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online course
The Only ML Course Built for Senior Engineers
Already an experienced software engineer? We teach you how to add Machine Learning and AI
on top of your skills and transition into an ML Engineer role in 3 months.
Watch trailer
Tools used in the course
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Weights & Biases
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MLflow
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GCP
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AWS
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Weaviate
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Pinecone
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Transformers
(Hugging Face) -
Kubernetes
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Docker
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TensorFlow
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PyTorch
-00:55
ML Engineer in 12 Weeks
Apply to join the next cohort
180+ hours of hands-on ML and AI training
4 live production ML and LLM systems built and deployed
12-week structured transition from Senior Software Engineer to ML Engineer
Dedicated ML interview preparation with real system design walkthroughs
Student Reviews
Course overview
Support that empowers you to finish writing your first draft.
12 weeks
Week One
1. ML Engineer Role and Workflow
A practical overview of ML engineer workflows, tools, and responsibilities, including how they differ from traditional software engineering roles.
2. ML Architectures Explained
A practical overview of core ML architectures - CNNs, RNNs, transformers, and LLM-based systems - when to use each, their trade-offs, and how they are applied in real products.
Week Two
3. Data, Features, and ML Inputs
How data flows into ML systems: datasets, feature engineering, data leakage, and preparing data for training and inference.
4. Classical ML in Production
Regression and tree-based models, when they outperform deep learning, and how they are used in real production systems.
Week Three
5. Deep Learning Foundations
Core deep learning concepts needed in practice, with a focus on CNNs and transformers rather than academic theory.
6. Training and Fine-Tuning Models
How models are trained and fine-tuned in real environments, including performance, cost, and iteration trade-offs.
Week Four
7. LLM Systems and Modern AI Products
Building LLM-powered systems using APIs, embeddings, RAG patterns, and understanding real-world limitations.
8. Vector Search and Retrieval Systems
How vector databases work, similarity search, and how retrieval layers are used in modern AI applications.
Week Five
9. Experiment Tracking and Model Versioning
Managing experiments, model versions, and artifacts using industry-standard ML tooling.
10. Model Serving and Inference Patterns
Online vs batch inference, latency considerations, scaling strategies, and reliability in production.
Week Six
11. Docker for ML Systems
Containerizing ML workloads for reproducible training and deployment across environments.
12. Kubernetes for ML Workloads
Deploying and scaling ML services on Kubernetes, including GPU workloads and production patterns.
Week Seven
13. Cloud Infrastructure for ML
Running ML systems on AWS/GCP, including compute, storage, and managed ML services.
14. MLOps and Model Lifecycle Management
CI/CD for ML, retraining strategies, monitoring, rollback, and handling data and model drift.
Week Eight
15. Capstone Production Projects
Building and deploying live ML and LLM systems that form a real portfolio for ML engineering roles.
16. End-to-End ML System Design (Real Case Studies)
Design complete ML systems from scratch: problem framing, data strategy, model choice, infrastructure, and operational trade-offs based on real company scenarios.
Week Nine
17. Production Debugging & Failure Modes
What breaks in real ML systems: bad data, silent model degradation, latency spikes, hallucinations in LLMs, cost explosions - and how to debug and fix them.
18. ML Performance, Cost, and Trade-offs
Optimizing models for latency, throughput, and cloud cost. Choosing between accuracy vs speed vs cost in real production environments.
Week Ten
19. Working with Product, Data, and Business Teams
How ML engineers collaborate with product managers, data teams, and stakeholders. Turning vague business problems into ML-ready tasks.
20. Interview-Ready ML Projects & System Walkthroughs
Deep dives into your capstone projects: explaining architecture decisions, trade-offs, failures, and improvements the way top companies expect in ML interviews.
Week Eleven
21. Designing ML Systems That Age Well
How to build ML systems that survive changing data, evolving products, new models, and growing scale - instead of breaking after the first release.
22. ML System Design Interviews
How to approach ML system design questions: framing the problem, choosing architectures, defining data flows, and explaining trade-offs clearly and confidently.
