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Head of b.telligent Austria | Data Science and AI Transformation Expert | AI Pragmatist | Speaker
Great news on organizations quickly becoming more efficient with putting machine learning models into production: Based on the ratio of (the number of) logged ML experiments to actually deployed/registered models, the average efficiency increased by a factor of 3 in only 14 months! The data for this evaluation stems from MLflow (as open source MLOps platform). And looking at the results separately, the number of registered models even increased by a factor of 10 (!) within a single year. The number of companies registering at least one model was up by 210%! All in all, great data to show that the GenAI wave also accelerates ML production adoption in general! Thanks Databricks for the insightful report! #mlflow #ml #ai
Our data shows that organizations have become 3x more efficient at putting ML models into production in just 14 months. They're spending fewer resources on experimental models that never provide real-world value. We break down these trends and more in the State of Data + AI, which examines usage data from our 10,000+ global customers. https://dbricks.co/3KB4OIK
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Interesting post to review for any Data Professionals that are responsible for building and putting ML models into production! Our data shows that organizations have become 3x more efficient at putting ML models into production in just 14 months. They're spending fewer resources on experimental models that never provide real-world value. We break down these trends and more in the State of Data + AI, which examines usage data from our 10,000+ global customers. https://dbricks.co/3KB4OIK Activate to view larger image,
Our data shows that organizations have become 3x more efficient at putting ML models into production in just 14 months. They're spending fewer resources on experimental models that never provide real-world value. We break down these trends and more in the State of Data + AI, which examines usage data from our 10,000+ global customers. https://dbricks.co/3KB4OIK
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This is interesting stats thinking back to when studies pinned 80% of ML projects going into the graveyard for lack of deployment. Nowadays MLOps is a word, treating models as software services is the norm, and executive support is present by default. While excited to see the continued growth of the AI/ML space in industry... Similarly dreading the trough of disillusionment that's bound to come with the Gen AI craze of late. It'll not help when the investment blows away and the prudent ML practitioners gets dragged down by the business-driven cycle. Disclaimer: Gen AI is very powerful and useful albeit niche. 2018 feels really recent but all i could give: https://lnkd.in/ek4f5Xtf
Our data shows that organizations have become 3x more efficient at putting ML models into production in just 14 months. They're spending fewer resources on experimental models that never provide real-world value. We break down these trends and more in the State of Data + AI, which examines usage data from our 10,000+ global customers. https://dbricks.co/3KB4OIK
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