Deploying trained models takes models from the lab to live environments and ensures they meet business requirements and drive value. Model deployment can bring great value to organizations, but it is not a simple process, as it involves many phases, stakeholders and different technologies. In this article, we provide recommendations for data professionals who want to improve and streamline their model deployment process.

Making a Question Answering (QA) bot that can cite your own documentation and help channels is now possible thanks to chatGPT and Langchain, an open-source tool that cleverly uses chatGPT but doesn’t require retraining it. But it’s a far cry from “out of the box.” One example is that you have to get the prompt just right. To get an LLM (large language model) to do exactly what you want, your instructions will have to be very clear, so what if we automate that too?

by Victor Sonck, Developer Advocate, ClearML Sarcasm can be difficult to detect in text, especially for machines. However, with the power of large language models, it’s possible to create a tool that can identify sarcastic comments with high accuracy. That’s exactly what the ClearML team did with their latest project: a sarcasm detector that combines various ClearML tools to showcase the capabilities of MLOps.