As the founder of Neuraxio and as of having done a lot of machine learning and deep learning projects in my life, I’ve developed a business process of doing machine learning for my clients to use to get results. What follows can be used for natural language processing, time series processing, computer vision, tabular data analysis, and so forth: it is general to all machine learning and deep learning projects, although not applicable to every AI project.
Several design patterns are discussed with practical examples and their implications. So not only you want to build neural networks and other machine learning algorithms, but also you want to find the best hyperparameters for them automatically. We’ll here demonstrate how it’s possible in a clean code way.
Daily, what does a data scientist do? And how can Automated Machine Learning avoid you to babysit your AI, practically?
Here is a metaphor: your data scientist is a mom. A babysitter.
The data scientist creates a nice artificial neural network and trains it on data. Then he’s going to supervise the learning. The data scientist will make sure that the learning converges in the right way so that the artificial neural network can give good predictions and then flourish.
Seriously, that’s all well and good, but it costs time, and it costs money.
Is there anything we can do to automate the process of being a mom - actually being a data scientist? Actually, we can use Automated Machine Learning.