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The survey asked companies how they used two overlapping types of tools to deploy analytical models: Data operations (DataOps) tools, which focus on creating a manageable, maintainable, automated flow of quality-assured data. If deployment goes wrong, DataOps/MLOps can even help solve the problem. ML Software Development.
Access to high-quality data can help organizations start successful products, defend against digital attacks, understand failures and pivot toward success. Emerging technologies and trends, such as machine learning (ML), artificial intelligence (AI), automation and generative AI (gen AI), all rely on good data quality.
When it comes to AI outputs, results will only be as strong as the data that’s feeding them. Trusting your data is the cornerstone of successful AI and ML (machine learning) initiatives, and data integrity is the key that unlocks the fullest potential.
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