GenAI-DevOps-Primer Overview

  • The inspiration and much of the material for the below GenAI resources comes from none other than Patrick Debois, the Godfather and OG of DevOps cultural movement and DevOpsDays. The first NYC GenAI DevOps Hackathon was held on January 24th 2024. Like Ghent in 2009, it was a transitional moment in DevOps: dev,sec,and ops have a cool cousin: data science. Through the last 15 years there have been transformational waves in devops like DevSecOps (security invited to the party) and around 2013 global acceptance then 2016 the global hegemony of devops practices. The addition of LLMs to integrations and new toolings feels very right. The dev/sec/ops frame Patrick provides makes the material even more accessible and understandable. Patrick, as a learner, has a special way of doing this.
  • MongoDB and Amazon AWS sponsored the event. Stacks in the hackathon included using AWS Sagemaker, Kendra, Amazon Q and S3 to create a sample app. MongoDB Atlas and vector databases were focussed and using MongoDB was a highlight as easy-to-deploy. See [MongoDB setup sheet] (all free-tier).
  • Takeaway: Working with LLMs and tool integrations with the sensibilities of DevSecOps is the default. Solutions being built by startups have DevSecOps (and now GenAI) sensibilities baked-in. The emergence of models as utility, and the prodigious growth of and how to create value from unharnessed unstructured data for companies holds great promise.

Followup MongoDB Hackathons for Vectorizing Data

  • April 8th resources and links

Patrick's Primers

GenAI Talks

Other Talks

LangChain

Prompt Engineering

- Reference Guide

  • Fabric Prompting with templates. The author, Daniel has good content and is worth a follow.

Other Great Follows in the GenAI and LLMs Space

  • Mark Hinkle, Ruevan Cohen, Joseph Enochs, Bill Bensing,
  • HuggingFace,

Significant Papers (a Long list coming soon)

  • Timnit Gebru was terminated from Google after the publication of this paper. This was ~2021 before LLMs were as common knowledge.

Optional Helpful Tracks to Pursue

  • Provide Python Primer. Covering basics like datatypes, structures, loops, conditionals. [Links] Feature using Jupyter notebooks, local and cloud based. How to launch and use Jupyter Notebooks

  • Git and version control primers and quick how-tos

  • Using APIs, step through request/response e.g. get, post, demo using Postman

  • Theoretical fundamental AI learnings and subsets. ML(subset: deep learning supervised/unsupervised), neural networks, NLP, reinforcement learning, prompt engineering, computer vision. Neural networks:review (forward and back propagation, gradient descent algorithm, weighting )

  • The difference between neural network architectures and more recent transformer architectures (2017).

  • Get an idea of the models and how they are trained
  • Text Embeddings and vector databases, why RAGs are so awesome
  • Advanced prompt engineering methods
  • AutoGen (MS) agent based, Advanced Document QA/multi-modal

GenAI and Deep Learning Course Material