We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. A new report by Scale AI uncovers what's working and what's not working with AI implementation, and the best practices for ML teams to move from just testing to real-world deployment. The report explores every stage of the ML lifecycle – from data collection and annotation to model development, deployment, and monitoring – in order to understand where AI innovation is being bottlenecked, where breakdowns occur, and what approaches are helping companies find success. The report's goal is to continue to shed light on the realities of what it takes to unlock the full potential of AI for every business and help empower organizations and ML practitioners to clear their current hurdles, learn and implement best practices, and ultimately use AI as a strategic advantage. For ML practitioners, data quality is one of the most important factors in their success, and according to respondents, it's also the most difficult challenge to overcome.

With Black History Month celebrations going on in several industries, it is important to realize the need and necessity to have black folks in the Artificial Intelligence (AI) arena as well! Here are a few examples of consequences that have occurred due to bias within AI systems and negligence within AI processes. In April 2020, a Google Image labelling service misclassified his black friend as a gorilla. There are a lot more models like this, and it isn't generally an issue at the human side, the AI or Ml based innovation will talk what the information has been taken care of to them. In the light of this, the tech industry has an important responsibility towards society, and here we are introducing you to seven top ML Leaders of Diversity -- who are making an impact in the Artificial Intelligence and Machine Learning world.