Introduction to Model Serving

In the context of governments, serving machine learning models means making trained models available for use by other applications and systems. This could include making predictions or classifications based on input data, or providing insights and recommendations based on the results of data analysis.

Serving machine learning models in a government context raises important issues related to data privacy. Government agencies are often responsible for collecting and managing sensitive personal data, such as health records, financial data, and criminal records. When serving machine learning models, it's critical to ensure that this data is protected and that access to it is strictly controlled.

To address these concerns, government agencies must implement robust data privacy and security measures when serving machine learning models. This could include encrypting data both at rest and in transit, implementing access controls and user authentication, and regularly monitoring for data breaches and vulnerabilities.

In addition to data privacy and security, it's also important to consider the ethical implications of serving machine learning models in a government context. Machine learning models can be biased or discriminatory, leading to unfair treatment of certain groups of people. To mitigate these risks, government agencies must carefully evaluate and monitor their machine learning models, and take steps to address any biases or discrimination that may arise.

Overall, serving machine learning models in a government context requires careful consideration of data privacy, security, and ethical concerns. By implementing robust measures to protect personal data and prevent bias, government agencies can leverage the power of machine learning to make better decisions and improve outcomes for citizens while maintaining trust and transparency.

Why serve with us?

Serving machine learning models with the Advanced Analytics Workspace (AAW) has several advantages. First, the AAW is an open-source data analytics platform that provides access to a variety of advanced analytics tools, including Python, R, and SAS. This makes it easy to deploy machine learning models and integrate them into existing workflows.

Second, the AAW supports multiple MLOps frameworks, including Couler, Seldon, and Argo Workflows. These frameworks provide a range of features, including model versioning, model serving, and model monitoring, that simplify the process of deploying and managing machine learning models in production.

Third, the AAW provides a secure and scalable platform for serving machine learning models with Protected B status. Models can be served using containerized environments, such as Docker, which provide a high level of isolation and security. The AAW also provides access to cloud computing resources, allowing users to scale up their compute power as needed to handle high volumes of requests.

Finally, the AAW is a collaborative platform that allows users to share code and data with other researchers and analysts. This fosters a community of users who can learn from each other's work and collaborate on projects that require advanced analytics capabilities.

In summary, serving machine learning models with the Advanced Analytics Workspace provides access to advanced analytics tools, multiple MLOps frameworks, a secure and scalable Proteced B platform, and a collaborative community of users, making it an ideal platform for data scientists and analysts who want to deploy and manage machine learning models in production.

Seldon Core

Seldon Core is an open-source platform for deploying, scaling, and monitoring machine learning models on Kubernetes. It provides a simple and efficient way to deploy machine learning models as microservices in a production environment.

Serving machine learning models using Seldon Core involves the following steps:

1. Model packaging: The first step is to package the trained machine learning model in a container image with all the required dependencies. Seldon Core supports various machine learning frameworks, including TensorFlow, PyTorch, and Scikit-learn.

2. Model deployment: Once the container image is created, the next step is to deploy the model on Kubernetes using Seldon Core. This involves defining the deployment configuration file, which specifies the resources required for the deployment, such as the number of replicas and the compute resources.

3. Model serving: Once the model is deployed, Seldon Core exposes a REST API endpoint that can be used to make predictions. Clients can send requests to the endpoint with input data, and the model will return the corresponding output. Seldon Core also supports various deployment patterns, such as canary deployment and A/B testing, to enable easy experimentation and testing of different models.

4. Model monitoring: Seldon Core provides various monitoring capabilities to track the performance of deployed models. This includes real-time monitoring of model metrics, such as latency and throughput, as well as logging of request and response data for debugging purposes.

Seldon Core makes it easy to serve machine learning models at scale, with support for high availability, scalability, and fault tolerance. It also provides integration with various Kubernetes-native tools, such as Istio and Prometheus, to enable advanced monitoring and observability.