The text highlights the progress made in machine learning (ML) in 2022, including significant advancements in generative AI, robotics transformers, and genome studies. However, it also notes that many ML teams are struggling to keep up with the rapid pace of innovation, citing issues such as model bias, lack of diversity in hiring and ethics, and inadequate monitoring tools. The report card on last year's predictions shows mixed results, with some predictions proving true (AI fairness getting worse before better, ML infrastructure ecosystem becoming more crowded and complex, ML engineering jobs outpacing available talent) and others being partially credited or false (enterprises shipping AI blind, the citizen data scientist rising). Looking ahead to 2023, the text predicts that generative AI will become mainstream but also faces growing pains, economic uncertainty will impact the ML infrastructure market, best-of-breed platforms will chip away at legacy players, and working with unstructured data will no longer be optional. Overall, while there are challenges ahead, the future of AI and ML teams holds promise for growth and improvement.