Does your product require AI/ML

MLOps.community · Intermediate ·📐 ML Fundamentals ·11mo ago

Key Takeaways

The video discusses the importance of evaluating whether a product requires machine learning (ML) or deep learning, and the costs associated with implementing these technologies. It also touches on the concept of model support tiers and the need for monitoring and maintenance of ML models in production.

Full Transcript

Yeah. Does your does your use case actually require machine learning like or even deep learning? That's a that's the first question you need to answer cuz that the machine does come doesn't come for free. Yeah. So you have price to pay. It's so expensive. So do you want actually want to do it? You want to evaluate on that front first, then start building your projects, train your models, prepare prepare your data, train your models, deploy models, do things like that. And are you looking at the cost? Cuz I I remember in the from predictive to generative blog, you talked about how there's different tiers of model support. And so if it's obviously the ride ETA, that's probably one of the most important models that can never go offline ever. And then if it's something maybe a little bit more experimental you are more relaxed about it. Yes. When Macanjo started back in 2016 at that time our mission was to enable machine learning for Uber basically get Wubver started with machine learning and at that time when M when Macandel started we only had like three use cases on Macel but now we have like thousand. Yeah. So and each one has their own dashboard. Each one has their own dashboard. Yeah. For example, if your model performance degrades, yeah, it automatically send out alerts to that team and then they look into why. And for you, if they're not shipping enough, then you're going to go look into why is there something blocking them in the Exactly. Yeah. Is our system is there some system bug that blocking uh the development? Is the pipeline keeps uh does the pipeline keep failing? And all things we we need to look at. So, but anyway, sorry I just I distracted you by thousands of dashboards. It just blows my mind. Yeah. Yeah. So um

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@UberEngineering #appliedAI #aiinfrastructure #mlops
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Playlist

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The video teaches the importance of evaluating the need for ML in a product and the costs associated with implementing ML. It also covers the concept of model support tiers and the need for monitoring and maintenance of ML models in production. By watching this video, viewers can learn how to determine whether ML is necessary for their product and how to build and deploy ML models effectively.

Key Takeaways
  1. Evaluate whether your product requires ML or deep learning
  2. Consider the costs associated with implementing ML
  3. Determine the appropriate model support tier for your use case
  4. Build and deploy ML models
  5. Monitor and maintain ML models in production
💡 Not all products require ML, and implementing ML can be expensive, so it's essential to evaluate the need for ML and consider the costs before starting a project.

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