Discover the Future of AI at Red Hat's OpenShift Commons Gathering in Raleigh
- Introduction
- The Growing Influence of AI in Various Industries
- The Importance of AI in Today's Business Landscape
- Exploring the Four Steps of Foundation Model Process
- 4.1 Initial Training of the Model
- 4.2 Fine-tuning and Prompt Tuning
- 4.3 Optimization and Customization
- 4.4 Serving the Model
- Challenges Faced in Operationalizing AI Models
- How Red Hat is Addressing the Challenges
- 6.1 Collaboration with IBM and Open Source Communities
- 6.2 Introducing OpenShift AI for Training and Validation
- 6.3 Achieving Low Latency and Scalability in Model Serving
- Partner Integrations for Enhanced Capabilities
- 7.1 Starburst for Federated Data Access
- 7.2 Watson X for Curated AI Models and AutoML
- 7.3 NVIDIA and Intel Collaborations for GPU Support
- Strategy for Generative AI and Foundation Models
- 8.1 Simplifying Complexity with Open Data Hub
- 8.2 Supporting Bring Your Own Model Use Cases
- 8.3 Extending the Light Speed Story Throughout the Infrastructure
- Conclusion
The Growing Influence of AI in Various Industries
In today's digital age, artificial intelligence (AI) has become ubiquitous, finding its way into almost every sector, be it telecommunications, healthcare, or automotive. Executives across industries are recognizing the necessity of implementing AI to gain a competitive edge. The increasing pervasiveness of AI in our daily lives signifies a shift from it being solely a commercial enterprise to an integral part of society. It is no longer confined to research labs, but rather embedded in our culture, with businesses building products and establishing themselves in the field.
AI's exponential growth has been particularly evident in the adoption of generative AI, specifically foundation models. These models serve as the building blocks for AI applications and have found their way into a wide range of enterprises. According to a survey, almost 30% of organizations have employees experimenting with generative AI, while over 5% have one or more generative AI use cases in production. These numbers are only set to rise as organizations become more acquainted with foundation models' potential.
The Importance of AI in Today's Business Landscape
Operationalizing AI models is a complex task that many organizations struggle with. Building the model itself is not the most challenging part; rather, it requires a wide array of complementary processes. From feature extraction and data collection to verification, serving infrastructure, and monitoring, a multitude of steps must be coordinated effectively. A survey reveals that more than 50% of respondents have faced difficulties in operationalizing their models. This points to the need for comprehensive solutions that address not only the model creation but also the associated complexities.
Recognizing the challenges faced by enterprises, Red Hat has been developing strategies to assist customers in navigating the AI landscape. Leveraging its expertise in infrastructure, open-source technologies, and Kubernetes, Red Hat aims to simplify the operationalization process. By providing a stack that streamlines the complexities surrounding foundation models, Red Hat empowers organizations to leverage AI effectively.
Exploring the Four Steps of Foundation Model Process
Foundation models serve as the basis for generative AI, powering a myriad of AI applications. Understanding the key steps involved in the foundation model process is crucial for organizations looking to harness the potential of AI. The process can be broken down into four main steps:
4.1 Initial Training of the Model
The first step in the foundation model process is the initial training of the model. This involves building the foundation model from scratch, utilizing large-Scale datasets and advanced algorithms. The training phase is resource-intensive and requires significant computational power.
4.2 Fine-tuning and Prompt Tuning
Once the foundation model is created, fine-tuning and prompt tuning are necessary to optimize its performance. Fine-tuning involves refining the model Based on specific parameters or objectives to achieve better results for a particular use case. Prompt tuning, on the other HAND, focuses on improving the model's ability to generate desired outputs based on formulated Prompts.
4.3 Optimization and Customization
The next step involves optimizing the foundation model further and customizing it to suit specific requirements. This may involve leveraging labeled data, parameter optimization, and parameter control to transform the generic foundation model into a tailored solution.
4.4 Serving the Model
The final step in the foundation model process is serving the model. This involves deploying the model into production environments, making it accessible to applications and users. Efficient serving mechanisms are crucial to ensure low latency, high scalability, and real-time inferencing capabilities.
Challenges Faced in Operationalizing AI Models
The operationalization of AI models presents several significant challenges for organizations. According to a survey conducted, over 50% of respondents revealed struggles in operationalizing their models. The complexity lies not only in model creation but also in managing the surrounding infrastructure, data collection, data verification, and monitoring processes.
The complexity further increases when bridging the gap between data scientists and application developers. Data Extraction, feature engineering, and model deployment need to be seamlessly integrated to ensure successful implementation. This necessitates an end-to-end workflow that encompasses the entire lifecycle of AI models.
Operationalizing AI models also demands scalability and low-latency inferencing capabilities. Responding in real-time to user requests and ensuring minimal processing time requires robust serving infrastructure capable of handling massive workloads. This includes efficient resource allocation, workload scheduling, and dynamic scaling mechanisms.
