Figure 1. Modzy Model Deployment Interface

Once your container image finishes uploading, follow a few simple steps to complete the full deployment:

  1. Configure the hardware and memory requirements for your model container (customizable to your infrastructure)
  2. Define the input and output specifications for your model so users understand how to submit inference API calls to your model and know what to expect as output
  3. Document information about your model (technical details, training data, performance metrics, and whatever else you prefer to include)

Now, you can publish your model, begin running production inference, and leverage all the monitoring features Modzy offers.

How the Python SDK Works

Two popular model training tools leveraged by data scientists are AWS Sagemakerand MLFlow. Both tools provide users an intuitive set of APIs to train, evaluate, and save ML models. While these tools are commonly leveraged for model development, their model deployment equivalents do not field the same widespread usage. With Modzy, users have automatic containerization and deployment support for both these model frameworks into Modzy’s Python SDK. To begin using this capability, you need (1) the raw output of a trained Sagemaker or MLFlow model, and (2) the model artifacts saved in an AWS s3 bucket or Azure Blob.

Once you have met these requirements, use Modzy’s Python SDK in your preferred editor (my tip: Jupyter Notebooks work great) and follow a few steps to deploy your model to Modzy:

  • Complete a yaml file with documentation about your model and provide any additional metadata files for the specific model type that you chose. In the case of image classification, you will need to provide a labels.json file that contains a mapping between numerical classes and human readable labels.
  • Include credentials required to access your cloud storage blob that contains your model artifacts, specify the path to your weights file(s) and additional model resources, define your model type (e.g., Image Classification), and pass this information to Modzy’s Model Converter through the SDK.
  • Execute the converter and see your model deployed to your environment in just minutes.

For data scientists and developers who wish to add a more programmatic approach to their MLOps pipeline, Modzy’s SDK deployment capability unlocks the potential for automation, improved efficiency, and most importantly, a significant improvement in speed to production.

Using Modzy’s AI model deployment solution makes this process easier for all data scientists and all existing MLOps pipelines. It also gives way to the many features the Modzy platform offers once models are in production. These features include the model versioning management, intuitive APIs for model inferencing, standardized infrastructure management, auto-scaling for resource management, and comprehensive monitoring capabilities including drift detection, explainability, model health checks, and more. Data scientists no longer need to modify their existing training processes to deploy their models into production and face the AI Valley of Death. Using Modzy, a seamless deployment is possible.