Databricks’ Post

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

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

I love this chart for two reasons. First, it shows how serious Manufacturers and Retailers - both process driven industries - have become about embedding ML into their business. ML is mainstream. Second, even industries that are less process driven and more experimental such as Financial Services (evaluating companies or investment hypotheses) and Health and Life Sciences (drug discovery) have refined their approaches. The initial wave of Machine Learning is over and companies are extracting real value.

How can brands stay competitive in an AI world? Hint: it's all about the data. Love this latest article from Zappi's Steve Phillips in WARC. He delves into the true impact of Generative AI for brands, and how your ability to leverage AI is, ultimately, dependent on the state of your data. 👉 Check it out: https://lnkd.in/ePu69qTX

Dive into the world of AI and decision-making with our latest blog post on Markov Decision Processes (MDPs) – your ultimate guide to understanding how to make optimal choices in a world filled with uncertainty and adventure! 🤖🌟 https://hubs.li/Q02hZGNf0 https://hubs.li/Q02hZGNf0

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Data is the most important part of GEnerative AI. These models are trained on massive data sets. The quality of the data also matters in terms of removing objectionable content for example. Most of the Gen AI companies have some protocols on this. Check out my Part 3 of the "Understanding Gen AI" series of videos which explains in simple terms about the type of data used by these models and how it is curated. If you are interested in more such videos, please subscribe to my youtube channel AIeconomics ( https://lnkd.in/dpnsukQC) #LLMs #generativeAI https://lnkd.in/g6_6gteQ

There's been a ton of buzz around generative AI and some of its potential uses. But how can you leverage it to generate enterprise-grade synthetic data? And what are some of the challenges to look out for? Learn about it here 👉 https://lnkd.in/d5ZggvsY #GenerativeAI #SyntheticData #K2view

Dive into the world of AI and decision-making with our latest blog post on Markov Decision Processes (MDPs) – your ultimate guide to understanding how to make optimal choices in a world filled with uncertainty and adventure! 🤖🌟 https://hubs.li/Q02hZz9Q0 https://hubs.li/Q02hZz9Q0

In this video, I am going to show you how I am using custom AI technology to completely dominate my competition. With the power of AI, I have developed a follow-up strategy that leaves my competitors in the dust.