Systems | Development | Analytics | API | Testing

January 2024

GenAI for Financial Services - MLOps Live #25 with McKinsey

Generative AI has sparked the imagination with the explosion of tools like ChatGPT, CodePilot and others, highlighting the importance of LLMs as the basis for modern AI applications. However, implementing GenAI in the enterprise is challenging, and it becomes even more difficult for banks, insurance companies, and other financial services companies. Many Financial Service companies are struggling and end up missing out on the great value of GenAI and the competitive edge it can provide.

Implementing Gen AI in Practice

Across the industry, organizations are attempting to find ways to implement generative AI in their business and operations. But doing so requires significant engineering, quality data and overcoming risks. In this blog post, we show all the elements and practices you need to to take to productize LLMs and generative AI. You can watch the full talk this blog post is based on, which took place at ODSC West 2023, here.

How Sense Uses Iguazio as a Key Component of Their ML Stack

Sense is a talent engagement platform that improves recruitment processes with automation, AI and personalization. Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization, including a large number of data and data science professionals. This includes a data team, an analytics team, DevOps, AI/ML, and a data science team. The AI/Ml team is made up of ML engineers, data scientists and backend product engineers.

How HR Tech Company Sense Scaled their ML Operations using Iguazio

Sense is a talent engagement company whose platform improves the recruitment processes with automation, AI and personalization. Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization including a large number of data and AI professionals. This includes a data team, an analytics team, DevOps, AI/ML, and a data science team. The AI/Ml team is made up of ML engineers, data scientists and backend product engineers.