AutoML without compromises
Use Cases and Deployment Scope
We are using the AutoML to create NLP models that are used to automate customer service enquiries, where we use the models to read the email and classify it. For those emails with a high confidence it will automatically respond but for those that they are not sure we use people to classify the email and help enrich the dataset.
Pros
- The NLP models results were much better than the ones that we did outside of the platform.
- It is really easy and quick to build a good model with a lot of the manual boring tasks all done automatically like one hot encoding, etc.
- Kortical shows the features and their importance for any model type as part of the platform which is great for understanding the models.
Cons
- It would be ideal to have Jupyter built into the platform, they say it is coming.
- Also while it is easy to use, at the start it would have been helpful to have more help guides.
Likelihood to Recommend
Kortical is really widely applicable to many use cases, although it doesn't handle images or video it is great to help you build really great ML models without needing to plan ahead what you are going to try, you let the platform build you the best model. It is suited to beginner and more advanced data scientists as you can edit the code to narrow the search space which makes model creation more you build it without AutoML. Hosting the model behind an API that is ready to go is great as it saves so much time vs doing that dev work from scratch.
