Foundation models, like Meta’s Segment Anything Model (SAM), have provided a host of benefits for data and ML teams looking to expedite the production of training data whilst improving the quality. This webinar walks you through how to go one step further and fine-tune foundation models, in particular Meta AI's SAM, to maximize relevance to your specific use case Here are the key resources from the webinar: Encord Active GitHub - our open source tool that allowed us to conduct our research The Google Colab Notebook used in yesterday’s session Our ML Solutions Engineer’s Fine-Tune SAM blog post

The European Parliament recently voted to adopt the EU AI Act, marking the world’s first piece of legislation on artificial intelligence. The legislation intends to ban systems with an “unacceptable level of risk” and establish guardrails for developing and deploying AI systems into production. We've put together this learning pack to equip you with all the resources you need to understand how the AI Act will impact you, whether or not you're based in the EU. From webinar recordings to the most informative blogs, sign up now to get access to these crucial resources.

OpenAI’s ChatGPT and CLIP releases have revolutionised the ways in which organisations and individual contributors can ship features to their users. At Encord, we’ve focused on how the neural network (CLIP) and LLM (ChatGPT) can be combined to build an effective and powerful Semantic Visual Search. Frederik Hvilshøj, Lead ML Engineer with a PhD in Generative AI, joins Eric Landau, CEO and Co-Founder of Encord, to provide actionable insights into how to build this function from scratch. Here are the key resources from the webinar: Collaboration notebook used by Frederik CLIP [paper/repo] ChatGPT [product updates, documentation] Encord blog: Lessons Learned: Employing ChatGPT as an ML Engineer for a Day Encord blog: What is vector similarity search?