optimization
Model building, custom layers, training loops
CNNs, object detection, segmentation
Transformers, BERT, GPT, fine-tuning
8-10 weeks
Flask, FastAPI, model serving
Containerization, images, compose
AWS SageMaker, GCP AI Platform, Azure ML
Quantization, pruning, distillation
8-10 weeks
Airflow, Kubeflow, MLflow
Drift detection, performance tracking
Experimentation, statistical significance
Automated training, testing, deployment
6-8 weeks
Multi-GPU, distributed data parallel
Spark, Hadoop, data lakes
ML system architecture, scalability
Streaming data, online learning