“When you write a short story, you had better know the ending first”
Isaac Asimov
MLOPs
The goal of MLOps (Model not Machine Learning) Operations is to use DataOps pipelines to create new or improve existing Data Models for organizational applications, departments and/or third parties. DevOps tests the output, deploys, and manages MLOps models. The aim of the MLOps process is to create measurable and repeatable revenue streams and/or time/cost savings for the organization and its customers. This validation is often visualized via existing reporting tools and customer feedback.
MODEL OPERATIONS (AIOps) KNOWLEDGE BASE
Click @ for links
- @ ML/AI/Data-MAD Landscape via Matt Turck (2024 version below)
OVERVIEWS
- @ Asimov: 3 Laws of Robotics
- @ Halon AI Strategy Landscape
- @ McKinsey: The State of AI (2024)
- @ Deloitte/Snowflake: AI Success in 2024
- @ State of AI Report 2024: Benaich & Hogarth
- @ Awesome LLM: curated LLM list on Github (2025)
- @ The MAD Landscape via Matt Turck at FirstMark (2024)
- @ Visual Capitalist: Most Popular AI Tools by Monthly Site Visits (2025)
TRAINING
- @ EDX Dev Resources
- @ AI/ML Beginners Guide
- @ O’Reilly Online Training
- @ USD: Top 20 AI Courses
- @ Builtin: 22 AI Certifications
- @ Zdnet: Best free AI Courses
- @ Analytics Vidhya Courses
- @ Machine Learning Guide
- @ R tutorial via DataCamp
- @ Coursera: Machine Learning (Ng)
- @ Coursera/Stanford: ML Syllabus
- @ Coursera: Neural Networks for Machine Learning (Hinton)
- @ DZone: 35 Free Online Books on Machine Learning
- @ Google: Machine Learning Glossary
- @ Hadoop training via MapR
- @ Hadoop with Python via Glennklockwood
- @ Practical Machine Learning with Python(sentdex)
- @ Oxford Deep NLP (Blunsom et al. 2017)
- @ Stanford CS231n: Convolutional Neural Networks for Visual Recognition (2016)
- @ Stanford CS224n: Natural Language Processing with Deep Learning (2017)
- @ Statistics How To: The Practical Statistics Handbook
- @ SQL tutorial via CodeAcademy & another via W3
- @ SQL Data Warehousingvia Linda.com
- @ Udacity: Machine Learning (Georgia Tech)
- @ Udacity: Intro to Machine Learning (Thrun)
- @ Udacity: Intro to TensorFlow for Deep Learning (TensorFlow)
- @ AI/ML in Practice: four sections: Machine Learning, NLP, Python, and Maths (Robbie Allen 2018 Edition)
TOOLS & OTHER MEDIA
- @ xAI Grok
- @ AWS Titan
- @ Meta Llama
- @ OpenAI GPT4
- @ Google Gemini
- @ Microsoft Copilot
- @ Anthropic Claude
- @ Hugging Face ML
- @ Zapier: The Best LLMs in 2024
- @ Agile Manifesto via the authors @AM.org
- @ Big Data Manifesto & The Big Data Institute
- @ Snowflake “Gen AI and LLMs for Dummies” (2024)
- @ Cheat Sheet of Machine Learning and Python (and Math) Cheat Sheets
- @ CISO: Garbage In, Garbage Out is NOT Why Machine Learning Fails
- @ DataRobot: Introduction to AI Storytelling
- @ Data Science for Security Professionals by Charles Givre for O’Reily Media
- @ DataStax Webcasts via DataStax
- @ Data Driven Webcasts via Firstmark
- @ DataBricks: Productionizing Machine Learning: From Deployment to Drift Detection
- @ Rabbit Hole List II : DL/NLP, ML, Python resources (Allen 2018)
- @ Rabbit Hole List I: researchers, academic work, social links, and more (Allen 2017)
- @ Data Science Weekly: Full List of Data Science Resources
- @ Qubole: AI and Data Analytics Resources
- @ AnalyticBridge: Data Science e-books, webinars, and resources
COMMENTARY
- @ 60 Minutes: AI Stories (2019-2023)
- @ FRONTLINE: In the Age of AI full film (2019)
- @ YT Original: How Far is Too Far? | The Age of A.I. (2019)
- @ DW: Artificial intelligence & algorithms: pros & cons (2019)
- @ TechCrunch: Beyond AI with Sam Altman and Greg Brockman (2019)
- @ Bloomberg: Y-Combinator’s Sam Altman Says AI Can Reset Global Equality (2018)
EDX
ENERGY DATA SCIENCE