The complete guide to data engines for AI
Less than a decade ago, generating entire software programs, marketing campaigns, and works of art in a matter of minutes or hours would have been impossible. Today's workforce, however, is armed with AI solutions like Copilot, ChatGPT, Stable Diffusion and more — with new and more powerful tools launching every day. AI has become an integral part of our day-to-day lives.
For enterprises, it's more important than ever to use AI effectively and quickly to build and maintain their competitive advantages. Today's AI teams have more computing power, more data, and more pre-trained models than before, but they also face more challenges and setbacks, including:
Spending more time wrangling data than building or training models
Navigating stores of unstructured data and drawing out what's relevant to specific use cases
Maintenance-heavy custom MLOps infrastructure and legacy data labeling services
A lack of established practices for handling emerging edge cases, use case expansions, and other common changes
An AI platform offers the tools and infrastructure to help mitigate these challenges and accelerate AI development. Read the guide to learn how.
