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Praveen Malla

Lead Analyst

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Praveen Malla, a Lead Analyst at Merkle, with over 6 years of comprehensive experience in Generative AI, Data Science, and Engineering. His proficiency extends to hands-on industrial experience in both NLP and CV domains. Currently, at Merkle, Praveen is actively involved in projects in the Media & Advertising and UX research space, with a specialized focus on NLP and CV domains. His expertise is particularly evident in his development of the end-to-end Retrieval-Augmented Generation (RAG) pipeline, chatbots, and applications centered around NLP, including Sentiment Analysis, NER, and Keyword Extraction. Beyond his project work, Praveen has also made significant contributions to the knowledge sharing culture at Merkle. He has conducted workshops and sessions on cutting-edge topics such as Generative AI, Large Language Models (LLM), and RAG.

The field of Generative AI has seen rapid advancements, yet the challenge remains in effectively measuring and validating these systems’ outputs. This session provides a comprehensive overview of the evaluation techniques that are pivotal for Generative AI systems, particularly those involving retrieval-augmented generation (RAG). We will dive into the intricacies of retrieval evaluation, discussing key metrics that help assess the relevance and accuracy of information retrieved by AI. Following this, we transition into evaluating generative aspects, exploring how these metrics ensure the generated content meets the desired standards of coherence and relevance.

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More
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