Accelerating AI Deployments

Artificial intelligence, machine learning, and deep learning (AI/ML/DL) have rapidly become critical for businesses and organisations with adoption of generative AI (gen AI) increasing rapidly. Establishing Artificial Intelligence / Machine Learning operational consistency across teams with a consistent user experience that empowers data scientists, data engineers, and DevOps teams to collaborate effectively is however complicated. 

Scaling AI/ML deployments can be resource-limited and administratively complex while requiring expensive graphics processing unit (GPU)-resources for hardware acceleration and distributed workloads for gen AI. Popular cloud platforms offer scalability and attractive toolsets, but those same tools often lock users in, limiting architectural and deployment choices.

Key characteristics of a future proof AI toolset and platform that people commonly refer to are amongst others: 

- Avoid lock-in from public cloud-specific tools

- Ability to extend the base platform with partner tools to gain increased capabilities in this rapidly evolving space

- Ability to serve models to production environments in a container-ready format, consistently, across hybridc cloud and edge environments

- Simplified IT operations

- Support for popular gen AI foundation models, that let you prompt-tune, fine tune, and serve these pretrained models for your unique use cases and with your own data

- Ability to exploit AI hardware acceleration, without the need to stand up and manage your own data science platform

However, how important are each of these for your organisation? During this CIONET Round Table, we will talk about these key characteristics and about how to put in place an AI toolset and platform that allows you to rapidly deliver intelligent applications on premise, on the edge or in the public cloud.