Scaling generative AI: How companies are harnessing its power
Scaling generative AI: Benefits, risks, and limitations Before generative AI, traditional AI technology focused on solving well-defined problems. These traditional AI models were designed for specific tasks, such as text classification, entity extraction, and predictive modeling, limiting business applications to narrow domains. A glimpse back at McKinsey’s State of AI in 2022 reveals popular enterprise AI use […]
Addressing data challenges with AI-powered solutions
SigmaOnTopic: Unlocking the power of unstructured data What if you could have a search engine for your company’s internal data? That’s the idea behind SigmaOnTopic. This semantic search tool with advanced capabilities helps organizations explore their internal knowledge bases to recover precise and relevant information. Imagine you need to troubleshoot a recurring machine issue. Instead of […]
Training data for machine learning: Here’s how it works
Discover the crucial role of training data for machine learning. Learn how it’s used to teach algorithms to make predictions and decisions.
What is synthetic data? Types, challenges, and benefits
Learn about the different types of synthetic data, the hurdles in creating it, and how it’s revolutionizing industries.
Gen AI: Challenges and opportunities
A conversation on Gen AI challenges and opportunities with insights from Sigma’s Executive Senior Advisor, Dr. Jean-Claude Junqua.
The machine learning workflow: Key steps and best practices
Master the machine learning workflow with this guide. Learn key steps, best practices, and tips for building successful ML models.
Conversational AI: How it works, use cases & getting started
Conversational AI is the synthetic language and brainpower that makes human interactions with machines more effective and natural.
Data preparation 101
Data preparation is a critical step in ensuring data is clean, usable, and properly interpreted for use in machine learning.
What is human in the loop (HITL)?
Human in the loop machine learning refers to models that reinforce the information produced by ML with constant human influence.