Prasad Venkatachar
AI Solutions Director@ Vast Data
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Prasad Venkatachar is the Director of AI Solutions Engineering at Vast Data. He is focused on building AI Solutions by working with AI partners like Nvidia. Prasad Venkatachar is an IEEE Senior Member, BCS Fellow, serving as Conference Advisory Board for Future of Memory and Storage, Google Databases Partner Advisory and served as Lenovo Technology Innovation panel member and Microsoft Data and AI Partner Advisory Member. He has launched multiple industry-leading Data & AI/ML products & solutions collaborating with Google, Microsoft, IBM, Oracle, Cloudera, and ISV partners. As subject matter expert in Data and Ai filed served fortune 500 enterprise customers to deliver business value outcomes for Datacenter and Cloud deployments. He has good experience and certified in Multiple AI/Gen AI certifications from Google, Nvidia, Deep Learning and Cloud (AWS/Azure/GCP/IBM) Database (Oracle/DB2/Azure Data) and A regular speaker in Industry Conferences: Microsoft Ignite, Oracle Open World, Gartner Conference, Developer conferences: Pass Summit, Oracle users’ group, Percona live and SNIA, SDC, Future of Memory & Storage. Prior to Vast Data he worked at Pliops, Lenovo, Hewlett Packard Enterprise, Sandisk
Optimizing LLM Inference for Scalable Enterprise Applications
The growing adoption of large language models (LLMs) in enterprises has unlocked new opportunities. However, deploying LLMs at scale poses challenges related to latency, cost-efficiency, fine-tuning, privacy, and compliance. This paper explores strategies to enhance LLM inference for real-world enterprise use cases by focusing on optimizing model performance, reducing operational bottlenecks, and ensuring enterprise-grade security and governance.
Unified Enterprise Data Platform for AI Eta
In today’s data-driven landscape, organizations require a scalable, unified, and high-performance data platform to seamlessly manage diverse datasets across enterprise workloads such as AI/ML, Big Data analytics, HPC and business applications. This abstract outlines the design of an Enterprise Data Platform (EDP) delivering breakthrough data architecture for the modern enterprise. We will discuss customer benefits for use cases for AI/ML Model training and inference, Big Data Analytics and building Multi-Cloud strategy.
AI/ML Model Training and Inference: High-speed access to datasets accelerates AI pipelines, making VAST Data ideal for training large language models and visual AI solutions.
Big Data Analytics: Enables real-time analytics for financial institutions, retailers, and enterprises, delivering insights from vast amounts of data. Hybrid Cloud and Multi-Cloud Strategy: Seamlessly supports data mobility between edge, core, and cloud environments for global businesses.
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