See how LEIP enables you to design, optimize and deploy AI to a variety of edge devices–at scale. Schedule a Demo
See how LEIP enables you to design, optimize and deploy AI to a variety of edge devices–at scale. Schedule a Demo
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 tools embedded with ML expertise, we empower your team to work more efficiently.
The lifecycle of machine learning models from the initial data collection stage to the final deployment isn’t as straightforward as it may seem. Each project comes with its unique set of constraints and requirements, like data limitations, hardware capabilities, power consumption, latency, and project timelines.
In a traditional workflow, teams often operate in a linear sequence. They go through each stage, one after the other. While this might sound logical, it frequently results in communication barriers and roadblocks in decision-making. There is no common tooling to help break down silos.
An all too common pitfall is the story of a model that seems like a superstar in the lab but flops when it faces the real world. Why? Unexpected hardware constraints, the kind that demand costly revisions. Gartner’s 2022 study states that 85% of AI projects fail to deploy. Some of that, we suspect, are due to these challenges.
We developed LEIP Recipes to solve these challenges and more. LEIP Recipes provide a structured and semantically meaningful way to design ML systems, bridging the gap between different stages of the pipeline. Recipes are modular, composable, shareable, and easy to reproduce. The true beauty of LEIP Recipes is that they eliminate the need to rush into design choices, allowing teams to focus on what truly matters: modeling and exploration.
LEIP Recipes are a game-changer for different roles in the ML world, including for:
LEIP Recipes revolutionize the way we explore the design space. Instead of making hurried choices, teams can now rely on well-qualified starting points, backed by empirical data and historical project insights. This shift allows for more informed decisions, especially when performance trade-offs are essential. Whether it’s achieving faster inference, training models with limited data, or optimizing for specific hardware, LEIP Recipes provide a vast array of possibilities.
The webinar unveiled the Recipe Miner, an engine that samples the recipe space and collects empirical performance metrics. It’s a treasure trove of information, giving ML practitioners insights into training time, inference speed, and resource consumption. It’s like having a data-driven compass for selecting the best-fitting recipes.
The Recipe Designer API simplifies the process of exploring the recipe space. It allows for programmatic model design, making it easy to iterate through different model families and backbones. ML practitioners can efficiently experiment with countless combinations and find optimal solutions tailored to their specific constraints and objectives.
In the world of ML, collaboration and reproducibility are gold. Recipes, compact in size, can be easily shared, stored, and executed with only the LEIP SDK and Application Framework required. This collaborative approach streamlines the development process, while historical data and correlations across projects offer invaluable insights over time.
LEIP Recipes open doors to a new era in machine learning model development. With a structured, semantically meaningful approach, they empower teams to explore design alternatives, make data-driven decisions, and dramatically accelerate ML projects. When we trained a recipe using the widely popular YoloV5 large model on the COCO dataset and optimized it for the Nvidia AGX, we showed more than a 3x improvement in inference speed when compared to a Pytorch baseline for a Nvidia Xavier AGX processor. Simultaneously, we observed an 84% reduction in model size while only seeing a slight accuracy drop below 3%.
Whether you’re an ML novice or an expert, LEIP Recipes have the potential to transform your approach to model development and usher in a new era of efficiency and high performance in the field of machine learning. LEIP Recipes enable you to swiftly generate models tailored for various hardware targets and diverse data sources, eliminating the need to start from scratch with each new project. We provide the means to deliver optimized and secured models at scale, ensuring the success and reliability of your AI initiatives.
To learn more, click here to watch our webinar on how to “Simplify your Model Design, Exploration and Delivery”.
Stay tuned for Parts 2 and 3 of our ML Model Design series…
Part 2: Make Data-Driven Design Decisions – Dive into the LEIP Recipe Designer API, an innovative tool that simplifies the ML model design process and enables you to find your best model faster.
Part 3: Mining for Success – Explore the cutting-edge capabilities of the Recipe Miner, your data-driven design companion. By analyzing training times, inference speeds, and resource consumption, it provides invaluable insights that empower your design decisions.
Contact info@latentai.com to supercharge your AI development and bring your projects to life with greater ease and speed.
