Newsletter Issue 21: Several Large Language Models are emerging: Google Bard/LaMDA, Meta's LLaMA, Amazon's Multimodal-CoT, HuggingFace's Bloom, and open-source ChatLLaMA.
Newsletter Issue 19: ChatGPT is part of OpenAI's GPT-3 family of large language models. It is immensely powerful but can confidently hallucinate too. Here is what ChatGPT can and can't do.
Newsletter Issue 18: Should you choose an all-in-one MLOps platform from your cloud provider or cobble together a solution from piecemeal tools?
Newsletter Issue 15: AI research continues to amaze us, but are those safe to use in products and services? Concerns about AI aligning with human goals have become real.
Newsletter Issue 14: Data pipelines transport data to the warehouse/lake. Machine Learning pipelines transform data before training/inference. MLOps pipelines automate ML workflows.
Newsletter Issue 13: Don't fall for the "AI-first" hype. Focus on being "data-first" and ML/AI will naturally follow.
Survey of data science and machine learning lifecycle from resource-constrained batch data mining era to current MLOps era of CI/CD/CT at the cloud scale.
Newsletter Issue 12: How to progressively adopt MLOps, but only as much as justified by your needs and RoI.
MLOps Lifecycle strings model and software development together in an unified machine learning life cycle for CI/CD/CT of ML products.
Newsletter Issue 11: You define logic in traditional programs. Machine Learning extracts logic (models) from the data.