MLA 023 Claude Code Components

Agentic engineering shifts the developer role from manual coding to orchestrating AI agents that automate the full software lifecycle from ticket to deployment. Using Claude Code with MCP servers and git worktrees allows a single person to manage the output and quality of an enti ...  Show more

Explains language models (LLMs) advancements. Scaling laws - the relationships among model size, data size, and compute - and how emergent abilities such as in-context learning, multi-step reasoning, and instruction following arise once certain scaling thresholds are crossed. The ...  Show more

Want to learn about AI and machine learning? There are plenty of resources out there to help — blogs, podcasts, YouTube tutorials — perhaps too many. Machine learning engineer Santiago Valdarrama has taken a far more focused approach to helping us all get smarter about the field. ...  Show more

<span class="s1">Alex Reeves joins us to discuss some of the challenges around building a serverless, scalable, generic machine learning pipeline.<span class= "Apple-converted-space"> </span> The is a technical deep dive on architecting solutions and a discussion of ...

In this episode, I discuss the architecture of a “machine learning house”, representing the skills and learnings you can use as foundations to build your data science career. Additional materials: www.superdatascience.com/474 

At the recent ODSC East conference, Daniel got a chance to sit down with Erin Mikail Staples to discuss the process of gathering human feedback and creating an instruction tuned Large Language Models (LLM). They also chatted about the importance of open data and practical tooling ...  Show more