In this episode of MLOps Live, Sabine and Stephen are joined by Kyle Morris, Co-Founder of Banana ML. They discuss running ML in production leveraging GPUs. They delve into GPU performance optimization, approaches, infrastructural and memory implications as well as other cases. With the increased interest in building production-ready, end-to-end ML pipelines, there’s an increasing need to employ the optimal toolset, which can scale quicker. Modern commodity PCs have a multi-core CPU and at least one GPU, resulting in a low-cost, easily accessible heterogeneous environment for high-performance computing, but due to physical constraints, hardware development now results in greater parallelism rather than improved performance for sequential algorithms. Machine Learning Build/Train and Production Execution frequently employ disparate controls, management, run time platforms, and sometimes languages. As a result, understanding the hardware on which one is running is critical in order to take advantage of any optimization that is feasible.

Today, we’re joined by Jacopo Tagliabue, Director of A.I. at Coveo. He currently combines product thinking and research-like curiosity to build better data-driven systems at scale. They examine how immature data pipelines are impeding a substantial part of industry practitioners from profiting from the latest ML research. People from super-advanced, hyperscale companies come up with the majority of ideas for machine learning best practices and tools, examples are Big Tech companies like Google, Uber, and Airbnb, with sophisticated ML infrastructure to handle their petabytes of data. However, 98% of businesses aren't using machine learning in production at hyperscale but rather on a smaller (reasonable) scale. Jacopo discusses how businesses may get started with machine learning at a modest size. Most of these organizations are early adopters of machine learning, and with their good sized proprietary datasets they can also reap the benefits of ML without requiring all of the super-advanced hyper-real-time infrastructure.

Today, we’re joined by Kuba Cieslik, CEO and Ai Engineer at tuul.ai. He has experience in building ML products and solutions and has a deep understanding of how to build visual search solutions. Visual search technology has been around for quite some time, as part of Google Pictures or Pinterest Lens. It has become increasingly popular in e-commerce, allowing customers to simply upload what they're looking for instead of going through a slew of attribute filters. Kuba discusses how one might go about creating such a visual search engine from the ground up, as well as what approaches work and the challenges in such a complex sector.

MLOps Live is a biweekly Q&A show where practitioners doing ML at a reasonable scale answer questions from other ML practitioners. Every episode focused on one specific subject related to MLOps. Only the juicy bits, the things you won’t find in a company blog post.