Thinking-data-science-a-data-science-practitioners-guide

14 1 Data Science Process
Fig. 1.10 Exhaustive list of neural network models/algorithms
have not even fully described so far. All you need to do is to provide them with a
dataset, and they will suggest you the best performing model, all fine-tuned and
ready-to-use for production. They provide the model rankings based on their accuracy
scores, and you can decide which one to use for your purpose.
These days you would find offerings from Google, Microsoft, and Amazon who
provide machine learning as a service (MLaaS), where you just need to upload your
dataset on their servers and eventually download a model pipeline which can be
hosted and used via a web service. Though this is fascinating, a true data scientist can
out-beat these automated services. Second, most of these follow a black-box
approach and do not provide you with the source code of model development for
you to fiddle with. A data scientist usually uses an automated service to quickly
narrow down his search on model selection.
For your own purposes and development, you may use some free libraries like
auto-sklearn, AutoKeras, or commercial versions of H2O.ai. I have provided an
exhaustive list of such frameworks in the AutoML chapter. These libraries support
many ML models for training and evaluation. They will train each model on your
dataset. After the training, they evaluate the model’s performance. Finally, they rank