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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Hi @ErnieZ, thank you for your question.
The most challenging thing when working with ML and AI as a developer to me is the step of gathering and preparing data for training - what will later produce our ML model.
A training dataset requires a column designated as the target - that is the value you want to predict, and is not the data that you will have at the time of making predictions.
Therefore, a training dataset might require you to combine multiple sources of data so it contains this target as well.
Other than that, it used to be that training the model itself was also a quite a time consuming process, but I find it pretty straightforward with DataRobot nowadays.
Hope this answers your question!
To my knowledge most things in the DataRobot UI are supported with APIs and libraries and vice versa, but there might be a few minor discrepancies with features not available in the APIs and libraries yet.
We don’t have a list to share, but if you have a concrete example in mind then our support team will be able to answer you this.
We are working on a set of demos and example projects. I'll be publishing the first one very soon - in the next few days.
I will update this response when I will have posted it and let you know!
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.
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 !
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?