Operationalize AI with Red Hat AI
To operationalize AI is to create systems and processes that support the deployment and maintenance of AI solutions at scale. Operationalized AI borrows from machine learning operations (MLOps), and shares the goal of encouraging collaboration, automation, and continuous improvement.
With Red Hat® AI, organizations can create AI workflows that support their goals and provide their teams with tools that reduce model and operational deployment complexity.
How can operationalized AI help your organization?
In an enterprise setting, operationalizing AI simplifies lifecycle management for AI-enabled applications.
Automate complex processes
Automate tasks like fine-tuning a model on a set schedule or in reaction to new data coming in. This helps reduce or reverse model drift and keep your AI applications working with the most up-to-date information.
Additionally, automating and optimizing processes with operationalized AI helps organizations save on resources, such as self-service access to AI accelerators, which may have otherwise required time-consuming manual work.
Reduce future growing pains
Operationalizing AI means thinking 1 step ahead about how to set your team up for success in the future.
For example, making the move to scale out deployments – distributing workloads across multiple servers – prepares you for a future where multiple models are in production across different teams. Training and serving models in this distributed manner allows models to process requests quickly and simultaneously even when demand increases.
Create data science pipelines
As you roll out AI experiments into production, an operationalized framework helps record and manage changes to the models, data, and configuration files. This makes it easier for organizations to scale and apply learnings to other use cases and functions.
Plus, by operationalizing the way AI-powered applications perform in production, teams can more easily maintain standards across their organization and reduce variability.
Improve governance and compliance
Operationalized AI lets organizations enforce security measures and ensure compliance with data privacy regulations. Monitoring performance and accuracy with an operational framework also ensures that common challenges like hallucinations and trust can be tracked as users interact with your models. This continuous auditing helps models maintain a high level of quality and accuracy over time.
Why Red Hat AI?
Red Hat AI is a portfolio of open source tools and technologies that provide you with transparent and optimized solutions for managing the AI lifecycle.
Red Hat OpenShift® AI is part of this portfolio and offers MLOps tooling that supports deployment at scale.
Manage costs
Inference servers like vLLM help you optimize the power of graphical processing units (GPUs) and run large language models (LLMs) more efficiently. You can also apply LLM compressor algorithms to further reduce hardware costs, and run them on hardware accelerators of your choosing.
Plus, distributed serving (also available through vLLM) lets IT teams split model serving across multiple GPUs. This lessens the burden on any single server, speeds up training and fine-tuning, and makes more efficient use of computing resources.
Centralize collaboration
AI is an interdisciplinary field and requires multiple teams to work together. Red Hat OpenShift AI provides a consistent user experience for data scientists, data engineers, application developers, and DevOps teams to unite on a single platform. This means better collaboration, fewer errors, and faster time to market.
Supervise model output
Monitor deployed models for performance and accuracy with out-of-the-box visualizations. Or integrate with existing observability services to track performance, operations, quality, as well as bias and fairness metrics.
Plus, AI guardrails offer detection capabilities that help identify and mitigate sensitive content like profane speech, personal information, or other data defined by corporate policies.
Stay flexible
Red Hat AI provides users with the flexibility to choose where to train, tune, deploy, and run models and AI applications–on premise, in the public cloud, at the edge, or even in a disconnected environment. By managing your AI models within your environment of choice, you can control access, automate compliance monitoring, and enhance data security.
Red Hat Consulting Services and Support
Our engineering team is dedicated to helping you navigate our AI platform. From the operating system to the individual tools, we can provide the help you need to move your AI strategy forward.
Red Hat AI
Tune small models with enterprise-relevant data, and develop and deploy AI solutions across hybrid cloud environments.
Customer stories
Clalit
Clalit uses Red Hat AI to identify trends within 20 years of patient data to better understand disease behavior patterns and improve patient care.
Agesic
Agesic uses Red Hat AI to standardize and scale the use of AI across Uruguayan government agencies with a consistent, hybrid AI platform.
DenizBank
DenizBank uses Red Hat AI to provide a hybrid cloud environment that empowers data scientists to build and deploy more secure models and improve time-to-market.
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