Data & AI Expo, highlights of Day One

Day one focused mainly around successfully implementing AI. Responsible AI (Trustworthy & Ethical) were topics mentioned during the presentations and discussions. Here are some highlights and learnings from three key sessions:

AI Strategy manual for CTOs - A corporate journey to production

• Mistakes often made when going to production

  • Overestimating the applicability of off the shelf solutions

  • Underestimating computing cost

  • Rushing from prototyping to production -> build a full stack team and define complete software specifications upfront

  • Underestimating the importance of change management -> design and implement re-skilling strategies for your workforce

High level steps:

• Opportunity mapping

• Tech assessment

• Prototyping: set KPIs

• Go-to-production strategy: build P&L of the project

• Product development

• Adoption, maintenance and improvements

Panel Discussion: Getting to Production-Ready: Challenges and Best Practices for Deploying AI

• Define carefully the use case

• Understand the data

• Understand how to move from development to production environment

• Align expectation across stakeholders and ensure buy-in

• Understand how the model becomes part of the larger ecosystem

• Plan for data acquisition -> data curation -> data guardrails

  • Models fail if guardrails not right

  • Test and try to break the model

• Avoiding bias

  • Responsibility starts at the collection of data and understanding the sources

    • Ensure data is up to date and keep it up to date

  • Data leaks and privacy are still an issue

• Explainability

  • Data needs to be timely = only possible with automation and orchestration starting at pre-production

• Ensure that data is sovereign (research shows 65% of data is managed onprem but 90% of organisation don’t have the infrastructure to run it)

Putting Structure into Chaos - How to develop trustworthy AI-Powered workflows

The key to successfully deploying AI is evaluating the AI model = does it fit my purpose?

Classification is easy to assess vs GenAI is more difficult

• Sampling is the most misunderstood  method

• Ask what is the confidence interval of the sample size?

• How much is enough?

It is easier to evaluate the workflow 

• Back propagating from customer journey

• Get advice from someone who has built the workflow for your industry