What are the most effective deployment strategies for AI/ML in continuous delivery?

1 Model Versioning

One of the key challenges of deploying AI/ML models is managing their versions and dependencies. Unlike traditional software, AI/ML models depend on data, algorithms, and parameters that may change over time. To ensure reproducibility and traceability, you need to use a model versioning system that can track and store all the components of your model, such as the code, the data, the configuration, and the performance metrics. A model versioning system can also help you compare different versions of your model and roll back to previous ones if needed.

2 Model Testing

Another challenge of deploying AI/ML models is ensuring their quality and accuracy. Unlike traditional software, AI/ML models may not have clear specifications or expected outputs, and may exhibit unpredictable behaviors or biases. To ensure that your models meet your business goals and ethical standards, you need to use a model testing framework that can validate your model's functionality, performance, robustness, and fairness. A model testing framework can also help you monitor your model's behavior and performance in production and detect any anomalies or drifts.

3 Model Packaging

Once you have versioned and tested your model, you need to package it for deployment. Packaging your model means creating a standardized and portable format that can encapsulate all the necessary components of your model, such as the code, the data, the dependencies, and the environment. Packaging your model can help you simplify and streamline the deployment process, as well as ensure compatibility and consistency across different platforms and environments. Some of the common formats for packaging AI/ML models are containers, such as Docker or Kubernetes, or serverless functions, such as AWS Lambda or Azure Functions.

4 Model Deployment

After you have packaged your model, you need to deploy it to your target environment, such as a cloud service, an edge device, or a web application. Deploying your model means making it available and accessible for inference, which is the process of generating predictions or recommendations based on new input data. Deploying your model can involve different methods and challenges depending on your use case and requirements, such as scalability, latency, security, and cost. Some of the common methods for deploying AI/ML models are REST APIs, batch processing, or streaming processing.

5 Model Updating

Once you have deployed your model, you need to update it regularly to keep it relevant and accurate. Updating your model means retraining it with new data, refining it with new algorithms or parameters, or replacing it with a new version. Updating your model can help you improve its performance and adapt to changing data and business needs. However, updating your model can also introduce risks and trade-offs, such as breaking compatibility, losing stability, or increasing complexity. Therefore, you need to use a model updating strategy that can balance the benefits and costs of updating your model, such as A/B testing, canary deployment, or blue-green deployment.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?