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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

Design, optimize, and deploy your edge AI

Latent AI Senior ML Engineer Sarita Hedaya recently discussed the challenges and solutions surrounding edge AI implementations. AI developers, data scientists and ML engineers can spend months trying to find the optimal combination of model and device for their data. Our solutions let users skip the research and start training on their data in minutes, … 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

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

Latent AI and the Atlantic Council

How a New AI Edge Continuum Architecture Can Better Enable JADC2 Objectives Latent AI recently held a series of sessions with the Atlantic Council to explore the best ways to support Joint All-Domain Command and Control (JADC2) strategy objectives with AI. We are proud to say the results of those sessions have just been published as … 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

Closing the Gap Between Edge AI Expertise and Implementation

Latent AI CEO Jags Kandasamy was recently interviewed by cybernews magazine about the state of edge AI and where the industry is headed. In the interview, Jags talks about why edge AI is the inevitable future and the rewards enterprises can gain when they get it right. Unfortunately, most are still struggling to reap the rewards edge … Continued

Latent AI Named IoT Emerging Company of the Year for the Enterprise Market

Latent AI was recently honored as the IoT Emerging Company of the Year for the Enterprise Market during the 10th Annual Compass Intelligence Awards. Other IoT category winners included technology stalwarts like Verizon, Samsung, and Palo Alto Networks, so we are doubly pleased to be included on such a distinguished list. Current AI is far too … Continued