Get started with Watson Machine Learning Accelerator

As a data scientist, get started with IBM Watson® Machine Learning Accelerator.

Start using Watson Machine Learning Accelerator in the following ways:

Accessing data to be used by Watson Machine Learning Accelerator

Access data to be used by Watson Machine Learning Accelerator, see:

Train your data using Watson Machine Learning Accelerator methods:

Use the WML Accelerator rest APIs to train your data. Generate an API key and try the API, see Try the Watson Machine Learning Accelerator REST API. .
Use the WML Accelerator command line interface (CLI).
- To download the WML Accelerator CLI, see Tools.
- To start using the dlicmd tool, run the following for usage:
```
python3 dlicmd.py --help
```

If you have the WML service installed and connected to the WML Accelerator service, you can run training from WML which can be monitored from WML Accelerator. To connect the WML service, see: Connecting Watson Machine Learning Accelerator to Watson Machine Learning.

Train your data using WML by using the following:

Get started using Watson Machine Learning Accelerator notebooks in Cloud Pak for Data. See: Working with Watson Machine Learning Accelerator notebooks in IBM Cloud Pak for Data

After submitting training, lview your training progress.

Deploy trained models as an inference service.

Use the elastic distributed inference command line interface (dlim), publish and start running the trained model as a service.
- To download and configure dlim, see: Download and configure the elastic distributed inference (dlim) command line utility
- To see reference guide, see dlim.
Use the WML Accelerator rest APIs for inference.

To troubleshoot problems in your jobs, see application logs.