The data and AI industries are constantly evolving, and it’s been several years full of innovation. Even less experienced technical professionals can now access pre-built technologies that accelerate the time from ideation to production. As a result, employers no longer have to invest large sums to develop their own foundational models. They can instead leverage the expertise of others across the globe in pursuit of their own goals.
However, the road to AI victory can be bumpy. Such a large-scale reliance on third-party AI solutions creates risk for modern enterprises. It’s hard for any one person or a small team to thoroughly evaluate every tool or model. Yet, today’s data scientists and AI engineers are expected to move quickly and create value. The problem is that it’s not always clear how to strike a balance between speed and caution when it comes to adopting cutting-edge AI.
As a result, many companies are now more exposed to security vulnerabilities, legal risks, and potential downstream costs. Explainability is also still a serious issue in AI, and companies are overwhelmed by the volume and variety of data they must manage. Data scientists and AI engineers have so many variables to consider across the machine learning (ML) lifecycle to prevent models from degrading over time. It takes a highly sophisticated ML operation to build and maintain effective AI applications internally. The alternative is to take advantage of more end-to-end, purpose-built ML solutions from trusted enterprise AI brands.
Introducing Cloudera AMPs
To help data scientists and AI engineers, Cloudera has released several new Accelerators for LL Projects (AMPs). Cloudera’s AMPs are pre-built ML prototypes that users can deploy with a single click within click here.
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