Read Part 2 and Part 3 In the evolving landscape of artificial intelligence, combining advanced techniques like Retrieval-Augmented Generation (RAG) and Named Entity Recognition (NER) has opened new avenues for extracting and structuring information from complex documents. This blog delves into the intricacies of building a 10-Q Analyzer—a tool I designed to process SEC 10-Q … Continue reading Building a 10-Q Analyzer: Part 1 | Extracting Financial Insights with AI
Category: machine learning
Data Science Leadership Series – Part 3: Building Great Products by Balancing Product and Engineering Mindsets
I wrote about why we need AI Product Management when building complex data products in a previous post in the Data Science Leadership Series, and I feel this has to be followed by a very important topic that almost every single software development team faces, especially in high growth startups where the speed of innovation … Continue reading Data Science Leadership Series – Part 3: Building Great Products by Balancing Product and Engineering Mindsets
The Game Changer: Large Language Models Unleashing the Power of AI in Cybersecurity
Applied Machine Learning and AI in cybersecurity is slowly growing into mainstream industry, not just as an add-on anymore. As threat actors have started automating and streamlining a lot of their attack pipelines using off the shelf Large Language Models, the good actors are essentially forced to catch up with them ! It’s not just … Continue reading The Game Changer: Large Language Models Unleashing the Power of AI in Cybersecurity
Data Science in Venture Capital – An Overview
I have been recently doing some research on applied Machine Learning and AI in Venture Capital (VC) funds, and it's a really interesting rabbit hole to go into, especially if you yourself are interested in either startups (as a would-be founder / employee) or in VCs themselves as to how they work as data driven … Continue reading Data Science in Venture Capital – An Overview
Data Science Leadership Series – Part 2 : How to choose data projects: Core Product Vs Support Consulting Vs Research | Beware of your bottomline
The biggest challenge for data scientists / managers in decision making capacity and one with the biggest consequential outcome for both the business and the data team, I feel, is the part where you say yes / no / let’s modify - to a new data project idea from leadership, or even starting a new … Continue reading Data Science Leadership Series – Part 2 : How to choose data projects: Core Product Vs Support Consulting Vs Research | Beware of your bottomline
Data Science Leadership Series : Part 1 – The need for AI Product Management
People keep talking about data science being such a rewarding and lucrative career, but I feel it's time to talk about the serious gaps that's been bugging this field, in terms of operationalizing a successful data teams. Few tech companies like the Googles and Microsofts of the world have got this working like a well-oiled … Continue reading Data Science Leadership Series : Part 1 – The need for AI Product Management
The Power of Distributed XGBoost: Efficient and Cost-Effective Training for Petabytes of Data
In the era of big data, managing and processing large volumes of information is a challenge faced by many organizations. As a data science professional, one must constantly explore innovative techniques to extract meaningful insights from massive datasets. You would be surprised how many data teams in really large organizations still default to neural network … Continue reading The Power of Distributed XGBoost: Efficient and Cost-Effective Training for Petabytes of Data
Infinite Couch 