Overcoming Data Management Challenges in Machine Learning | Molecula

Описание: The adoption of machine learning in the enterprise continues to grow as more businesses – hungry for automation and intelligence – look to digitally transform legacy processes to enable greater efficiency, agility, and innovation. From collaboration and security to customer service and sales, established use cases are paving the way for new best practices and an expanding array of applications. At the same time, many machine-learning projects are still subject to ongoing technical challenges, including data quality, integration, and governance issues as well as problems architecting and optimizing models and pipelines.

To educate its readers about emerging best practices and strategies for overcoming data management challenges in machine learning, DBTA kicked off 2022 with a special roundtable webinar. Watch this video to dive into this exciting new area with our experts!

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