#199 Leaders Exchange: Productionalising ML across the Enterprise: What it Takes to Get this Right!

For the most innovative and forward-thinking organisations, the next frontier forward for data is focused on machine learning, and specifically the role that MLOps plays in driving outcomes. Questions that data leaders need to be asking themselves now include: What steps do organisations need to take to deliver ML maturity, how can they take the leap from experimentation to production, and how ML teams be effectively organised and motivated around this goal? To dive deeply into this critical discussion, Data Futurology recently brought together a panel of some of the leaders in ML strategy and execution. Each of these companies have been successful in productionalising ML across their enterprises, and discuss their strategies and successes in an open and free-flowing discussion: Agustinus Nalwan, Head of AI and Machine Learning, carsales.com.au Farhan Baluch, Principal Data Scientist, Apple (USA) Kendra Vant, Executive GM Data, ML & AI, Xero Ram Radhakrishnan, General Manager Customer Analytics, AI & Data Science at Woolworths Group These four experts also highlight just how important it is to motivate teams around an ongoing process of learning and discuss how to deliver a dynamic understanding of the changing role of data across the organisation. Whether the data team is inwardly-looking, or focused on customer outcomes, emerging concepts such as “software 2.0” – as mentioned in the webinar – will continue to throw curveballs that MLOps teams will need to have the agility to adapt to and capitalise on. Ahead of the Scaling AI with MLOps event to be held in Melbourne on October 25, this webinar is a unique opportunity to gain insight from those at the very bleeding edge of data innovation. Enjoy the show! Thank you to you our sponsor, Talent Insights Group! Join us for one of our upcoming events: https://www.datafuturology.com/events Join our Slack Community: https://hubs.li/Q01gKNBn0 Read the full episode summary here.

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