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Analytics

Cloud Data Warehouse: A Comprehensive Guide

With the advent of modern-day cloud infrastructure, many business-critical applications like databases, ERPs, and Marketing applications have all moved to the cloud. With this, most of the business-critical data now resides in the cloud. Now that all the business data resides on the cloud, companies need a data warehouse that can seamlessly store the data from all the different cloud-based applications. This is where Cloud Data Warehouse comes into the picture.

LLM ChatBot Augmented with Enterprise Data

This video demonstrates how to use an open source pre-trained instruction-following LLM (Large Language Model) to build a ChatBot-like web application. The responses of the LLM are enhanced by giving it context from an internal knowledge base. This context is retrieved by using an open source Vector Database to do semantic search.

MLOps for Generative AI with MLRun

The influx of new tools like ChatGPT spark the imagination and highlight the importance of Generative AI and foundation models as the basis for modern AI applications. However, the rise of generative AI also brings a new set of MLOps challenges. Challenges like handling massive amounts of data, large scale computation and memory, complex pipelines, transfer learning, extensive testing, monitoring, and so on. In this 9 minute demo video, we share MLOps orchestration best practices and explore open source technologies available to help tackle these challenges.

ThoughtSpot Sage: data security with large language models

With the recent announcement of ThoughtSpot Sage, we launched a number of enhancements to our search capabilities including AI-generated answers, AI-powered search suggestions, and AI-assisted data modeling. In this article we will walk you through the steps we take to secure your data during the LLM interaction.

Overcoming Fear of the Unknown with Generative AI

The explosive rise of generative AI has prompted incredible excitement about its transformative potential, much like the advent of the Internet. But if like me, you’re old enough to remember what that looked like circa 1995, there was a lot we did not know at the time, creating uncertainty in both worlds of work and education on how to best leverage it, and whether providing unlimited access to employees or students was a good idea.

What are the Advantages of Automated Machine Learning Tools?

AutoML (Automated Machine Learning) helps organizations deploy Machine Learning (ML) models faster, by making the ML pipeline process more efficient and less error-prone. If you’re getting started with AutoML, this article will take you through the first steps you need to find a tool and get started. If you’re at an advanced stage, it will help you validate you’re on the right track.

Integrate.io vs. Monte Carlo

In today's fast-paced world, businesses generate massive amounts of data every second. With the sheer volume of data being produced, it's becoming increasingly difficult to monitor, detect and troubleshoot errors in real time. That's where data observability solutions come into play. Two of the most popular tools in this space are Integrate.io and Monte Carlo.