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Faster time to market with Latent AI and Kili Technology, part 2

Kili and Latent AI have partnered to make edge AI easier to implement by combining high quality data with faster training, prototyping, and deployment. By combining approaches, we can forge a path toward AI solutions that not only overcome current deployment and scalability hurdles but also lay a solid foundation for the future of AI development … Continued

Faster time to market with Latent AI and Kili Technology, part 1

Latent AI helps organizations reduce the time it takes to prototype and train edge ML, simplify their development processes, and deliver ML efficient and powerful enough for compute constrained devices. We are actively engaging in a series of strategic partnerships that help combine our solutions to move models to market faster with repeatable processes that deliver reliable, … Continued

DevOps for ML Part 3: Streamlining edge AI with LEIP pipeline

Part 1: Optimizing Your Model with LEIP Optimize Part 2: Testing Model Accuracy with LEIP Evaluate Welcome to Part 3 of our ongoing DevOps For ML series that details how the components of LEIP can help you rapidly produce optimized and secured models at scale. In Parts 1 and 2, we have already explored model … Continued

DevOps for ML Part 1: Boosting model performance with LEIP Optimize

The Latent AI Efficient Inference Platform (LEIP) creates specialized DevOps processes for machine learning (ML) that produce ultra-efficient, optimized models ready for scalable deployment as executable files. But how does it work? How does AI actually go from development to deployment to a device? In this series of blog posts, we’ll walk you through the … Continued

Why smaller AI is still important in the age of bigger computers

What happens to our business if nobody needs bigger, faster computer processors? This was the quiet question keeping computer chip executives awake at night in the early 21st Century. It wasn’t that they were hitting a technical ceiling: their engineers continued to defy “Moore’s Law is dead” doomsayers, cranking out faster and faster chips year … Continued

Solving Edge Model Scaling and Delivery with Edge MLOps

The sheer amount of data and the number of devices collecting it means sending it to the cloud for processing is simply too slow and not scalable. Processing has to move closer to the source of the data at the edge. But getting AI models to work on edge devices fails far more often than … Continued