06-19, 13:30–14:15 (Europe/London), Tower Suite 1
Identifying the right tools for high performance production machine learning may be overwhelming as the ecosystem continues to grow at break-neck speed. In this session showcase how practitioners can productionise ML models in scalable ecosystems in an optimizable way without having to deal with the underlying infrastructure challenges. We will be taking a GPT-2 HuggingFace model, optimizing it with ONNX and deploying to MLServer at scale using Seldon.
Identifying the right tools for high performant production machine learning may be overwhelming as the ecosystem continues to grow at break-neck speed. In this session we aim to provide a hands-on guide on how practitioners can productionise optimized machine learning models in scalable ecosystems using production-ready open source tools & frameworks.
We will dive into a practical use-case, deploying the renowned GPT-2 NLP machine learning model using MLServer and Seldon Core, which allows data scientists to productionise ML models without having to deal with the complexity of the underlying infrastructure - abstracting the complexity of the underlying model servers and runtime (Docker and Kubernetes) environments & frameworks.
We will showcase the foundational concepts and best practices to consider when leveraging production machine learning inference at scale. We will present some of the key challenges currently being faced in the MLOps space, as well as how each of the tools in the stack interoperate throughout the production machine learning lifecycle. Namely, we will introduce the benefits that the ONNX Open Standard and Runtime brings, as well as how we are able to leverage the optimized triton server and the orchestration framework Seldon Core to achieve a robust production machine learning deployment that can scale to your growing team / organisational needs.
By the end of this talk, attendees will have a better understanding of how they will be able to leverage these tools for their own models, as well as for the broad range of pre-trained models available. We will also provide a broad range of links and resources that will allow attendees do dive deeper into detailed areas, such as observability, scalability, governance, etc.
No previous knowledge expected
Alejandro Saucedo is Director of Engineering at Seldon Technologies, where he leads teams of machine learning engineers focused on the scalability and extensibility of machine learning deployment and monitoring products. Alejandro is also the Chief Scientist at the Institute for Ethical AI & Machine Learning, where he contributes to policy and industry standards on the responsible design, development and operation of AI, including the fields of explainability, GPU acceleration, ML security and other key machine learning research areas. With over 10 years of software development experience, Alejandro has held technical leadership positions across hyper-growth scale-ups and has a strong track record building cross-functional teams of software engineers. He is currently appointed as governing council Member-at-Large at the Association for Computing Machinery, and is currently the Chairperson of the GPU Acceleration Kompute Committee at the Linux Foundation.