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Writer's pictureGeorges Zorba

Part 2: Building Intelligent Models: AI Development and Deployment on OpenShift

Welcome back to our series on OpenShift AI! In the first part, we introduced OpenShift AI and explored its capabilities and benefits. Now, let’s dive into the process of developing and deploying AI models using OpenShift. This guide will help you understand the tools and workflows involved in creating intelligent models on the OpenShift platform.


Developing AI Models on OpenShift


Developing AI models on OpenShift is a streamlined process, thanks to its support for a wide range of AI/ML frameworks and tools. Here’s how you can get started:



  1. Select Your Tools and Frameworks: OpenShift AI supports popular AI/ML frameworks such as TensorFlow, PyTorch, and JupyterLab. These tools provide a robust environment for data scientists and developers to create and experiment with AI models  .

  2. Set Up Your Development Environment: Use JupyterLab for interactive development and exploratory data analysis. OpenShift AI provides pre-configured notebook images that include essential libraries and tools for AI/ML development  .

  3. Data Preparation: Gather and prepare your data for training. OpenShift AI supports various data sources and formats, allowing you to preprocess and clean your data efficiently.

  4. Model Training: Train your AI models using the selected frameworks. OpenShift AI’s infrastructure is optimized for distributed training, enabling you to leverage multiple GPUs and nodes for faster training times .

  5. Model Tuning: Fine-tune your models to improve performance. OpenShift AI provides tools for hyperparameter tuning and model optimization, ensuring that your models achieve the desired accuracy and efficiency.


Deploying AI Models with OpenShift


Once your model is developed and trained, the next step is to deploy it. OpenShift AI makes deployment straightforward and scalable:


  1. Containerize Your Model: Package your AI model into a container. OpenShift AI supports Docker containers, making it easy to encapsulate your model and its dependencies.

  2. Deploy to OpenShift: Deploy the containerized model to OpenShift. You can use OpenShift’s built-in deployment tools to manage the deployment process, ensuring that your model is available and scalable across your chosen infrastructure .

  3. Model Serving: OpenShift AI supports various model serving frameworks, including TensorFlow Serving and TorchServe. Choose the appropriate serving framework based on your model’s requirements and deploy it to OpenShift .

  4. Monitor and Manage Your Model: Use OpenShift AI’s monitoring tools to track your model’s performance. Monitor key metrics such as latency, throughput, and accuracy to ensure that your model is performing optimally in production.


Case Study: AI Model Deployment



To illustrate the deployment process, let’s look at a real-world example:


Case Study: Deploying a Fraud Detection Model


  • Problem Statement: A financial institution wants to deploy a machine learning model to detect fraudulent transactions in real-time.

  • Model Development: Data scientists develop and train a fraud detection model using TensorFlow on OpenShift AI. The model is fine-tuned for high accuracy and low false positives.

  • Containerization and Deployment: The trained model is containerized using Docker and deployed to OpenShift. OpenShift’s scalable infrastructure ensures that the model can handle high transaction volumes.

  • Model Serving and Monitoring: TensorFlow Serving is used to serve the model. OpenShift AI’s monitoring tools track the model’s performance, alerting the team to any anomalies or performance issues.

  • Results: The deployed model successfully identifies fraudulent transactions in real-time, reducing financial losses and improving customer trust.


Developing and deploying AI models on OpenShift AI is a powerful way to leverage the platform’s capabilities for real-world applications. With support for popular AI/ML frameworks, scalable infrastructure, and robust monitoring tools, OpenShift AI simplifies the entire lifecycle of AI model development and deployment.


Stay tuned for the next part of this series, where we’ll explore how to manage and scale AI workloads on OpenShift.


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