OpenAI's Batch API allows you to submit queries for 50% of what you'd normally pay. Not all their models work with the service, but in many use cases this will save you lots of money on your LLM inference, just so long as you're not building a chatbot!
On the difficulties in precisely defining a machine learning pipeline, exploring how code changes, versioning, and naming conventions complicate the concept in MLOps frameworks like ZenML.
Exploring the evolution of MLOps practices in organizations, from manual processes to automated systems, covering aspects like data science workflows, experiment tracking, code management, and model monitoring.
Today, we're back to LLM land (Not too far from Lalaland). Not only do we have a new LoRA + Accelerate-powered finetuning pipeline for you, we're also hosting a RAG themed webinar.
As our AI/ML projects evolve and mature, our processes and tooling also need to keep up with the growing demand for automation, quality and performance. But how can we possibly reconcile our need for flexibility with the overwhelming complexity of a continuously evolving ecosystem of tools and technologies? MLOps frameworks promise to deliver the ideal balance between flexibility, usability and maintainability, but not all MLOps frameworks are created equal. In this post, I take a critical look at what makes an MLOps framework worth using and what you should expect from one.
