The launch of Seldon Deploy 1.0 was packed with interesting insights from MLOps experts who shared best practises and predictions for the coming year. This new product is the result of years of hard work from our team towards a shared mission to accelerate the adoption of machine learning to solve some of the world’s most challenging problems. At this milestone, we were excited to hear our users and customers share stories of the real-life value and use cases that can be achieved with open-source MLOps and their perspective on industry trends.
“It’s quite an exciting journey we’re on….within AstraZeneca that we’re bringing all these technologies together to look at how we can make drug discovery faster and getting drugs to patients quicker.”
Adrian Rossall, AstraZeneca
MLOps plays a key part in AstraZeneca’s mission to reduce the research phase of the drug discovery cycle by half, from 24 months to 12 month, by 2025. Powering such industry-changing ambition demonstrates the power and potential our platform brings to machine learning leaders and innovators like Adrian Rossall, Head of Augmented Drug Discovery. We’re also proud to hear that Seldon is a “perfect fit for what we’re trying to achieve at H&M for a range of use cases, such as serving high-level SLAs to hm.com according to Errol Koolmeister, who noted that “it’s the best technology we’ve come across so far to be able to live up to our high standards”.
“We have a saying: ‘We don’t care how good your model is, if it’s not in production and delivering value then it’s not worth anything’.”
Errol Koolmeister, H&M
In discussing what the MLOps landscape will look like over the next year, explainability came up as a key challenge. In his role as a lawyer in the AI space, moderator John Buyers warned that “explainability is a huge, looming regulatory issue that is going to be coming down the pipe”. Sophie Watson, Principal Data Scientist from Red Hat emphasised that her team has “been focusing on and making the most of is the Seldon Alibi library – this provides explainability and interpretability to those black box machine learning models…it makes it so simple to get those insights quickly”.
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