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Make data-driven design decisions with LEIP Design

In a recent webinar, we shed light on the potential of LEIP Recipes to accelerate meaningful results, enhance model optimization, and minimize the time and effort invested in machine learning projects. LEIP Recipes are flexible templates within the Latent AI Efficient Inference Platform (LEIP) that equip your team with the tools necessary to work with greater DevOps for … Continued

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

Faster ML project design and creation with LEIP recipes

Our recent webinar shed light on the potential of LEIP Recipes to accelerate meaningful results, enhance model optimization, and minimize the time and effort invested in machine learning projects. Recipes are customizable templates within the Latent AI Efficient Inference Platform (LEIP), a comprehensive software development kit (SDK) to simplify and expedite your AI development. By streamlining DevOps for ML through specialized … 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 2: Testing model accuracy with LEIP evaluate

Part 1: Optimizing Your Model with LEIP Optimize The Latent AI Efficient Inference Platform (LEIP) SDK creates dedicated DevOps processes for ML. With LEIP, you can produce secure models optimized for memory, power, and compute that can be delivered as an executable ready to deploy at scale. But how does it work? How do you … 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

Federated learning: Balancing collaboration and privacy in the digital age

With user data, comes great responsibility From kindergartners picking up behavioral skills in the playground to developers using stack exchange to debug code, sociology tells us that humans are constantly learning from each other. If the goal of artificial intelligence (AI) is to imitate and ultimately supersede human intelligence, it only makes sense to utilize … Continued

Why Recipes Mean Reproducible Workflows: The AI Recipe

We previously explained Latent AI technology and how it delivers optimized edge models quickly and reliably by comparing it to the Iron Chef competitive cooking show. We’ve talked about how Iron Chef and Latent AI Recipes can be modified to meet regional tastes or specific hardware, respectively. We also touched on what it means to … 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