From Big Data to Smart Data: How to Manage, Clean and Curate Your Visual Datasets for AI Development

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

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.