1. Building Efficient and Reliable Models
    Zia AutoML enables you to easily build production-ready machine learning models with high accuracy and precision, and low error margins. You can customize the characteristics of the dataset you provide and choose the columns for the model’s training as required. This ensures that your models are efficient, sustainable, and perform well.
  2. Abstraction of Complexities
    Zia AutoML is generally highly useful and advantageous for non-experts in machine learning. The complex processing behind a model’s creation and training, including the algorithm implementation and data pipelines, are completely abstracted. Catalyst entirely handles the implementation of AutoML, which allows you to focus on the essentials instead of behind-the-scenes management.
  3. Rapid and End-To-End Processing of Automated Machine Learning
    AutoML covers the entire pipeline from analyzing raw data to building a production-ready machine learning model. It substantially accelerates the time it takes to configure, train, and deploy machine learning models. You can easily build and train a functionally-rich AutoML model within minutes and implement it in your Catalyst application.
  4. Insightful Evaluation Reports
    The evaluation reports provided by AutoML contains insightful and actionable information, that is different for each model variant based on relevance. You can obtain a clear and perceptive view of your model’s strengths and potential from the Catalyst console after the model’s training is complete. You can even train a different model with different feature selections for the same dataset easily, based on the evaluation.
  5. Testing Before Implementation
    Catalyst allows you to test the model’s performance from the web console or from your local machine’s terminal with the click of a button. You can thoroughly test, rebuild, retrain, and customize your model before you implement it in your application.

Last Updated 2023-05-19 12:11:43 +0530 +0530