Week Twelve
23. ML Engineering Interview Deep Dives
Common interview topics for ML engineers: model selection, data leakage, training vs inference, scaling, monitoring, and real-world failure cases.
24. Behavioral & Experience-Based ML Interviews
How to present your transition story, explain past projects, handle “no ML background” questions, and position yourself as a senior engineer moving into ML.
Who is this course for
Check if your skills check
This is a quick self-check test from 7 questions to confirm you have a solid software engineering foundation for MLBootcamp course.
It will take less than 2 minutes
Question 1 of 7
You successfully completed test!
Frameworks and platforms
Select frameworks or platforms where you’ve shipped features or maintained production code.
Backend fundamentals
Mark the backend concepts you’ve implemented in production or serious side projects.
DevOps and shipping
Pick the operational skills you’ve used while deploying, monitoring, or maintaining live systems.
Data and system thinking
Select architecture and data design topics you can reason about with real implementation context.
Algorithms and data structures
Choose practical algorithm and data structure topics you can apply while solving engineering problems.
Math (practical level)
Select the math foundations you’re comfortable using when evaluating model behavior and trade-offs.
your result:
Good Fit!
Some advice. And come back to us later.
ML Engineer in 12 Weeks
Apply to join the next cohort
180+ hours of hands-on ML and AI training
4 live production ML and LLM systems built and deployed
12-week structured transition from Senior Software Engineer to ML Engineer
Dedicated ML interview preparation with real system design walkthroughs
Be ready for real ML engineering work
Job-Ready for ML Roles
Be confident applying for ML Engineer roles (mid-level+) with real project experience.
End-to-End ML Engineering
Work like a production ML engineer: data - training - deployment - iteration.
2026 ML Landscape
Understand what’s actually used in LLMs, CNNs, and classical ML - and why.
Think Like an ML Engineer
Turn business goals into ML-ready systems and make confident architecture decisions under real constraints.
Projects you will build
Three production-grade systems that prove real ML engineering skills across LLMs, computer vision, and classical ML.
Project 1. LLM agent service
Voice AI Calling Agent
Design and deploy an AI agent that can handle real phone calls: understand intent, follow workflows, and complete tasks reliably.
Real-time conversation flow and tool calling
Context + memory + safe action handling
Latency, cost, and failure-mode control
Deploy as a live service with monitoring
Project 2. Vision generation pipeline
Talking-Head Video Generator
Build an end-to-end system that generates realistic talking-head video from text or audio, including preprocessing, inference, and production delivery.
Face/video preprocessing pipeline and quality control
Model inference workflow and performance constraints
Production delivery: rendering, batching, and scaling
Evaluation of realism, artifacts, and failure handling
Project 3. Time-series prediction system
Trading Bot with Forecasting + Risk Controls
A production-style trading system focused on regression/forecasting, backtesting discipline, and safe execution. The goal is engineering rigor, not profit promises.
Time-series feature pipeline and leakage-safe training
Regression/forecasting models with strong baselines
Backtesting engine + risk rules (position sizing, stop logic)
Live execution service with monitoring and fail-safes
ML Engineer in 12 Weeks
Apply to join the next cohort
180+ hours of hands-on ML and AI training
4 live production ML and LLM systems built and deployed
12-week structured transition from Senior Software Engineer to ML Engineer
Dedicated ML interview preparation with real system design walkthroughs
Frequently asked questions
This course is built for experienced software engineers who want to move into ML roles. If you’ve shipped production systems and want to add ML/AI on top, you’re the target audience.
No. We cover ML fundamentals quickly and spend most time on production workflows, tooling, and real-world systems.
Plan for 8–12 hours per week. You’ll split time across guided lessons, labs, and project work.
Yes. You’ll build three end-to-end systems across LLMs, computer vision, and classical ML, including deployment and monitoring.
It’s cohort-based with weekly milestones, feedback, and live sessions to keep you accountable.
Yes. We include ML system design walkthroughs, project deep dives, and interview prep tailored for senior engineers.