Sayanti is a Data Science professional with over 18 years of experience in advanced analytics. At Merkle, Sayanti leads Analytics for Fortune 500 companies in industries such as Retail and Consumer Brands and e-commerce, helping various business functions drive impact and ROI and institutionalize analytics practices at scale.
By leveraging new machine learning and artificial intelligence techniques, Sayanti and her team have helped improve clients’ business performance. She has been guided by the belief that the triangulation of data, expertise, and technology, keeping the business problem at the core, can enable the efficient solving of any business problem and extract greater value from analytics. Holding a Masters in Economics, Sayanti’s analytics skills are rooted in a strong foundation of Econometrics.
In the rapidly growing landscape of Generative AI, businesses have been using it for unprecedented growth opportunities. However, with this potential comes the need to use it wisely and responsibly. To use GenAI effectively, we need to understand what it can do and what problems businesses might face around its capabilities and ethical considerations.
In this talk, we will discuss some opportunities to use Generative AI for business in line with their best fit applications to enhance real-time decision-making, while also shining a light on the critical nuances that should be considered before its deployment.
While GenAI is an exciting prospect, we will discuss scenarios where GenAI and its autonomous application are not advised and should be guardrailed with human augmentation, alternate modeling approaches, human validation, etc.. Such scenarios could include analyses revolving around sensitive customer information like geolocation & addresses, medical history, classification of themes/topics/intents in contact center conversations, financial forecasting, interpreting legal procedures, etc.
The goal is to empower the audience with the awareness & considerations necessary to harness Generative AI responsibly for their business towards a conscious fact-based decision on whether the applicability of GenAI is “To be or not to be.”
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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 MoreManaging 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