Table of Contents
- Automated ML Introduction
- Setup using Compute Instances
- Setup using a Local Conda environment
- Setup using Azure Databricks
Automated ML introduction
Automated machine learning (automated ML) builds high quality machine learning models for you by automating model and hyperparameter selection. Bring a labelled dataset that you want to build a model for, automated ML will give you a high quality machine learning model that you can use for predictions.
If you are new to Data Science, automated ML will help you get jumpstarted by simplifying machine learning model building. It abstracts you from needing to perform model selection, hyperparameter selection and in one step creates a high quality trained model for you to use.
If you are an experienced data scientist, automated ML will help increase your productivity by intelligently performing the model and hyperparameter selection for your training and generates high quality models much quicker than manually specifying several combinations of the parameters and running training jobs. Automated ML provides visibility and access to all the training jobs and the performance characteristics of the models to help you further tune the pipeline if you desire.
Below are the three execution environments supported by automated ML.
Setup using Compute Instances - Jupyter based notebooks from a Azure Virtual Machine
- Open the ML Azure portal
- Select Compute
- Select Compute Instances
- Click New
- Type a Compute Name, select a Virtual Machine type and select a Virtual Machine size
- Click Create
Setup using a Local Conda environment
To run these notebook on your own notebook server, use these installation instructions. The instructions below will install everything you need and then start a Jupyter notebook.
1. Install mini-conda from here, choose 64-bit Python 3.7 or higher.
- Note: if you already have conda installed, you can keep using it but it should be version 4.4.10 or later (as shown by: conda -V). If you have a previous version installed, you can update it using the command: conda update conda. There's no need to install mini-conda specifically.
2. Downloading the sample notebooks
- Download the sample notebooks as zip and extract the contents to a local directory.
3. Setup a new conda environment
The automl_setup script creates a new conda environment, installs the necessary packages, configures the widget and starts a jupyter notebook. It takes the conda environment name as an optional parameter. The default conda environment name is azure_automl. The exact command depends on the operating system. See the specific sections below for Windows, Mac and Linux. It can take about 10 minutes to execute.
Packages installed by the automl_setup script:
- python
- nb_conda
- matplotlib
- numpy
- cython
- urllib3
- scipy
- scikit-learn
- pandas
- tensorflow
- py-xgboost
- azureml-sdk
- azureml-widgets
- pandas-ml
For more details refer to the automl_env.yml
4. Running configuration.ipynb
- Before running any samples you next need to run the configuration notebook. Click on configuration notebook
- Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (instructions in notebook)
5. Running Samples
- Please make sure you use the Python [conda env:azure_automl] kernel when trying the sample Notebooks.
- Follow the instructions in the individual notebooks to explore various features in automated ML.
6. Starting jupyter notebook manually
To start your Jupyter notebook manually, use:
conda activate azure_automl
jupyter notebook
or on Mac or Linux:
source activate azure_automl
jupyter notebook
Setup using Azure Databricks
NOTE: Please create your Azure Databricks cluster as v7.1 (high concurrency preferred) with Python 3 (dropdown). NOTE: You should at least have contributor access to your Azure subcription to run the notebook.