Identifying the right tools to enable for high performance machine learning may be overwhelming as the ecosystem continues to grow at break-neck speed. This becomes particularly emphasised when dealing with the ever growingly popular large language and image generation models such as GPT2, OTP and DALL-E, between others. In this session we will dive into a practical showcase where we will be productionising the large image generation model DALL-E, and showcase some optimizations that can be introduced as well as considerations as the use-cases scale. By the end of this session practitioners will be able to run their own DALL-E powered applications as well as integrate these with functionalities from other large language models like GPT2, etc. We will be leveraging key tools in the Python ecosystem to achieve this, including Pytorch, HuggingFace, FastAPI and MLServer.
Identifying the right tools to enable for high performance machine learning may be overwhelming as the ecosystem continues to grow at break-neck speed. This becomes particularly emphasised when dealing with the ever growingly popular large language and image generation models such as GPT2, OTP and DALL-E, between others. In this session we will dive into a practical showcase where we will be productionising the large image generation model DALL-E, and showcase some optimizations that can be introduced as well as considerations as the use-cases scale. By the end of this session practitioners will be able to run their own DALL-E powered applications as well as integrate these with functionalities from other large language models like GPT2, etc. We will be leveraging key tools in the Python ecosystem to achieve this, including Pytorch, HuggingFace, FastAPI and MLServer.
Speakers: Alejandro Saucedo