oci generative ai Cloud Solution for Businesses

OCI Generative AI Cloud Solution for Businesses is a game-changer. It allows businesses to unlock the full potential of generative AI, with features like automated data labeling and model training.

With OCI Generative AI, businesses can reduce the time and cost associated with data labeling by up to 90%. This is because the solution automates the process, freeing up human resources for more strategic tasks.

The cloud-based solution is highly scalable, making it perfect for large enterprises with complex AI needs. It also provides a secure and compliant environment for sensitive data.

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Llama 3.1 is the latest release from Meta, bringing more flexibility, control, and state-of-the-art capabilities that rival the best closed source models.

This latest version is designed to make it easier to access and utilize its capabilities, without the need to manage containers or infrastructure.

You can access Llama 3.1 on OCI Generative AI, which means you don't have to worry about the technical details of setting it up.

Llama 3.1's state-of-the-art capabilities make it a powerful tool for a wide range of applications, from text generation to conversational AI.

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The OCI Generative AI service supports models like Meta's Llama 2 and Cohere's Command 52/6B models, offering a multilingual embedding capability for more than 100 languages.

This means you can use these models to create content in many different languages, making it a powerful tool for businesses with a global presence.

The service also includes improved GPU cluster management with multi-endpoint support and endpoint analytics features, making it easier to manage and monitor your AI models.

With OCI's Generative AI service, you can fine-tune models to fit your specific needs, enabling businesses to adapt AI to their unique requirements.

The service offers flexible fine-tuning options, allowing you to customize the models to suit your business needs.

Oracle's new OCI Data Science AI Quick Actions feature is a key new capability that simplifies accessing and deploying advanced models, making it easier for users without deep technical expertise to utilize state-of-the-art AI tools.

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This feature provides a user-friendly, no-code interface for model fine-tuning and deployment, reducing the complexity typically associated with these processes.

The service provides a curated list of pre-tested models, including LLMs, allowing users to quickly select and fine-tune the models that best fit their specific requirements.

This approach democratizes access to cutting-edge AI technologies and streamlines the workflow, integrating telemetry, visualizations, and execution processes into a comprehensive, easy-to-navigate ecosystem.

With oci generative AI, businesses can enter a new era of productivity by leveraging AI embedded across the full technology stack.

You can automate administrative tasks in the healthcare industry, freeing up staff to focus on more critical responsibilities.

Gather market analyses and automate blog writing to streamline your marketing efforts and save time.

By generating doctor discharge notes, healthcare organizations can improve communication speed and provide better patient care.

Oracle helps customers create personalized treatment plans, improving the overall patient experience.

Generative AI solutions can also generate logos and branded content, making it easier to create consistent and engaging marketing materials.

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Customer experience is the biggest cost center in any organization. With dozens of support engineers answering hundreds of chats per day, being able to automate time-consuming activities using generative AI, like summarizing support tickets, means our support engineers can instead spend those thousands of hours per year focused on increasing customer satisfaction and reducing time to results.

Urvashi Sheth, Chief Customer Officer at Intermedia, has seen firsthand how generative AI can improve customer experience. By automating tasks like summarizing support tickets, support engineers can focus on providing better service and increasing customer satisfaction.

Here are some key benefits of using generative AI for customer experience:

Customers are raving about OCI Generative AI, and for good reason. It's helping businesses like Singlife develop innovative solutions to everyday problems using emerging technologies.

Varun Mittal, Head of Innovation and Ecosystems at Singlife, says that Singlife collaborated with Oracle and OCI to develop proof of concepts for their internal competition, the #BetterIdea Challenge. These solutions cut across different domains, ranging from chatbot and document analysis to AI-assisted recommendations.

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Generative AI is being integrated across the Oracle ecosystem, from Autonomous Database to Fusion SaaS applications. This simplifies the process for organizations to deploy generative AI with their existing business operations, according to Ritu Jyoti, Group Vice President at IDC.

Automating time-consuming activities using generative AI can free up thousands of hours per year for support engineers to focus on increasing customer satisfaction and reducing time to results. Urvashi Sheth, Chief Customer Officer at Intermedia, knows this firsthand.

Oracle is taking a full stack approach to enterprise generative AI, directly connecting to customer business value. This is a game-changer for businesses looking to improve their customer experience, as noted by Dave Vellante, Chief Analyst at theCUBE Research.

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Oracle Devrel is making a big splash in the customer experience space. They've integrated generative AI into every layer of their stack, offering an end-to-end experience built on high-performing AI infrastructure.

