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Why large enterprises struggle to find suitable platforms for MLops

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Summary

This article discusses the difficulties that large enterprises face when trying to find suitable platforms for MLops (machine learning operations). MLops is a relatively new field, and most platforms are not yet equipped to handle the large number of models that these enterprises require. This article quotes several ML experts who discuss the various problems they have encountered with existing MLops platforms.

Q&As

What do ML practitioners say about prepackaged MLops systems?
ML practitioners say that they have yet to find what they need from prepackaged MLops systems.

What do companies need from a MLops system?
Companies need a system that can handle the large number of models they deploy.

Why do larger enterprises struggle to find suitable platforms for MLops?
Larger enterprises struggle to find suitable platforms for MLops because they need a system that can handle the large number of models they deploy.

How many models do larger enterprises deploy?
Larger enterprises deploy thousands and even millions of models.

What is the goal of MLops?
The goal of MLops is to manage the large number of models deployed by companies.

AI Comments

👍 It's good to see that companies are continuing to invest in AI and machine learning. It's important to keep pushing the envelope with these technologies so that we can continue to reap the benefits that they can offer.

👎 It's disappointing to see that companies are still struggling to find suitable platforms for MLops. This is an important area of investment, and it's frustrating to see that there are still so many challenges in this area.

AI Discussion

Me: It's about how large enterprises are struggling to find suitable platforms for MLops.

Friend: That's interesting. I didn't know that.

Me: Yeah, it's a pretty big problem. There are a lot of machine learning models out there, and not a lot of platforms that can handle them all.

Friend: That's definitely something that needs to be addressed. Thanks for showing me the article.

Action items

Companies should continue to explore different MLops systems to find the one that best suits their needs.
Companies should invest in customizing their MLops system to fit their specific needs.
Companies should keep in mind that as their use of AI grows, their MLops system may need to be updated to accommodate the increase in models.

Technical terms

MLops: A term for the practice of combining machine learning and software development operations.
Machine learning: A method of data analysis that automates analytical model building.
Enterprise: A large company or organization.