Here is a detailed summary of the video transcription in Markdown format, with the key takeaways broken down into sections:
Challenges of Deploying Generative AI at Scale
- The main objective is to achieve the required accuracy level for the business use case.
- Maintaining data pipelines and moving data in and out of the platform is a key challenge.
- Building these pipelines is complex, with the introduction of agents, tools, and connectors.
- Cost is a constant concern, especially for large-scale data ingestion, processing, and generating inference workloads.
- The fast-paced innovation in this space requires continuous updates and adapting to the changing landscape.
Nvidia's Solutions: Nemo and Nemo Retriever
- Nemo Retriever is a set of microservices for deploying, customizing, and scaling retrieval, including text embedding and re-ranking.
- Nvidia Inference Microservices (Nims) are optimized inference engines packaged in containers for deployment on-premises, in the cloud, or in hybrid environments.
- Nvidia's team has been leading in the MT Evaluation leaderboard for text retrieval models and has developed a high-performance vector search library, QVSS CUDA Vector Search.
- Nims offer high throughput, low latency, and easy scalability, along with the ability to customize models for specific domains and use cases.
- Nvidia provides Blueprints, which are reference architectures for building end-to-end pipelines for tasks like multimodal PDF ingestion and retrieval-augmented generation.
Cohesity's Kiya Gaia: Leveraging Enterprise Data for Generative AI
- Cohesity has built the Kiya Data Cloud, which includes four main pillars: data protection, data security, data mobility, and data access.
- Kiya Gaia is the first offering in Cohesity's Data Insights pillar, which leverages the enterprise data asset to drive operational insights and intelligence using generative AI.
- Kiya Gaia enables administrators to create data sets from Cohesity's data, apply fine-grain access controls, and use them with large language models for generation tasks.
- The solution integrates with Nvidia's Nemo Retriever and Nims to build a robust, secure, and scalable information retrieval pipeline.
- Kiya Gaia simplifies the deployment of generative AI capabilities, enabling organizations to leverage their existing data assets and drive faster decision-making.
Future Roadmap: Expanding into Multimodal Data and Ecosystem Collaboration
- Cohesity's vision for Kiya AI encompasses three pillars: platform, insights, and ecosystem.
- The platform AI focuses on using AI to manage and optimize the data platform, while insights AI leverages the data to drive rich insights.
- The ecosystem pillar aims to provide tools, platforms, and services to enable customers to bring AI to their businesses, including data curation, filtering, and model building.
- Upcoming developments include expanding the multimodal data capabilities, such as ingesting and processing audio, video, and images, and further integrating with the Nvidia ecosystem.