Bridging the chasm between ‘Just AI’ and ‘Useful AI’ can be challenging, however it’s apparent that leveraging valuable data is crucial to this. As access to data increases, computer vision teams need to produce informative and reliable training data as a priority, one approach is through developing active learning pipelines. From data curation to annotation and beyond, this webinar will provide you with the tools to implement active learning pipelines and level up your computer vision models Here are the key resources from the webinar: [Guide] How to curate your data [Case Study] How one customer improved per-class performance by 67%

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

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?