Data Science Lab
  • What is Data Science Lab?
  • Accessing the Data Science Lab Module
  • Data Science Lab Quick Start Flow
  • Project
    • Environments
    • Creating a Project
    • Project List
      • Keep Multiple Versions of a Project
      • Sharing a Project
      • Editing a Project
      • Activating a Project
      • Deactivating a Project
      • Deleting a Project
    • Tabs for a Data Science Lab Project
      • Tabs for TensorFlow and PyTorch Environment
        • Notebook
          • Ways to Access Notebook
            • Creating a Notebook
            • Uploading a Notebook
          • Notebook Page
            • Preview Notebook
            • Notebook Cells
              • Using a Code Cell
              • Using a Markdown Cell
            • Modifying a Notebook
            • Resource Utilization Graph
            • Notebook Taskbar
            • Notebook Operations
              • Datasets
                • Copy Path (for Sandbox files)
              • Secrets
              • Algorithms
              • Transforms
              • Models
                • Model Explainer
                • Registering & Unregistering a Model
                • Applying Filter
              • Predict
              • Artifacts
              • Variable Explorer
              • Find and Replace
          • Notebook List Page
            • Export
              • Export to Pipeline
              • Export to GIT
            • Notebook Version Control
            • Sharing a Notebook
            • Editing a Notebook
            • Deleting a Notebook
        • Dataset
          • Adding Data Sets
            • Data Sets
            • Data Sandbox
          • Dataset List Page
            • Preview
            • Data Profile
            • Create Experiment
            • Data Preparation
            • Delete
        • Utility
        • Model
          • Model Explainer
          • Import Model
          • Export to GIT
          • Register a Model
          • Unregister A Model
          • Register a Model as an API Service
            • Register a Model as an API
            • Register an API Client
            • Pass Model Values in Postman
          • AutoML Models
        • Auto ML
          • Creating Experiments
            • Accessing the Create Experiment Option
              • Configure
              • Select Experiment Type
          • AutoML List Page
            • View Report
              • Details
              • Models
                • View Explanation
                  • Model Summary
                  • Model Interpretation
                    • Classification Model Explainer
                    • Regression Model Explainer
                  • Dataset Explainer
            • Delete
      • Tabs for PySpark Environment
        • Notebook
          • Ways to Access Notebook
            • Creating a Notebook
            • Uploading a Notebook
          • Notebook Page
            • Preview Notebook
            • Notebook Cells
              • Using a Code Cell
              • Using a Markdown Cell
            • Modifying a Notebook
            • Resource Utilization Graph
            • Notebook Taskbar
            • Notebook Operations
              • Datasets
                • Copy Path (for Sandbox files)
              • Secrets
              • Variable Explorer
              • Find and Replace
          • Notebook List Page
            • Export
              • Export to Pipeline
              • Export to GIT
            • Notebook Version Control
            • Sharing a Notebook
            • Editing a Notebook
            • Deleting a Notebook
        • Dataset
          • Adding Data Sets
          • Dataset List Page
            • Preview
            • Data Profile
            • Data Preparation
            • Delete
        • Utility
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  1. Project
  2. Tabs for a Data Science Lab Project
  3. Tabs for TensorFlow and PyTorch Environment

Auto ML

The Auto ML tab allows the users to create various experiments and list all the created experiments.

PreviousAutoML Models NextCreating Experiments

Last updated 2 years ago

Automated Machine Learning (AutoMl) is a process that involves automating the selection of machine learning models and hyperparameters tuning. It aims to reduce the time and resources required to develop and train accurate models by automating some of the time-consuming and complex tasks.

The Auto ML feature provided under Data Science Lab is capable of covering all the steps, from starting with a raw data set to creating a ready-to-go machine learning model.

An Auto ML experiment is the application of machine learning algorithms to a dataset.

Check out the given illustration to understand the step-by-step flow of the Auto ML.

Steps to create an Auto ML Experiment

Please Note:

  • Auto ML functionality is a tool to help speed up the process of developing and training machine learning models. It’s always important to carefully evaluate the performance of a model generated by the AutoML tool.

Refer the Data Science Lab Quick Start Flow page to get an overview of the Data Science Lab module in nutshell. to get redirected to the quick start flow page.

Click here