Developers and data scientists often work together in teams, but our objectives and approaches to data and ML couldn’t be more different: from how we reason about data, why we collect it, and the tools we use, to where AI actually fits in our workflow.
In this Ask the Expert event, you will be able to chat with Zan and ask your questions about AI and ML development, DataRobot APIs, etc. On this interesting and important topic, Zan is available to answer your questions.
|Zan Markan is a Developer Advocate at DataRobot. He has spent over a decade working in technology, in a variety of roles and enterprises—as a developer, manager, and in developer relations.
He is passionate about educating developers and enabling them to be successful with whichever technology they are using.
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As a developer there are many ways and reasons why you could consider using machine learning technologies in your apps.
If you're working on a team with data scientists, then you can integrate the models that they build into your applications, or build visualizations to monitor the models you have deployed in production.
If you're not working alongside data scientists you can also create models yourself, with the data you are already likely collecting - for logging or analytics for example.
And if you're looking for use-cases themselves, I would recommend you check out the answer I gave a few days ago to another community member about some interesting use-cases: https://community.datarobot.com/t5/ai-ml-general-discussions/ask-the-expert-ai-and-ml-for-developers...
Hope this helps!
DataRobot's MLOps automatically monitors models when you deploy them into production.
One of the ways models are being monitored is for data drift - it's checking for whether the data you are using to make predictions is starting to deviate from the data that you used to train the model. Models with a high measure of data drift are considered less reliable.
One of the most common applications is what we call a Model Factory.
The idea here is that you build your own automation layer on-top of DataRobot.
For example, you might need to build several hundred propensity models for different products to use inside a large scale product recommendation or lifetime customer value application. You can use the DataRobot APIs to automate the building of these large number of projects and models, and then automatically deploy and integrate them.
Does that make sense?
I checked out that link and see a lot of other good info in your wiki too. thanks for clarifying and also sending along that link. I'll be busy reading it all for awhile !
Both TensorFlow and PyTorch are individual open source libraries and frameworks for machine learning (deep learning to be precise).
DataRobot's Automated Machine Learning product will use multiple libraries and frameworks in addition to TensorFlow to train models, in order to suggest you which one works best on your dataset, and your use-case.