How Red Hat is Addressing the Challenges
Recognizing the multifaceted challenges associated with operationalizing AI models, Red Hat has taken a proactive approach by developing solutions that simplify the process for enterprises. Leveraging its extensive knowledge in infrastructure, Kubernetes, and open-source technologies, Red Hat aims to empower organizations to navigate the complexities of AI implementation effectively.
6.1 Collaboration with IBM and Open Source Communities
Red Hat has collaborated with IBM and other open-source communities, such as Hugging Face, to build a comprehensive stack that facilitates the creation, training, and fine-tuning of foundation models. This collaboration aims to provide customers with the necessary tools to develop their own foundation models, as well as access curated AI models offered by IBM.
6.2 Introducing OpenShift AI for Training and Validation
Red Hat's OpenShift AI acts as a foundational layer for AI implementation. Built on top of OpenShift, it enables customers to leverage Kubernetes and OpenShift's capabilities to maximize their investment in AI. OpenShift AI provides the infrastructure, orchestration, and distributed workload management necessary for efficient training and validation of foundation models.
6.3 Achieving Low Latency and Scalability in Model Serving
To address the challenges of serving AI models with low latency and high scalability, Red Hat has focused on developing efficient serving mechanisms. Partnering with IBM and other technology giants like NVIDIA and Intel, Red Hat has integrated GPU acceleration, optimized serving engines, and scalable serving runtimes into OpenShift AI. This ensures high-performance inferencing, enabling real-time decision-making.
Partner Integrations for Enhanced Capabilities
Red Hat has formed strategic partnerships with industry leaders to enhance the capabilities of its AI implementation stack. These partnerships facilitate seamless integration with complementary tools and technologies, making it easier for customers to leverage AI effectively.
7.1 Starburst for Federated Data Access
Red Hat's collaboration with Starburst aims to address the challenges of data access and governance in AI implementations. Starburst provides a technology called Trino, which enables federated access to data scattered across different data sources and data lakes. This allows organizations to overcome data silos and leverage data effectively in their AI models.
7.2 Watson X for Curated AI Models and AutoML
Working closely with IBM's Watson X, Red Hat enables customers to access curated AI models that streamline specific use cases. Watson X offers tools like IBM Code Assistant, Prompty Lab, and Tuning Studio, making it easier for citizen data scientists and business users to leverage AI without extensive knowledge of model building. Additionally, Watson X provides AutoML capabilities to assist users in automating the model creation process.
7.3 NVIDIA and Intel Collaborations for GPU Support
Red Hat's collaborations with NVIDIA and Intel provide GPU acceleration and support for organizations that require high-performance computing for AI workloads. These partnerships enable customers to leverage GPUs for training and inference tasks, ensuring efficient and scalable model processing. This integration extends to serving engines and runtimes, maximizing the potential of GPU resources.
Strategy for Generative AI and Foundation Models
Red Hat has outlined a clear strategy for enabling customers to harness the power of generative AI and foundation models effectively. This strategy revolves around simplifying complexity, providing support for bring-your-own-model use cases, and extending the light speed story throughout the infrastructure.
8.1 Simplifying Complexity with Open Data Hub
Red Hat realizes that operationalizing AI models involves various complexities. To simplify this process, Red Hat has developed the Open Data Hub, an open-source project that serves as the Upstream for OpenShift AI. This project allows customers to leverage the power of open-source technologies, such as Kubernetes and OpenShift, while integrating seamlessly with the Red Hat stack.
8.2 Supporting Bring Your Own Model Use Cases
Red Hat acknowledges that many organizations have invested substantial efforts in building their own AI models. To cater to these "bring your own model" scenarios, Red Hat provides the necessary infrastructure, tools, and frameworks to deploy, fine-tune, and operationalize custom models within the OpenShift AI environment. This empowers organizations to leverage their existing AI investments while benefiting from Red Hat's comprehensive stack.
8.3 Extending the Light Speed Story Throughout the Infrastructure
Red Hat aims to harness the power of generative AI and foundation models across other areas of infrastructure beyond anable light speed. The ability to generate descriptive YAML from foundation models opens up opportunities to leverage generative AI for other features and components within the Red Hat ecosystem. By incorporating generative AI capabilities into more products, Red Hat aims to provide customers with an extensive range of AI-powered solutions.
Conclusion
As AI continues to revolutionize industries, organizations must navigate the complexities of operationalizing AI models effectively. Red Hat, in collaboration with IBM and other partners, is at the forefront of simplifying this process. Red Hat's OpenShift AI provides a comprehensive stack for training, validating, and serving foundation models, while strategic partnerships enhance its capabilities.
With the growing pervasiveness of generative AI and foundation models, Red Hat's focus on simplifying complexity, supporting custom models, and extending AI across the infrastructure positions it as a trusted partner for organizations seeking to leverage the power of AI. By offering solutions that address the unique challenges faced by enterprises, Red Hat empowers businesses to harness AI's potential for innovation and competitive AdVantage.
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