DeepLearning.AI's weekly newsletter. This issue introduces Andrew Ng's AI Engineering Skills Map, plus news on Meta, Google robotics and MiniMax's open video model.
Why I recommend it: Useful for mapping which AI engineering skills to learn next. DeepLearning.AI sells courses, so the skills map points toward its own catalog.
A free self-paced curriculum for the Forward Deployed Engineer role — the person who sits with a customer and turns an AI model into a system that actually runs in production. 11 modules, 150+ topic articles, 700+ open-book questions and 15 daily drill questions, covering systems thinking, problem decomposition, LLM/RAG/agent architecture, data and event systems, physical AI and robotics, plus client discovery, integration, deployment and stakeholder management. It also publishes a map of 15 layers of the modern AI engineering stack drawn from real FDE and AI-engineer job specs, with links out to where to learn each one.
Why I recommend it: The whole curriculum opens without an account or a card — I checked — and an account only saves your progress. There is no pricing page anywhere on it, so nothing is being held back behind a paywall today. Worth knowing why this matters: 'Forward Deployed Engineer' is a job title that is actually being hired for right now, and it pays like engineering while rewarding the consulting side most engineers avoid. Two flags. It is one person's independent site, not an accredited course or a certificate anyone will recognize — the value is the material, not a credential. And it makes a strong claim, that foundation models are becoming a commodity and the money lives in the last mile; that is an argument, not a measured finding, so read it as a well-argued position.
A free, live, online one-day conference on 22 October 2026 with 122 speakers across four tracks: BUILD (building with and of AI), LEAD (managing an AI-native organisation), SECURE (trust and safety) and a business track. Sessions are 30-minute practical implementation talks from engineers and managers at OpenRouter, Microsoft, Accenture, Qualcomm, CVS Health, Mastercard, Anaconda, Illumina and MITRE. The organisers state there are zero vendor pitches. Registration is free; the site also publishes free directories of AI conference dates, deadlines and free virtual events.
Details: Worth the calendar slot if you want to hear how AI is actually being shipped inside large companies rather than how it is marketed — the 30-minute implementation format and the no-vendor-pitch rule are the reasons. Register free and pick the track that matches your job; the talks run in parallel, so you cannot see everything live. Check their own page for whether replays are posted, and confirm the start time in your own time zone.
Andrew Ng on the skills that let a developer shape what gets built, not just implement someone else's spec: driving the build loop, product decisions, communication and high-agency ownership.
Why I recommend it: A clear answer to "what should I actually learn next" in AI work.
Early-career Data & AI Engineering opportunity at Procter & Gamble for 2027 graduates. A path into applied data science, analytics, and machine learning inside a global consumer-goods company.
Technical AI newsletter for engineers building on AI systems, covering evals, inference, and agentic architectures.
In plain terms: This technical newsletter and podcast covers how leading teams build artificial intelligence models, agents, and infrastructure. You can use it to learn how modern AI systems are built and keep your engineering knowledge up to date.
Why I recommend it: If you want an AI engineering job, this is the vocabulary you need.
