Abhishek Mishra的动态

A private SLLM-based GPT like #SCOTI from smartR AI can help with messy data by cleaning, structuring, and analyzing it. It can extract relevant information, identify patterns, and generate summaries or reports, all within a secure and confidential environment. Isn't it time to think smartR AI

Great summary detailing why data pipelining is a key building block in todays AI hungry marketplace. A reminder that there are no shortcuts when it comes to building scalable AI solutions that actually solve industry problems. https://lnkd.in/dPrvM9mg

It always matters knowing how to read/interpret data :-)

Solved Big data is so large that it might take two or three | Chegg.com: Your solution's ready to go! Enhanced with AI, our expert help has broken down your problem into an easy-to-learn solution you can count on. See?... #bigdata #cdo #cto

Data = Information = Business Strategy = Competitve Edge

Did you miss Max Zito presentation on Pyramid Analytics at Big Data LDN. Or maybe you want to watch it again ?? Either way, please take a moment to watch the below. ?? Overview of Pyramid ? ?? Building analytics using your voice ? ?? Self Services in an embedded environment? #AI #GenAI #BGenBI #Analytics #Pyramidanalytics

Don't Wait for Perfect Data: Start Now & Continuously Improve Your AI! Join Zehra Cataltepe,? CEO & Co-Founder at TAZI, and Samantha Lewsley, Ex-Director of Customer Engagement and Operations at a Midsize US Insurance company, as they explore the profound impact of AI technologies in transforming the insurance sector. As Samantha Lewsley says: "You don't need perfect data to get started with AI. Use what you have, learn from it, and refine your model as you go!"?? https://bit.ly/3yeZXuh For the full video, see the link in the comments.???? Ready to take the first step? We can help!?? ?https://bit.ly/3q0XCyN ?? #AI #MachineLearning #DataScience #TAZI

Deliver Your Data as a Product, But Not as an Application Data as a product is an intriguing concept, but beware of the application trapContinue reading on Towards Data Science ?... https://lnkd.in/eruKHpAA #AI #ML #Automation

Here's a fun paradox: AI needs data to learn, but these days, AI has the capacity to produce data as well. How does that work? If you train a different model using this data, how does it affect performance? What about bias? One of the biggest challenges to solve while building a synthetic data company is ensuring that the data you produce (regardless of how you produce it) is reliable and accurate. While we work on maximising how much your models benefit from our synthetic data, take a moment to understand what exactly we mean when we say "synthetic". Here is a great video by IBM explaining what synthetic data actually means, and no, its not "fake" data!

What Is AI-Ready Data? And How to Get Yours There