Agentic Conversations (formally mlops.community)

Latest Episodes:

What Does Best in Class AI/ML Governance Look Like in Financial Services? // Charles Radclyffe // MLOps Meetup #2 Mar 26, 2020

Show notes

MLOps community meetup #2! Last Wednesday, we talked to Charles Radclyffe, Technology Governance and ESG Specialist, AI Ethics.

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What does best-in-class AI/ML governance look like in financial services?

For this episode, we are joined by Charles Radclyffe, who until very recently was the Head of AI at Fidelity. Some of his other feats include starting 3 companies, TEDx talks, and advising the likes of HSBC, Barclays, Morgan & Stanley, and Deutsche Bank. He has focused his career on solving tough technology challenges for some of the world's largest organizations. For more on him, follow him on Twitter or connect on LinkedIn

Governance is coming for us all, but it’s especially pertinent in regulated industries such as the finance sector. Financial institutions must be mindful of how their machine learning models are being used and experimented with, as regulators are keen to understand the quality of controls across the industry.

Our conversation is centered around Charles’ past experiences heading up the AI capability for a large organization in the financial industry, and his learnings during that time. We will also touch on what ideal AI/ML governance looks like in his eyes and where he sees we need to focus our attention for future success within this area. What do data scientists and ML engineers need to learn about governance to ensure business success as laws are continually changing?

This episode is a virtual fireside chat for the first 40 minutes, and in the last 20 minutes, we open up the floor to any questions.

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Link to the MIT Techlash blog Charles wrote: https://insights.techreview.com/to-end-the-techlash-ai-ethics-debate-needs-to-shift/

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Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1 Mar 23, 2020

Show notes

The 1st MLOps.community meetup on 3.18.2020 featuring Luke Marsden from Dotscience.

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What is MLOps, and how can it help me work remotely? The first episode of our weekly MLOps community virtual meetup with CEO and founder of the MLOps platform dotscience Luke Marsden talk to us about the current state of Machine Learning, what some of the main difficulties are at this stage when developing models, how the machine learning lifecycle differs from traditional software development and a deep dive of collaboration for data science teams in a fully remote world.

MLOps is the intersection of three disciplines: software engineering, DevOps, and machine learning. MLOps refers to the entire end-to-end lifecycle of getting models from lab to live, where they can start delivering value.

What do software engineers and DevOps need to learn about machine learning to ensure that it can be integrated into their dev & deployment pipelines? What do data scientists and ML engineers need to learn about DevOps, model deployment, and monitoring to ensure they can effectively deploy their work without racking up tonnes of technical debt? And now that working from home is fast becoming the new normal, how can MLOps help my team stay efficient when asynchronous collaboration is needed, something our software engineering and DevOps friends have already mastered?

MLOps is a complex discipline due to the many more moving parts involved than regular software DevOps. In this inaugural MLOps.community meetup, we'll explore and navigate this new space together and give you a guide on how to avoid the most common pitfalls and challenges getting AI into production and collaborating effectively with your team – even when you're distributed.

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