In the rapidly-transforming digital world, we see a lot of parallels between AI computing and Formula One racing. Speed, accuracy, and agility are all crucial for AI success, much like they are for Formula One racing. In this post, we’ll explain some of the challenges in creating production-grade AI models and explain how you can professionally measure your AI model’s performance and reach maximum performance.
The problem: perfection is hard to achieve
It’s vital that your enterprise has the ability to deploy powerful, fast, AI models so you can react faster, make better business decisions, predict customer behavior, and stay ahead of market trends. Your AI models process vast amounts of data every day, producing knowledgeable insights and valuable predictions that form the foundation of your business success.
Mario Andretti, the legendary Formula One driver, once said: “If everything seems under control, you’re just not going fast enough.” It’s a compelling message, but it obscures the fact that every car he’s ever driven benefited from millions of dollars of development and testing. Racing car manufacturers invest time and money to ensure that every component of the car is in perfect condition, and works in harmony with every other component–producing an integrated system that operates smoothly at peak performance.
If you were designing an engine for the next season’s Formula One car, you’d most certainly test it in a number of different environments. You’d apply an iterative process of build, test, measure, learn, and build again, taking note of the unpredictable results that guide you to keep improving your engine. You know that every component either improves the performance of the car as a whole, or drags performance down. There’s no such thing as a neutral item that has no impact. For this reason, you analyze and check every part before moving to production.
The same is true for AI models and, more specifically, deep learning models. Although you want to keep pushing the boundaries of speed and agility, you also need each element to work in sync to deliver optimized performance, including highly accurate results.
Deploying AI models to production is not a simple process. Just like designing a Formula One racing car, you take time to test every element of your AI model, ensuring that you fully understand the ways it will interact with and impact the environment as a whole. You consider production costs too, and the need to provide consistent and clear benchmarks for managers.
Finally, your AI models and datasets are, by definition, elastic and dynamic. Any measurement that you make will never be a one-off; you need to continuously track performance and cost to ensure that your models remain accurate and cost-effective.
The solution: test your models before inference
Deci offers a solution to help you test, track, and understand the different elements of your AI pipeline in order to optimize performance across the board (this service is not available to Formula One car designers). The Deci Inference Performance Simulator (DIPS), a free service based on our robust technology platform, enables you to easily analyze any deep learning model in diverse environments, at the click of a button.
DIPS allows you to measure throughput, model latency, cloud cost, model memory usage, and other performance metrics, creating a standard way to analyze inference performance. We compare these metrics across multiple hardware types, currently supporting four different AWS instances and multiple frameworks, including PyTorch, TensorFlow and ONNX, with more to come.
Model inference performance is a vital factor in the productization of your deep learning models. With a sharper understanding of your AI model metrics, you can develop better models and create enhanced products and services, giving you an edge over those competing against you in the race.
The benefits of using DIPS
There’s no reason not to use DIPS, and every reason why you should. For starters, the process is free. There’s absolutely no risk or cost involved. It takes just 2 minutes for you to enter the information we need to assess your model performance metrics and create a report that reveals exactly how well your models are reaching their potential. Our report is generated automatically and sent to you through secure email within 24 hours.
Benchmark your models
Our comprehensive, inference performance report analyzes various aspects of your models in a number of environments–for example different hardware, different versions of software, etc. Saving the results of previous DIPS reports lets you compare performance across different hardware and cloud platforms. This imparts valuable benchmarks that help you make informed decisions about the best model and hardware for your product. With firm data about performance, you can communicate your hardware needs and justify model choices in a way that is clear and concise.
DIPS also helps you identify any bottlenecks in your model performance, revealing actionable insights and recommendations for both developers and AI executives.
What’s more, DIPS keeps all your model information safe and secure. We don’t need access to your model weights, so you can send random or obfuscated data and still receive the same eye-opening visibility into model inference performance. We never keep a copy of your model, and never share it with anyone else.
Feeling insecure? Try DIPS with an off-the-shelf model
While DIPS keeps whatever you submit completely secure, and your weights and data are not needed, perhaps you just want to “dip into” DIPS without sharing your models? If that’s the case for you, we offer the ability to use an off-the-shelf model, such as Resnet, EfficientNet and others, to try DIPS out.
Try DIPS today. Your newly blazing-fast models will thank you for giving them the speed, accuracy, and agility of a Formula One racecar.