Session Outline Since Data mesh started to get discussed, many companies have started to adopt it. In Adevinta, we have embraced the data products platform as the way to go, to ensure that Data products are being built...
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Iterate on Data in Production – Safely – Micha Ben Achim Kunze, Maersk
Session Outline Production data is all you need. We will show how we build and iterate on data and ML products in our production environment. Using the established practice of feature flags in our setup we will try to...
ML Training in Production at Meta – Shivam Bharuka, Meta
Session Outline Machine learning models are growing rapidly in scale in order to support the recommendation and content understanding use-cases at Meta. In order to keep up with this growth, we have re-architected the...
Where to start after you have a great ML model – MLOps Battlefiled stories – Oswaldo Gomez, Roche
Session Outline In this presentation, I will be sharing my individual contributor perspective of extracting value from AI, after working as a MLOps Engineer in Financial and Biotech industry. Key Takeaways – Where...
Computer Vision at Shell – Siva Chamarti, Chaitanya Katukuri & Natarajan Kalidos, Shell
Session Outline This presentation presents some of the challenges in developing Computer vision use cases deploying them on Edge and Cloud. How we, at Shell, accelerated development of computer vision projects by...
Journey from manual towards automated car damage assessment – Vladimir Tripkovic, Topdanmark
Session Outline We would like to share our experiences, thoughts, and results on partial automation of car damage reports. The presentation will describe our journey in the past, present and the future regarding...
Full life cycle of NLP models to get insights from the employee feedbacks continuously – Busra Cikla, ING
Session Outline Natural Language Processing (NLP), an ever-growing interest area in the analytics community, creates big impacts on several domains including the HR domain. It enables transformation of unstructured data...
Our model stopped working! Now what? A credit card fraud multi-persona MLOPs story – Iulia Feroli, Dataiku
Session Outline There are many approaches and strategies for MLOPs in data science departments, each with their own focus, benefits, or pain points. From standardizing practices and way of work, to automating the boring...
