Top considerations for building a production-ready AI/ML environment

Get more business value from your data

The amount of data created is expected to reach more than 221,000 exabytes by 2026.¹ In a digital world, your data can be a critical competitive advantage. Even so, collecting data is only the starting point — how you use your data is the real differentiator.

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) employ data to deliver business insight, automate tasks, and advance system capabilities. These technologies have the potential to transform all aspects of business — from customers and employees to development and operations. Building AI/ML into your applications can help you achieve measurable business outcomes:

• Increase customer satisfaction.
• Offer differentiated digital services.
• Optimize existing business services.
• Automate business operations.
• Increase revenue.
• Improve decision making.
• Increase efficiency and reduce costs.

Key technologies

This e-book discusses several technologies for actionable data analysis:

• Artificial intelligence involves machines imitating human behavior to perform tasks that typically require human intervention.
• Machine learning is a subset of AI that uses algorithms and statistical models to perform tasks without explicit instructions.
• Deep learning is a subset of ML that uses layers to progressively extract high-level features from raw input, similar to a human brain. For example, generative AI can create high-quality text, images, and other content based on trained DL models.
• Machine learning operations (MLOps) encompasses all of the tools, platforms, and processes needed to create, train, deploy, monitor and continuously improve AI/ML models for use in cloud-native applications.

Build a production-ready AI/ML environment

Deploying AI/ML in production is an iterative process that extends beyond simply creating AI/ML models. The main steps in the AI/ML life cycle are:

1. Set business goals for your AI/ML initiative and share them with all stakeholders.
2. Gather and prepare the data required for your AI/ML initiative.
3. Develop models according to your goals.
4. Deploy models in your application development process.
5. Implement intelligent, ML-powered applications and start inferencing.
6. Monitor and manage models for accuracy over time.

AI/ML deployment challenges

Organizations face several challenges when building an AI/ML environment:

• Talent shortages. A limited number of available AI/ML experts makes it harder to find and retain data scientists and engineers, ML engineers, software developers, and other staff with the right knowledge.
• Lack of readily usable data. Organizations collect large amounts of data, but must find, prepare, and protect the appropriate data for each AI/ML initiative.
• Disparate teams and technology. Slow, manual, and disconnected operations and infrastructure can impede collaboration between teams and AI/ML deployment across your organization.
• Delayed resource availability. Slow infrastructure and tool delivery can hinder model development, integration, and deployment within applications.

Even so, you can overcome these challenges by applying cloud-native application development approaches to your AI/ML life cycle.

A production-ready AI/ML architecture requires several key technologies and capabilities:

• AI/ML and MLOps tools allow data scientists, ML engineers, and application developers to create, deploy, and manage ML models and applications.
• A cloud platform gives data engineers, data scientists, ML engineers, and application developers access to the resources they need to work rapidly.
• Compute, storage, and network accelerators speed data preparation, model development, and inferencing tasks.
• Infrastructure endpoints provide resources across on-site, virtual, edge, and private, public, and hybrid cloud environments for all stages of AI/ML operations.
• Edge deployments (optional) provide large amounts of data from devices and sensors that can be used to train models and gain insights in real time.

This e-book reviews key considerations for building an effective AI/ML architecture.

Containers

A container is a basic unit of software that packages applications with all of their dependencies.

Data scientists, ML engineers, and application developers need access to their preferred tools and resources to be most productive. At the same time, IT operations teams need to ensure that resources are up to date, in compliance, and used in a secure manner. Containers simplify application build processes and allow applications to be deployed across different environments without change. They let you quickly and easily deploy a broad selection of AI/ML tools across hybrid environments in a consistent way. Teams can iteratively modify and share container images with versioning capabilities that track changes for transparency. Meanwhile, process isolation and resource control improve protection from threats.

Container orchestration

Container orchestration involves managing the creation, deployment, and life cycle of containers across your environment.

Once you adopt containers, you need a way to deploy, manage, and scale them efficiently. A container orchestration tool lets you administer the life cycle of your containers in a consistent way. These tools typically centralize access to compute, storage, and networking resources across on-site, edge, and cloud environments. They also provide unified workload scheduling, multitenancy controls, and quota enforcement.

Application life cycle management

Application life cycle management involves deploying, scaling, and administering applications that run within containers.

