Building operational machine learning applications comes with many data management challenges. Feature stores are some of the most effective tools for solving them. In this episode of The Data Standard, host Catherine Tao sits down with Willem Pienaar, a Tech Lead at Tecton, to talk about feature stores. Feature stores are operational data systems that bridge the gap between data science and production machinery. They enable data scientists to ship offline data into production and ensure their prediction models work with accurate data. Data is the hardest part of machine learning, as it keeps bringing chaos into an environment full of complexities that you need to simplify. Feature stores are there to help eliminate chaos and ensure you spend more time on positive rather than negative engineering. When it comes to the future of feature stores, Willem Pienaar anticipates a greater focus on data quality. He also believes that ML data monitoring and data validation software will become more prevalent in ML and data sciences operational aspects. Tune in with Catherine Tao to listen to this episode, as Willem Pienaar goes into detail about feature stores, data engineering and product lifecycles, challenges with ML data, and more.