Subject archive for "machine-learning"

True reproducibility means anyone can return to a point in time — anywhere in the AI/ML lifecycle — and see how a model was built and understand its purpose and KPIs. Yet, most AI models are built outside of controlled environments and systems of record. Enterprise AI platforms like Domino solve this by automatically unifying and capturing all model provenance and all artifacts across teams, users, tools, and environments without manual detective work, which can produce mixed results.

See how NVIDIA's RAPIDS v23.10 revolutionizes AI workloads with GPU-accelerated Pandas on Domino, offering massive speed boosts and data handling efficiency.

Domino's integrated AutoML offers scalable, reproducible, & flexible solutions with editable code, diverse team collaboration, & hybrid-cloud support.

OpenAI demonstrated the profound impact generative AI could have. Such techniques turn datasets into transformative tools and products. Tangible AI projects that are both inspiring and can save your company time and money. Better yet, you are in a good position to aim high.

In today's economic environment, all organizations need to unlock greater AI value, faster. 98% of CDOs and CDAOs say the companies that bring AI and ML solutions to market fastest will be the ones who survive and thrive in the upcoming times of economic uncertainty. More than ever, organizations need to scale the creation and operationalization of ML models across their businesses and bring to bear the latest, most powerful AI methods and tools.

A picture is worth 1000 words, so let's get right into exploring Domino Code Assist (DCA). As I mentioned in my prior blog, with DCA you can import a dataset, make a few data visualizations, and deploy those data visualizations as a Python data app - all through a point-and-click interface. At the end of this, you have a perfectly executable Python or R script that follows the steps that you took in the UI.