AI/ML environments are inherently complex. Container application life cycle management components that integrate with your container orchestration tool let you directly administer containerized applications, including AI/ML development tools. IT operations teams can automate common life cycle management tasks like configuration, provisioning, and updates to gain efficiency, speed, and accuracy. Data scientists, ML engineers, and application developers can use tools and applications from a preapproved service catalog — without needing to engage IT teams. Automation also frees staff from tedious tasks, allowing them to focus on more interesting strategic activities.

MLOps practices

MLOps practices bring together the tools, platforms, and processes needed to operationalize AI/ML at scale.

Organizations need to develop and deploy AI/ML models — and the applications that use them — quickly and efficiently. Collaboration across teams is critical for success in these efforts. Similar to DevOps, MLOps approaches foster collaboration between AI/ML teams, application developers, and IT operations to accelerate the creation, training, deployment, and management of ML models and ML-powered applications. Automation — often in the form of continuous integration/continuous delivery (CI/CD) pipelines — makes rapid, incremental, and iterative change possible for faster model and application development life cycles.

Hybrid cloud platform

A hybrid cloud platform provides a foundation for developing, deploying, and managing intelligent applications and models across on-site, edge, and cloud environments.

AI/ML models and intelligent applications require infrastructure for development and deployment. A consistent hybrid cloud platform allows you to develop, test, deploy, and manage models and applications in the same manner across all parts of your infrastructure. It gives you the portability, scalability, and flexibility to provision AI/ML environments on demand. It can also provide self-service capabilities to speed resource delivery while maintaining IT control. Finally, a consistent platform supplies a foundation for technology integrations from third-party vendors, open source communities, and any custom-developed tools you may use.

Edge deployments

Edge deployments are decentralized environments with devices that collect data and perform functions on site, outside your core datacenter.

Edge computing can deliver insights and experiences at the exact location and moment they’re needed. Sensors and devices often generate large volumes of data that can be used in AI/ML workflows for model training and runtime inferencing. This data can be difficult and costly to transmit to a central cloud in real time. For example, image recognition algorithms run more efficiently closer to the source of the data, removing the need to transfer large amounts of data for processing.

Start with an open, flexible foundation

Red Hat provides a complete technology portfolio, proven expertise, and strategic partnerships to help you achieve your AI/ML goals. We deliver a foundation for building production-ready AI/ML environments as well as services and training for rapid adoption.

Red Hat OpenShift® is a unified, enterprise-ready application platform for cloud-native innovation. On-demand compute resources, support for hardware and graphics processing unit (GPU) acceleration, and consistency across on-site, public clouds, and edge environments provide the speed and flexibility teams need to succeed. For example, you can create a self-service MLOps platform for data scientists, data engineers, and developers to rapidly build models, incorporate them into applications, and perform inferencing tasks. Collaboration features let teams create and share containerized modeling results with peers and developers in a consistent manner.

Red Hat OpenShift AI is a platform that help you train, serve, monitor and manage the lifecycles of AI/ ML models and applications. Red Hat OpenShift AI gives data scientists and developers a powerful AI/ML platform for gathering insights and building intelligent applications. Teams can move from experiment to production in a collaborative, consistent environment that integrates key certified partner offerings from NVIDIA, Intel, Starburst, HPE (Hewlett Packard Enterprise), and IBM to name a few.

The Red Hat Application Services portfolio helps you create a unified environment for application development, delivery, integration, and automation. Data integration services let you build effective data pipelines, while runtime services simplify application development. Process automation tools and services can access intelligent applications and ML models to automate business decisions.

Finally, Red Hat platform products — including Red Hat Enterprise Linux®, Red Hat OpenStack® Platform, and Red Hat OpenShift Platform Plus — provide a scalable, software-defined infrastructure.

Gain flexibility with a certified partner ecosystem

Red Hat’s certified partner ecosystem lets you integrate popular AI/ML, data analysis, management, storage, security, and development tools into this architecture. We work closely with partners to certify their software on our platforms for increased manageability, security, and support. Many partners also provide certified Red Hat OpenShift operators to simplify software life cycle management.

Choose your preferred products and technologies

Red Hat fosters a growing ecosystem of certified AI/ML partners, allowing you to incorporate popular products and technologies into your environment.

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AI/ML and MLOps are transforming nearly every aspect of business.

Red Hat can help you build a production-ready AI/ML environment that accelerates development and delivery of intelligent applications to support your business goals.

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