He spends time on the podcast this week talking about what that means for the department, and the implications it has for recruitment more broadly. This week on the Data Futurology podcast, we have the special privilege of talking to the top analytics leader in Australia, according to IAPA (the Institute of Analytics Professionals of Australia).īrad Petry, the Executive Director – Operations, Insights, and Digital Channels at the Department of Jobs, Precincts and Regions in the Victorian Government, was awarded this accolade for his work in leveraging AI and machine learning to overcome biases in the recruitment process.
#SPOTIFY CAREERS SENIOR DATA ENGINEER FULL#
Read the full podcast episode summary here.
Join us in Melbourne for Scaling AI with MLOPS: If you want to learn more about building modern applications on AWS and attend a virtual conference, just google “AWS Innovate” or click the link below. This allows any developer to be a citizen data scientist and ML engineer by leveraging Amazon SageMaker. You'll gain insights into how AWS customer Carsales keeps up with increased demand in building and productionising AI models, and their strategy to democratise AI across the whole development teams. Join Romina to find out how you can easily accelerate your MLOps journey using Amazon SageMaker Pipelines. Operationalising machine learning models, particularly scaling MLOps capability across teams within an organisation is a difficult feat. In today’s episode, we have Romina Sharifpour, Machine Learning Specialist at Amazon Web Services (AWS).
Thank you to our sponsor, Talent Insights Group! For more on how Aukea was able to structure Volvo for success with machine learning in operations, tune in to the podcast! There are, of course, technical challenges, but for anyone aiming to get true value out of machine learning, the focus needs to be on the processes.Īukea then explains how, with those processes in place, he and his team have been able to start delivering deep and valuable insights. Ultimately, Aukea said, quality comes from the processes, rather than the technology. “We evangelised these first principles to develop good ways of working, and then adopted the infrastructure and tooling towards building AI on top of that.” “We went back and decided to focus on first principles,” he said. You don’t need to choose the most extreme state-of-the-art tools.”Īt one point, Aukea noted, things being pushed into production were becoming unmanageable, so he and his teams took a step back and reset. “Think about processes, ways of working, and the cultural aspects, and try to fit tooling and infrastructure along that kind of endeavour. This might not be immediately comfortable to them, but as Aukea said, ML experts are smart people working on complex problems, and facilitating an open-minded approach across the organisation is key to driving long-term success.
#SPOTIFY CAREERS SENIOR DATA ENGINEER SOFTWARE#
He said that one of his first priorities was to bring the various data science teams together to minimise the impact of siloing, and encourage the machine learning practitioners to adopt software engineering principles. This week’s guest on the Data Futurology podcast is Leonard Aukea, the Head of Machine Learning, Engineering & Operations at Volvo, who shares with us insights into what the global vehicle giant is doing to bring value to the operations chain across the company.įor Aukea, it has been a story of establishing best processes across the organisation. The motor industry has always been right at the forefront of innovation, and this is also true when it comes to embracing machine learning and AI.
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