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 the Tempo SDK, 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.