Courses and reading

Why it matters: GPU operations, accelerated data science, and networking (Spectrum, BlueField) sit on the chips and infrastructure layers of the stack.

• NVIDIA training portal
• Accelerated data science
• CUDA Python
• Networking academy

Why it matters: GPU vs CPU, on-prem and cloud placement, storage, networking, power, and cooling — the operator’s view of an AI cluster.

Stack: GPUs, NVIDIA, CUDA.

Why it matters: a cloud path for someone who already ships enterprise systems — Azure Machine Learning, cognitive services, and bot integration.

Stack: Azure, Python.

Why it matters: most of the lab apps only become real once they run as containers. Covers architecture, manifests, Minikube, kubectl, and a small web app with MongoDB.

Stack: Docker, Kubernetes, MongoDB, YAML.

Why it matters: API habits and workplace usage for Claude, which shows up in both the model layer and day-to-day building.

Anthropic Learn

Why it matters: Model Context Protocol is how an assistant reaches files, APIs, and data without a one-off integration each time.

DeepLearning.AI short course

Why it matters: short, specific labs — prompt engineering, LangChain, fine-tuning, embeddings on Vertex AI, diffusion, and Semantic Kernel — instead of one endless survey.

Why it matters: multi-agent roles and tool use, which is the shape of the predictive maintenance system.

Stack: Python, AutoGen.

Why it matters: research agents that route questions, summarize, and debug tool calls over your own documents.

Why it matters: a functional picture of how generative models work and where companies try to create value with them.

Stack: AWS, Python.

Why it matters: regression, trees, clustering, bias and variance, regularization, neural nets, and transformers are the vocabulary under every later course.

Software side: Python, data structures, TensorFlow or PyTorch, and scikit-learn.

Why it matters: instructor-led labs on training, CNNs, data augmentation, RNNs, autoencoders, and GANs.

Stack: GPU notebook, JupyterLab, TensorFlow, Keras.

Why it matters: Python plus AI-assisted coding, which is how a lot of the small apps on the work page were built.

Why it matters: reviews, CI, testing, and production habits around the models, not only the models themselves.

Course materials

Why it matters: a single map of paths, tools, and communities when the course list starts to sprawl.

Open the ByteByteGo map

Why it matters: Kleppmann’s three pillars — reliability, scalability, maintainability — are the checklist I use when a demo has to survive real data.

Why it matters: a repeatable frame and case studies for systems I have not operated at that scale yet.

Why it matters: Chip Huyen treats data, features, retraining, and monitoring as one system, which matches how infrastructure people already think.

Why it matters: a 7-step approach and worked examples (smart compose, personalized generation) for designing GenAI systems, not only calling an API.

Why it matters: ten end-to-end ML system questions with diagrams, useful when a project needs a boundary and a metric, not another notebook.

Why it matters: a long view of information networks, from bureaucracy to AI, which is the backdrop for the industry stack on the homepage.

Why it matters: a first-person account of how modern computer vision, and a lot of today’s AI, actually got built.

Why it matters: the friction between business goals and technical goals, which is also the subject of the doctoral research interest.

Why it matters: Nigel Vaz’s SPEED frame — strategy, product, engineering, experience, data — is a useful checklist when AI work has to land in an existing company.

Notes from the book

Why it matters: product language for shipping models, which complements the infrastructure background rather than replacing it.

• The 2025 AI Engineering reading list
• Generative AI and the history of architecture
• AI implementation in organizations: a systematic review
• Precursors of AI adoption in business
• An interactive agent foundation model
• The AI supply chain tug of war
• AI’s $600B question
• Notes: how to read AI research papers