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The recent success of artificial intelligence based large language models has pushed the market to think more ambitiously about how AI could transform many enterprise processes. However, consumers and regulators have also become increasingly concerned with the safety of both their data and the AImodels themselves.
Think about securing training data by protecting it from theft and manipulation. Organizations can use datadiscovery and classification to detect sensitive data used in training or fine-tuning. They can also implement data security controls across encryption, access management and compliance monitoring.
An enterprise data catalog does all that a library inventory system does – namely streamlining datadiscovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.
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Delphina Demo: AI-powered Data Scientist Jeremy Hermann | Co-founder at Delphina | Delphina.Ai In this demo, you’ll see how Delphina’s AI-powered “junior” data scientist can transform the data science workflow, automating labor-intensive tasks like datadiscovery, transformation, and model building.
It also addresses data security obligations such as data access intelligence and governance, data security posture management, data minimization and breach management. Our Data Command Center provides unparalleled visibility and control enabling the safe use of data and AI.
One of the hardest things about MLOps today is that a lot of data scientists aren’t native software engineers, but it may be possible to lower the bar to software engineering. And so those are more sideshows of the conversations or other complementary pieces, maybe. Thank you for sharing that, David.
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