Product details
Biographical note
Benjamin Tan Wei Hao is a product manager and principal engineer known for turning data into reliable ML delivery machines. With years leading platform builds, Benjamin distills deep MLOps experience into step-by-step guidance that helps readers ship scalable, maintainable models.
Shanoop Padmanabhan is a software engineering manager recognized for advancing autonomous-vehicle perception through robust ML platforms. He translates complex deployment challenges into replicable patterns.
Varun Mallya is a machine-learning engineer responsible for bank-wide ML platform stability and growth. With experience in scaling mission-critical models, Varun offers grounded insight on reliability and monitoring.
Machine learning models look great in notebooks, then collapse in production. Ready to build an ML platform that actually delivers? Here’s a step-by-step, project-driven guide to building an MLOps-ready platform from scratch.
Inside you’ll find:
- Step-by-step ML pipeline assembly: A true “from-scratch” playbook that assembles an end-to-end MLOps stack.
- Deploy machine learning models to production: Combine Kubeflow, MLflow, BentoML, Feast, and Evidently without vendor lock-in.
- Build end-to-end data pipelines: Move seamlessly from raw data to monitored, live predictions.
- Robust deployment patterns: Serve fast, scalable models that stay responsive under real traffic.
- Effective monitoring and explainability: Detect drift early and keep stakeholders confident.
Build a Machine Learning Platform (From Scratch) by Benjamin Tan Wei Hao, Shanoop Padmanabhan, and Varun Mallya delivers a practical field guide in print and eBook formats. Three veteran engineers lead you through every layer of modern MLOps.
The chapters construct two reference systems, an image classifier and a recommendation engine, while teaching orchestration, training, serving, and monitoring techniques. The actionable items for each concept include sample code, architecture diagrams, and checklists.
By the end of this book, you will end up with a reusable blueprint that slashes deployment time, reduces firefighting, and thrives with team growth. You will start shipping platforms that thrive.
Ideal for Python-savvy data scientists and software engineers eager to master production-quality machine learning.
PART 1: LAYING THE FOUNDATIONS
1. GETTING STARTED WITH MLOPS AND ML ENGINEERING
2. WHAT IS MLOPS?
3. BUILDING APPLICATIONS ON KUBERNETES
4. DESIGNING RELIABLE ML SYSTEMS
5. ORCHESTRATING ML PIPELINES
6. PRODUCTIONIZING ML MODELS
PART 2: DEVELOPING REAL-WORLD ML PIPELINES
7. DATA ANALYSIS & PREPARATION
8. MODEL TRAINING AND VALIDATION: PART 1
9. MODEL TRAINING AND VALIDATION: PART 2
10. MODEL INFERENCE AND SERVING
PART 3: CLOSING THE LOOP
11. MONITORING AND EXPLAINABILITY
APPENDICES
APPENDIX A: INSTALLATION AND SETUP
APPENDIX B: BASICS OF YAML
APPENDIX C: TABLE OF TOOLS
- Step-by-step ML pipeline assembly: A true “from-scratch” playbook that assembles an end-to-end MLOps stack.
- Deploy machine learning models to production: Combine Kubeflow, MLflow, BentoML, Feast, and Evidently without vendor lock-in.
- Build end-to-end data pipelines: Move seamlessly from raw data to monitored, live predictions.
- Robust deployment patterns: Serve fast, scalable models that stay responsive under real traffic.
- Effective monitoring and explainability: Detect drift early and keep stakeholders confident.
Product details
Biographical note
Benjamin Tan Wei Hao is a product manager and principal engineer known for turning data into reliable ML delivery machines. With years leading platform builds, Benjamin distills deep MLOps experience into step-by-step guidance that helps readers ship scalable, maintainable models.
Shanoop Padmanabhan is a software engineering manager recognized for advancing autonomous-vehicle perception through robust ML platforms. He translates complex deployment challenges into replicable patterns.
Varun Mallya is a machine-learning engineer responsible for bank-wide ML platform stability and growth. With experience in scaling mission-critical models, Varun offers grounded insight on reliability and monitoring.