Hi HN, we have seen a lot of AutoML frameworks out there. As a Data Scientist myself, I have refrained from using these because at the end of the day, you have to submit complete source code to your clients, not just a functioning model. That is why we created AutoAI. Given data and target (value to predict), it can automatically discover and fully train the best performing AI solution. Still, most importantly, it also goes on to produce high-quality Jupyter Notebook code. AutoAI does Whitebox AutoML. A much-needed feature for Data Scientists. Do give it a try, and let me know what you think.
Looks like a nice project. I just bookmarked it to try sometime.<p>At a previous job, my boss wanted me to spend time on AutoML. I based my work on Google’s AdaNet [1] that did architecture search inside a single TensorFlow session. Unfortunately that project seems to have been abandoned.<p>[1] <a href="https://github.com/tensorflow/adanet" rel="nofollow">https://github.com/tensorflow/adanet</a>
I noticed a variety of AutoML solutions in the market addressing similiar pain points. How is this different from other solutions/platforms.... Isn't this a saturated space?