This means users can expect a more intuitive experience, making AI more accessible and customizable according to specific business needs. Oracle's approach ensures that generative AI features are not just add-ons but deeply integrated into its technological offerings' fabric.

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Their cloud applications, such as Oracle Fusion Cloud Applications, Oracle NetSuite, and industry-specific applications like Oracle Health, now come embedded with generative AI capabilities. These capabilities focus on functionalities like summarization and assisted authoring.

Oracle's integration extends to its databases, including recent updates to Oracle Database 23c with AI Vector Search and MySQL HeatWave with Vector Store. This gives users a wide range of options, from dedicated AI clusters to fine-tuning models and even on-premises cloud deployments.

The OCI Generative AI Service sets the stage for AI solutions that put the customer's needs first. It's a carefully thought-out approach to generative AI that simplifies the AI integration process and enables businesses to leverage AI more effectively in their core operations.

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OCI Generative AI is built on top of the Oracle Cloud Infrastructure, allowing for seamless integration with other Oracle services.

The model is trained on a massive dataset of over 1 billion parameters, enabling it to generate highly accurate and context-specific text.

OCI Generative AI supports multiple languages, including English, Spanish, French, and more, making it a versatile tool for global applications.

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The OCI Generative AI API allows you to interact with custom model endpoints or use out-of-the-box models for tasks outside the OCI Platform.

You can access tutorial videos on how to set up the API, making it easier to get started with your project.

The API is designed to be flexible and efficient, giving you the freedom to customize your AI models to suit your specific needs.

To use the API, you'll need to set up your OCI config, which can be done through a demo video provided by Oracle.

By leveraging the OCI Generative AI API, you can unlock new possibilities for your business and take your AI applications to the next level.

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Dedicated Clusters are a key component of OCI's infrastructure, offering a unique combination of high-performance computing and low latency.

OCI's Dedicated AI Clusters are built on GPU-based compute resources, which provide a significant boost in processing power.

These clusters also feature an exclusive RDMA (Remote Direct Memory Access) cluster network, allowing for efficient data transfer between nodes.

With NVIDIA H100 clusters, you can achieve throughput of up to 3,200 GBPS, making them ideal for demanding workloads.

Low latency is also a key benefit, with some clusters offering latency as low as 2 microseconds.

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The generation model hyperparameters are a crucial aspect of fine-tuning your model for optimal performance. You can control the creativity of the output by adjusting the temperature, which can range from 1 to 5.

The temperature parameter is a key factor in determining the output's creativity. Lower temperatures make the output more deterministic, while higher temperatures make it more creative.

You can also use the Top K and Top P parameters to control the output's diversity. Top K picks the next token from the top K tokens based on probability, while Top P picks the next token based on the cumulative probability.

The Stop Sequence parameter allows you to control the length of the output by specifying a string that tells the model to stop generating more content. For example, if the stop sequence is a period (.), the model stops generating text once it reaches the end of the first sentence.

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Here are the key hyperparameters and their functions:

The Maximum Output Token parameter limits the number of tokens generated per response to up to 4,000 tokens.

The OCI Generative AI Service offers a range of embedding models, including Retrieval-Augmented Generation (RAG).

One of the embedding models available is Retrieval-Augmented Generation (RAG), which is a part of the OCI Generative AI Service.

To work with these models, you'll need to understand the basics of fine-tuning and inference, which are essential concepts in the context of the OCI Generative AI service.

Fine-tuning involves taking a pre-trained foundational model and providing additional training using custom data to create a custom model.

Here are some key facts about embedding models:

By using embedding models like RAG, you can create custom models that are tailored to your specific needs and tasks.

Fine-tuning configuration parameters is an essential step in creating a custom model. You can start with default values and adjust based on model performance.

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The key hyperparameters to consider are the controls for creativity, format, and extractiveness. These parameters help tailor the output to your specific needs.

Controls for creativity, default value is 1, with a maximum of 5, determine how deterministic or creative the output will be. Lower temperatures make the output more deterministic, while higher temperatures make it more creative.

The format parameter specifies the format of the summary, with options for free-form paragraphs or bullet points. This helps ensure the output is in a format that's easy to understand and use.

The extractiveness parameter controls how much of the original text is reused in the summary. High extractiveness reuses sentences verbatim from the input, while low extractiveness paraphrases more and relies less on direct quotes.

Here's a summary of the key hyperparameters to consider: