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1. Signed Parameters for Secure ML Model Deployments

   In the Python ecosystem, using pickle to serialize machine learning models is very common. Pickle is a built-in Python library module that makes it easy to convert in-memory objects into bytestreams that can be saved to a hard drive or sent over networks. Pickling an object is very quick and simple and is the easiest way to persist a complex Python object for later use. However, pickle is not a secure serialization standard. The documentation for the pickle module in the Python standard library explicitly mentions the insecure nature of the pickle format: Warning The pickle module is not secure. Only unpickle data you trust. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling. Never unpickle data that could have come from an untrusted source, or that could have been tampered with. In this blog post, we'll be downloading a dataset, exploring it, training a model, signing the model parameters, and deploying the model parameters and model to a Kubernetes cluster as a RESTful service. We'll also be loading the model parameters from a network storage service to show how to secure the model parameters while they are stored separately from the model deployment.

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2. Health Checks for ML Model Deployments

   Deploying machine learning models in RESTful services is a common way to make the model available for use within a software system. RESTful services are the most common type of service deployed, since they are very simple to build, have wide compatibility, and have lots of tooling available for them. In order to monitor the availability of the service, RESTful APIs often provide health check endpoints which make it easy for an outside system to verify that the service is up and running. A health check endpoint is a simple endpoint that can be called by a process manager to ascertain whether the application is running correctly. In this blog post we'll be working with Kubernetes so we'll focus on the health checks supported by Kubernetes.

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3. Policies for ML Models

   Machine learning models are being used to make ever more important decisions in the modern world. Because of the power of data modeling, ML models are able to learn the nuances of a domain and make accurate predictions even in situations where a human expert would not be able to. However, ML models are not omniscient and they should not run without oversight from their operators. To handle situations in which we don't want to have an ML model make predictions, we can create a policy that steps in before the prediction is returned to the user. A policy that is applied to an ML model is simply a rule that ensures that the model will never make predictions that are unsafe to use. For example, we can create a policy that make sure that a machine learning model that makes predictions about optimal airline ticket prices never makes predictions that cost the airline money. A good policy for an ML model is one that allows the model some leeway while also ensuring that the model’s predictions are safe to use. In this blog post, we'll write policies for ML models and deploy the policies alongside the model using the decorator pattern.

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4. Load Tests for ML Models

   In a previous blog post we showed how to create a RESTful model service for a machine learning model that we want to deploy. A common requirement for RESTful services is to be able to be able to continue working while being used by many users at the same time. In this blog post we'll show how to create a load testing script for an ML model service.

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