Decoding Gen AI From Prototype to Production

S1 E007

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DEVOPS

Nov 10, 2023

About Speaker

Shirsha Ray Chaudhuri

Director of Engineering at Thomson Reuters

Shirsha a prominent leader of the ML Engineering team at Thomson Reuters Labs in Bangalore, Shirsha spearheads the development and deployment of well-architected solutions on AWS and other cloud platforms for ML projects. Her focus is on enhancing efficiency and value across content workflows within Thomson Reuters products, platforms, and business systems. Shirsha is particularly passionate about the successful operationalization and sustained integration of ML solutions into live workloads. Actively engaged in AI for good initiatives and societal impact projects, she also contributes to the tech for diversity and inclusion space. Shirsha thrives on networking with individuals utilizing AI and modern technology to create a more inclusive, digital world, fostering collaboration for a better tomorrow.

About Host

Sathya Narayanan Nagarajan

Co-founder and CTO, Amnic

Sathya is an experienced technologist with over two decades in Artificial Intelligence (AI), Electric Vehicles (EV), and Distributed Systems. As the Co-founder and CTO of Amnic, he drives the development of a cloud Intelligence Platform, emphasizing efficiency, cost reduction, and reliability. Sathya's leadership spans roles at Ola Electric Mobility, Ola Cabs, Yahoo and many internet companies. With 11 patents in AI, EV, and Distributed Systems, he is committed to knowledge sharing and guiding industry thought leaders.

Summary of Podcast

In the "Decoding Gen AI in Production: From Prototype to Production" podcast, Shirsha Ray Chaudhuri, Director of Engineering at TR Labs, shares her background and journey in the field of AI. Starting as a software developer in telecom industry, she transitioned into data analysis and business intelligence as the nature of work evolved. Shirsha then focused on building platforms for cost-effective ML solutions and MLops. Now, she observes the evolution of AI from prototypes to production, expressing optimism about its potential impact.

The podcast explores various job profiles and industries that can benefit from generative AI, including data scientists, product designers, logistics experts, and more. Shirsha discusses the technical aspects, such as the debate between using pre-trained language models and fine-tuning, emphasizing the importance of evaluating and refining AI prototypes.

The speaker highlights challenges in ensuring accuracy in AI answers, particularly in computer vision, and stresses the importance of ongoing testing to build confidence in AI solutions. She discusses the need for checks and balances in critical industries like aviation to avoid miscommunications.

The podcast concludes with excitement about the evolving capabilities of large language models like GPT-3.5 and expectations for transformative use cases. Throughout, Shirsha underscores the importance of a testing ecosystem, scalability considerations, and ongoing learning to navigate the dynamic landscape of AI. The audience expresses gratitude for the insightful information, covering conceptualization, prototyping, and production of AI solutions.

About Amnic

Amnic is a cloud cost observability platform, helping businesses measure and rightsize their cloud costs. Amnic helps businesses visualize, analyze and optimize their cloud spends, in turn building a lean cloud infrastructure. Amnic offers out of the box solutions that help breakdown cloud bills and provide greater visibility and understanding into cloud costs along with recommendations to lower spends, alerts and anomaly detection.

Amnic delivers a wide range of features including K8s visibility, cost analyzer, alerts and custom reporting, budgeting, forecasting and smart tagging. DevOps and SRE teams rely on Amnic to deliver a simplified view into their cloud costs, allowing them to maintain governance and build a culture of cost optimization. Setup in 5-minutes and get 30-days of free trial.

Visit www.amnic.com to get started.

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