ML engineering education
A learning repository/course (Jupyter Notebooks) teaching how to build production-grade ML applications end-to-end, from experimentation to deployment.
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Open book on ML engineering covering infrastructure and large-scale training; same learning domain, different format (prose book vs. interactive course).
Machine Learning Engineering Open Book
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Narrower course focused on a specific LLM assistant, not covering the full production ML lifecycle.
Learn to build your Second Brain AI assistant with LLMs, agents, RAG, fine-tuning, LLMOps and AI systems techniques.
Vendor-specific notebooks for SageMaker, narrower than a general production ML course.
Example π Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using π§ Amazon SageMaker.
These projects were analysed and named Made-With-ML among their alternatives. The relationship is not symmetric β how Made-With-ML rates them is a separate judgement, made when Made-With-ML is analysed in its own right.
calls Made-With-ML βchoose for a production-focused ML curriculum with more MLOps depth but less agent/LLM breadth.β
Learn it. Build it. Ship it for others.
calls Made-With-ML βChoose it for a similarly practical, production-focused ML engineering course with a permissive MIT license and code-first notebooks.β
Learn ML engineering for free in 4 months! Register here ππΌ
calls Made-With-ML βChoose for a structured MLOps curriculum covering data/training/deployment, if your focus is production ML rather than LLM apps.β
In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
calls Made-With-ML βChoose if you want a production-focused ML course with deployment and iteration rather than a general roadmap.β
Roadmap to becoming an Artificial Intelligence Expert in 2022
calls Made-With-ML βChoose for a production/MLOps-focused curriculum rather than deep learning theory.β
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
calls Made-With-ML βProject-based course on production ML/MLOps; choose it when you want hands-on application development rather than a systems-principles textbook.β
Machine Learning Systems: Foundations, Scaling, Agentic AI, and Physical AI (Vols IβIV) β’ Harvard CS249r | https://mlsysbook.ai
calls Made-With-ML βWhen you want production/MLOps-focused learning rather than fundamentals.β
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
calls Made-With-ML βwhen you want a project-based focus on production ML/MLOps rather than classical ML theory.β
Open Machine Learning Course
calls Made-With-ML βUse it when you need a guided production-ML/MLOps curriculum rather than a math/CS theory compendium.β
Become a cracked AI/ML researcher/engineer with this unconventional textbook covering maths, computing, and ML with intuition.
calls Made-With-ML βChoose when your focus is production-grade ML development and deployment rather than general ML theory.β
:speech_balloon: Machine Learning Course with Python:
calls Made-With-ML βChoose it for a hands-on production-ML course instead of a systems-internals textbook, though it is MLOps-focused rather than compiler or cluster design.β
γMachine Learning Systems: Design and Implementationγ (V2 is launching soonοΌ
calls Made-With-ML βProduction-ML-focused notebooks; choose for MLOps-oriented learning instead of a general survey.β
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.