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AI-Enhanced Big Data: Integrating Private LLMs and Vector Databases
Wed, 23 April
In this talk, we explore the fusion of AI, particularly ChatGPT, with data-intensive architectures. The discussion covers the enhancement of big data processing and storage, the integration of AI into distributed data systems like Hadoop and Spark, and the impact of AI on data privacy and security. Emphasizing AI’s role in optimizing big data pipelines, the talk includes real-world case studies and culminates in a forward-looking Q&A session on the future of AI in big data.
This talk delves into the innovative integration of advanced AI models like ChatGPT into data-intensive architectures. We begin by discussing the significance of big data in modern business and the role of AI in scaling data solutions. Next, we address the challenges and strategies in architecting big data processing and storage systems, highlighting how AI models enhance data processing efficiency.
A significant portion of the talk focuses on distributed data systems and frameworks, such as Apache Hadoop and Spark, and how ChatGPT can be utilized within these frameworks for improved parallel data processing and analysis. The discussion also covers the critical aspects of data privacy and security in big data architectures, particularly in the context of integrating AI technologies like ChatGPT.
The talk further delves into best practices for managing and optimizing big data pipelines, with a focus on the role of AI in automating workflows, managing data lineage, and optimizing partitioning techniques. Real-world case studies will illustrate the successful implementation of AI-enhanced data-intensive architectures in various industries.
Target Audience: This session is primarily aimed at DataTech Professionals, as it focuses on big data architectures and how AI can enhance data processing, storage, and privacy. DeepTech Professionals are the secondary audience, as they will be interested in understanding the application of advanced AI models like ChatGPT in distributed data systems. Software Architects may also find value in this talk, as it covers architectural strategies for integrating AI into big data pipelines and ensuring data security and scalability.
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About the speaker
Rohit Bhardwaj
Director of Architecture, Expert in Cloud-native Solutions
Rohit Bhardwaj is a Director of Architecture working at Salesforce. Rohit has extensive experience architecting multi-tenant cloud-native solutions in Resilient Microservices Service-Oriented architectures using AWS Stack. In addition, Rohit has a proven ability in designing solutions and executing and delivering transformational programs that reduce costs and increase efficiencies.
As a trusted advisor, leader, and collaborator, Rohit applies problem resolution, analytical, and operational skills to all initiatives and develops strategic requirements and solution analysis through all stages of the project life cycle and product readiness to execution.
Rohit excels in designing scalable cloud microservice architectures using Spring Boot and Netflix OSS technologies using AWS and Google clouds. As a Security Ninja, Rohit looks for ways to resolve application security vulnerabilities using ethical hacking and threat modeling. Rohit is excited about architecting cloud technologies using Dockers, REDIS, NGINX, RightScale, RabbitMQ, Apigee, Azul Zing, Actuate BIRT reporting, Chef, Splunk, Rest-Assured, SoapUI, Dynatrace, and EnterpriseDB. In addition, Rohit has developed lambda architecture solutions using Apache Spark, Cassandra, and Camel for real-time analytics and integration projects.
Rohit has done MBA from Babson College in Corporate Entrepreneurship, Masters in Computer Science from Boston University and Harvard University. Rohit is a regular speaker at No Fluff Just Stuff, UberConf, RichWeb, GIDS, and other international conferences.
