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Chapter 1. Overview

Red Hat OpenShift AI is an artificial intelligence (AI) platform that provides tools to rapidly train, serve, and monitor machine learning (ML) models onsite, in the public cloud, or at the edge.

OpenShift AI provides a powerful AI/ML platform for building AI-enabled applications. Data scientists and MLOps engineers can collaborate to move from experiment to production in a consistent environment quickly.

You can deploy OpenShift AI on any supported version of OpenShift, whether on-premise, in the cloud, or in disconnected environments. For details on supported versions, see Red Hat OpenShift AI: Supported Configurations.

1.1. Data science workflow
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For the purpose of getting you started with OpenShift AI, the following figure illustrates a simplified data science workflow. The real world process of developing ML models is an iterative one.

Figure 1.1. Simplified data science workflow

Simplified data science workflow
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Simplified data science workflow

The simplified data science workflow for predictive AI use cases includes the following tasks:

  • Defining your business problem and setting goals to solve it.
  • Gathering, cleaning, and preparing data. Data often has to be federated from a range of sources, and exploring and understanding data plays a key role in the success of a data science project.
  • Evaluating and selecting ML models for your business use case.
  • Train models for your business use case by tuning model parameters based on your set of training data. In practice, data scientists train a range of models, and compare performance while considering tradeoffs such as time and memory constraints.
  • Integrate models into an application, including deployment and testing. After model training, the next step of the workflow is production. Data scientists are often responsible for putting the model in production and making it accessible so that a developer can integrate the model into an application.
  • Monitor and manage deployed models. Depending on the organization, data scientists, data engineers, or ML engineers must monitor the performance of models in production, tracking prediction and performance metrics.
  • Refine and retrain models. Data scientists can evaluate model performance results and refine models to improve outcome by excluding or including features, changing the training data, and modifying other configuration parameters.

1.2. About this guide
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This guide assumes you are familiar with data science and ML Ops concepts. It describes the following tasks to get you started with using OpenShift AI:

  • Log in to the OpenShift AI dashboard
  • Create a data science project
  • If you have data stored in Object Storage, configure a connection to more easily access it
  • Create a workbench and choose an IDE, such as JupyterLab or code-server, for your data scientist development work

  • Learn where to get information about the next steps:

    • Developing and training a model
    • Automating the workflow with pipelines
    • Implementing distributed workloads
    • Testing your model
    • Deploying your model
    • Monitoring and managing your model

See also OpenShift AI tutorial: Fraud detection example. It provides step-by-step guidance for using OpenShift AI to develop and train an example model in JupyterLab, deploy the model, and refine the model by using automated pipelines.

1.3. Glossary of common terms
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This glossary defines common terms for Red Hat OpenShift AI.

accelerator
In high-performance computing, a specialized circuit that is used to take some of the computational load from the CPU, increasing the efficiency of the system. For example, in deep learning, GPU-accelerated computing is often employed to offload part of the compute workload to a GPU while the main application runs off the CPU.
artificial intelligence (AI)
The capability to acquire, process, create and apply knowledge in the form of a model to make predictions, recommendations or decisions.
bias detection
The process of calculating fairness metrics to detect when AI models are delivering unfair outcomes based on certain attributes.
custom resource (CR)
A resource implemented through the Kubernetes CustomResourceDefinition API. A custom resource is distinct from the built-in Kubernetes resources, such as the pod and service resources. Every CR is part of an API group.
custom resource definition (CRD)
In Red Hat OpenShift, a custom resource definition (CRD) defines a new, unique object Kind in the cluster and lets the Kubernetes API server handle its entire lifecycle.
connections
A configuration that stores the parameters required to connect to an S3-compatible object storage, database or OCI-compliant container registry from a data science project.
connection type
The type of external source to connect to from a data science project, such as an OCI-compliant container registry, S3-compatible object storage, or Uniform Resource Identifiers (URIs).
data science pipelines
A workflow engine that is used by data scientists and AI engineers to automate pipelines, such as model training and evaluation pipelines. Data science pipelines also includes experiment tracking capabilities, artifact storage, and versioning.
data science project
An OpenShift project for organizing data science work. Each project is scoped to its own Kubernetes namespace.
disconnected environment
An environment on a restricted network that does not have an active connection to the internet.
distributed workloads
Data science workloads that are run simultaneously across multiple nodes in an OpenShift cluster.
fine-tuning
The process of adapting a pre-trained model to perform a specific task by conducting additional training. Fine tuning may involve (1) updating the model’s existing parameters, known as full fine tuning, or (2) updating a subset of the model’s existing parameters or adding new parameters to the model and training them while freezing the model’s existing parameters, known as parameter-efficient fine tuning.
graphics processing unit (GPU)
A specialized processor designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display. GPUs are heavily utilized in machine learning due to their parallel processing capabilities.
inference
The process of using a trained AI model to generate predictions or conclusions based on the input data provided to the model.
inference server
A server that performs inference. Inference servers feed the input requests through a machine learning model and return an output.
large language model (LLM)
A language model with a large number of parameters, trained on a large quantity of text.
machine learning (ML)
A branch of artificial intelligence (AI) and computer science that focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving the accuracy of AI models.
model
In a machine learning context, a set of functions and algorithms that have been trained and tested on a data set to provide predictions or decisions.
model registry
A central repository containing metadata related to machine learning models from inception to deployment. The metadata ranges from high-level information such as the deployment environment and project origins, to intricate details like training hyperparameters, performance metrics, and deployment events.
model server
A container that hosts a machine learning model, exposes an API to handle incoming requests, performs inference, and returns model predictions.
model-serving runtime
A component or framework that helps create model servers for deploying machine learning models and build APIs optimized for inference.
MLOps
The practice for collaboration between data scientists and operations professionals to help manage the production machine learning (or deep learning) lifecycle. MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. It involves model development, training, validation, deployment, monitoring, and management and uses methods like CI/CD.
notebook interface
An interactive document that contains executable code, descriptive text for that code, and the results of any code that is run.
object storage
A method of storing data, typically used in the cloud, in which data is stored as discrete units, or objects, in a storage pool or repository that does not use a file hierarchy but that stores all objects at the same level.
OpenShift Container Platform cluster
A group of physical machines that contains the controllers, pods, services, and configurations required to build and run containerized applications.
persistent storage
A persistent volume that retains files, models or other artifacts across components such as model deployments, data science pipelines and workbenches.
persistent volume claim (PVC)
A persistent volume claim (PVC) is a request for storage in the cluster by a user.
quantization
A method of compressing foundation model weights to speed up inferencing and reduce memory needs.
serving
The process of hosting a trained machine learning model as a network-accessible service. Real-world applications can send inference requests to the service by using a REST or gRPC API and receive predictions.
ServingRuntime
A custom resource definition (CRD) that defines the templates for pods that can serve one or more particular model formats. Each ServingRuntime CRD defines key information such as the container image of the runtime and a list of the model formats that the runtime supports. Other configuration settings for the runtime can be conveyed through environment variables in the container specification. It also dynamically loads and unloads models from disk into memory on demand and exposes a gRPC service endpoint to serve inferencing requests for loaded models.
vLLM
A high-throughput and efficient inference engine for running large-language models that integrates with popular models and frameworks.
workbench
An isolated environment for development and experimentation with ML models. Workbenches typically contain integrated development environments (IDEs), such as JupyterLab, RStudio, and Visual Studio Code.
workbench image
An image that includes preinstalled tools and libraries that you need for model development. Includes an IDE for developing your machine learning (ML) models.
YAML
A human-readable data-serialization language. It is commonly used for configuration files and in applications where data is being stored or transmitted.
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