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Forget the cloud, GIS and edge AI solve problems where they happen

Driven by the urgency to solve real-world challenges, organizations increasingly recognize the power of AI. Powerful cloud-based models like large language models (LLMs) or computer vision excel in the cloud but falter when faced with locations that have low bandwidth or fluctuating connectivity to the cloud. For organizations dealing with dynamic situations like disaster recovery … Continued

Find your best model using LEIP recipes

Researching which hardware best suits your AI and data can be a time consuming and frustrating process that requires machine learning (ML) expertise to get right. LEIP accelerates time to deployment with Recipes, a rapidly growing library of over 50,000 pre-qualified ML model configurations that let you quickly compare performance across different hardware targets (CPUs, … Continued

Reduce your AI/ML cloud services costs with Latent AI

Cloud computing offers more operational flexibility than privately maintained data centers. However, operational expenses (OPEX) can be especially high for AI. When deployed at scale, AI models run millions of inferences which add up to trillions of processor operations. It’s not just the processing that’s costly. Having large AI models also means more storage costs. … Continued

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