Flexible AI tools and seamless integration: How IBM Granite on AWS empowers businesses

Artificial intelligence (AI) adoption is accelerating across industries, with 72% of organizations integrating AI into at least 1 business function, according to a 2024 McKinsey survey.

This surge underscores the growing recognition of AI's potential to drive innovation and efficiency. IBM® Granite™ models, now integrated with Amazon Web Services (AWS), exemplify this trend by offering businesses robust AI capabilities tailored to diverse operational needs.

However, many companies face challenges in accessing powerful, flexible AI tools that integrate seamlessly with their existing infrastructure. IBM Granite models on AWS tackle these by merging cutting-edge AI capabilities with the vast AWS ecosystem, offering a transformative AI solution accessible to businesses of all sizes.

IBM has strategically positioned Granite models on the following key AWS services, broadening their accessibility and utility for enterprises:

• Amazon SageMaker: Granite models are available on Amazon SageMaker JumpStart, allowing AWS customers to deploy these robust, pretrained models with minimal setup. This lets businesses seamlessly embed IBM’s AI expertise into their AWS environment, enhancing operational efficiency and reducing setup time.
• Amazon Bedrock: For businesses looking to embed generative AI capabilities in applications, IBM Granite models are now accessible through Amazon Bedrock with custom model import capabilities. Developers can use the sophisticated AI capabilities of Granite models to build high-impact applications that meet enterprise-grade demands, such as customer service bots and intelligent document processing solutions.

The Granite family features models designed to support a wide range of enterprise needs, with models tailored to optimize specific tasks. These models include:

• General purpose models: The Granite models are versatile, supporting tasks such as Retrieval Augmented Generation (RAG), summarization and classification. This enables enterprises to streamline information retrieval, content generation and data analysis.
• Specialized models: Mixture of experts (MoE) and Guardian models provide enhanced capabilities, such as safety features and guardrails, ideal for regulated industries where compliance and accuracy are essential.

Imagine a large retail company using Granite models on Amazon SageMaker Jumpstart to gain real-time customer insights. By integrating customer interaction data with Granite’s advanced AI, the retailer can use RAG for better product recommendations, sentiment analysis and feedback categorization. This use case not only boosts customer engagement but also drives personalization at scale. This is a direct path to improved customer satisfaction and increased sales.

Consider a financial institution aiming to modernize its legacy systems by converting code from COBOL to Java. By deploying IBM's Granite models on Amazon SageMaker, the institution can automate significant portions of the code conversion process. This approach not only reduces manual effort but also minimizes errors, leading to faster project completion and cost savings.

By releasing Granite under an Apache 2.0 license, IBM empowers businesses with unmatched flexibility. Enterprises can customize Granite models, integrate them with proprietary data and build their own intellectual property on top of IBM technology.

Also, Granite models are optimized for fine-tuning with enterprise data, enabling companies to achieve competitive performance without needing massive models. This is ideal for companies wanting specific insights without the hefty compute costs of training large language models.

For instance, IBM and Red Hat® InstructLab technique enable businesses to fine-tune models, allowing them to integrate unique enterprise knowledge directly into AI-driven insights. This integration reduces costs while keeping proprietary data secure, a combination rarely achievable with traditional AI solutions.

IBM’s commitment to trustworthy AI is evident in its governance features, including:

• watsonx.governance™: Integrated with Amazon SageMaker, this end-to-end toolkit for AI governance helps manage risk and compliance across the entire AI lifecycle.
  
• IP indemnity: IBM offers IP indemnity for all Granite models on watsonx.ai™, giving enterprises confidence when merging their data with these models. This level of assurance is crucial for businesses wary of data security in AI deployments.

The Granite on AWS partnership aligns with IBM’s broader strategy of creating a robust, interconnected AI ecosystem. These ecosystem efforts include:

• Collaborations with other tech giants: IBM has partnered with companies like Salesforce and SAP to extend the availability of Granite, allowing even broader AI access for enterprises across different platforms.
• IBM Consulting®: IBM Consulting services provide expert guidance to clients, helping ensure maximum return on investments (ROI) on generative AI initiatives through strategic use of Granite models.

With Granite on AWS, IBM has created a bridge between powerful AI capabilities and cloud infrastructure, democratizing enterprise-grade AI. From retailers to financial services, organizations are harnessing this partnership to gain actionable insights, automate complex processes and drive growth in ways that were previously out of reach.

By combining Granite’s AI strength with AWS’s scalable platforms, this partnership is shaping the future of enterprise AI, 1 use case at a time.

Whether your business is looking to enhance customer experiences, streamline workflows or improve decision-making, IBM Granite on AWS offers an accessible, flexible and secure path to realizing AI’s full potential.

To see firsthand how AI can redefine your business, don’t miss AWS re: Invent. Be among the first to experience how IBM Granite models on AWS are empowering businesses to achieve more.

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