5 Best Practices for Efficient Model Training
In the course of our research and product development we’ve codified a number of best practices for efficient CNN training, and we’d like to share some of them with you here.
Why Enterprises Should Treat AI Models Like Critical IP (Part 2)
In 2022, the potential of Large Language Models (LLM) and Generative AI entered the mainstream, while organizations began to recognize the value of state-of-the-art AI models to activate their data. In part 2 of this blog, I explore why enterprises should treat AI models as some of their most important intellectual property in 2023 and beyond.
Why Enterprises Should Treat AI Models Like Critical IP (Part 1)
Five years ago, The Economist proclaimed that data was the new oil. Since then, the power of amassed data to impact the world has become even more undeniable. That’s why companies should treat AI models as some of their most important intellectual property, rather than setting them aside as something with the potential for future impact. Today’s large, state-of-the-art AI models can be viewed as a powerful tool to activate an organization's data - and maximize its value.
Efficiently Estimating Pareto Frontiers with Cyclic Learning Rate Schedules
Benchmarking the tradeoff between model accuracy and training time is computationally expensive. Cyclic learning rate schedules can construct a tradeoff curve in a single training run. These cyclic tradeoff curves can be used to evaluate the effects of algorithmic choices on network training efficiency.
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