MLOps community meetup #3! Last Wednesday we talked to Phil Winder, CEO, Winder Research. //Abstract Phil Winder of Winder Research joined us for the 3rd instalment of our MLOps community meetup. In this clip taken from the long conversation, he speaks about why or why not he sees companies automating the retraining of Machine Learning Models. You can find the whole conversation here: https://www.youtube.com/watch?v=MRES5IxVnME   The topic of conversation for our virtual meetup was an in-depth look at a pyramid of software engineering best practices that built up to incorporate data science best practices. That is to say, we analyzed “the essentials”, "nice to have" and "optimal" ways of doing data science.  Machine Learning/Data Science/AI is an extension of the technical stack. So you can't really talk about Data science best practices without accidentally talking about software engineering best practices. For example, model provenance doesn't count for anything if you don't have code or container provenance.  Just as Maslow has the basic human needs so too do we have basic MLOps needs. Where does "MLOps", as a "thing", starts and end? For example, the four very reasonable best practices of the operation of models, but these are usually consumed into higher-level abstractions because there is a lot more to do than "just" provenance.   //Bio Dr Phil Winder is a multidisciplinary software engineer and data scientist. As the CEO of Winder Research, a Cloud-Native data science consultancy, he helps startups and enterprises improve their data-based processes, platforms, and products. Phil specializes in implementing production-grade cloud-native machine learning and was an early champion of the MLOps movement. More recently, Phil has authored a book on Reinforcement Learning (RL) (https://rl-book.com) which provides an in-depth introduction of industrial RL to engineers.  He has thrilled thousands of engineers with his data science training courses in public, private, and on the O’Reilly online learning platform. Phil’s courses focus on using data science in industry and cover a wide range of hot yet practical topics, from cleaning data to deep reinforcement learning. He is a regular speaker and is active in the data science community.  Phil holds a PhD and M.Eng. in electronic engineering from the University of Hull and lives in Yorkshire, U.K., with his brewing equipment and family. //This was a virtual fireside chat between Phil Winder and Demetrios Brinkmann. relevant links can be found below: Join our MLOps slack community: https://bit.ly/3aOTwgR   Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/   Connect with Phil on LinkedIn: https://www.linkedin.com/in/drphilwinder/ Follow Phil on Twitter: https://twitter.com/DrPhilWinder  Learn more about Phil's company Winder research: https://winderresearch.com/

MLOps community meetup #2! Last Wednesday we talked to Charles Radclyffe, Technology Governance and ESG Specialist, AI Ethics. 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 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. Please feel free to join our slack channel or forum to chat more about MLOps. Link to MIT Techlash blog Charles wrote. Join our open community where we discuss everything MLOps: https://mlops.community/ Join our MLOps slack channel: https://bit.ly/33wDUf1 MLOps.community forums: https://forum.mlops.community/ Sign up for the next weekly meetup: https://zoom.us/webinar/register/WN_a_nuYR1xT86TGIB2wp9B1g

The 1st MLOps.community meetup on 3.18.2020 featuring Luke Marsden from Dotscience. 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. Join our open community where we discuss everything MLOps: https://mlops.community/ Join our MLOps slack channel: https://bit.ly/33wDUf1 MLOps.community forums: https://forum.mlops.community/ Sign up for the next weekly meetup: https://zoom.us/webinar/register/WN_a_nuYR1xT86TGIB2wp9B1g