How to upload a Keras ML model with Giskard?

[olujerry]

Outline

Introduction

• Briefly introduce the concept of model testing and the importance of quality assurance in ML applications.
• Introduce Giskard as an open-source testing framework for ML models.
• Mention the focus of the article: step-by-step guide to uploading a Keras ML model with Giskard.

Preparing for Model Upload

• Install Giskard: Provide instructions on how to install Giskard using pip.
• Prepare the Keras Model: Explain the requirement of having a trained Keras model saved as a file.

Importing Giskard and Loading the Model

Import Giskard Library: Showcase the import statement for the Giskard library in Python. Load the Keras Model: Describe how to load a pre-trained Keras model into memory using appropriate methods

Uploading the Model to Giskard

• Utilize the upload_model() Function: Explain the usage of the giskard.upload_model() function to upload the Keras model.
• Specify Model Details: Discuss any additional parameters that need to be provided, such as model name or version.

Testing and Validation with Giskard

• Overview of Giskard Features: Highlight the various testing and validation functionalities offered by Giskard.
• Vulnerability Detection: Explain how Giskard can detect vulnerabilities in the uploaded model.
• Automatic Test Generation: Discuss the generation of relevant tests based on detected vulnerabilities.
• Leveraging Giskard Catalog: Explain how to utilize the Giskard catalog for quality assurance best practices.

Conclusion

• Recap the process: Summarize the steps involved in uploading a Keras ML model with Giskard.
• Highlight the benefits: Mention the advantages of using Giskard for model testing and validation.
• Encourage further exploration: Suggest exploring the extensive capabilities of Giskard beyond model upload.

[alexcombessie]

@luca-martial what do you think?

[luca-martial]

@olujerry has registered through the community writing program, we've taken the conversation offline