Squark AI Tips Archive

Diving Deep into the AI RMF Core: A Comprehensive Guide – Part 5 of 6 Welcome back to our 6-part series on the Artificial Intelligence Risk Management Framework (AI RMF) on the Squark AI blog. As we’ve journeyed through this

Navigating AI Risk Management in B2B: The Effectiveness of the AI RMF, Part 4 of 6 In the intricate world of Business-to-Business (B2B) Artificial Intelligence (AI), understanding and managing risks is not just a best practice—it’s a business imperative. The

Understanding from the AI Risk Management Framework: Building Trustworthy AI, Part 3 of 6 In the world of Artificial Intelligence (AI), trust is a critical factor. The National Institute of Standards and Technology (NIST) has developed a comprehensive guide, the

Understanding the NIST Artificial Intelligence Risk Management Framework: The Audience. Part 2 of 6 In the rapidly evolving landscape of Artificial Intelligence (AI), risk management is a critical aspect that demands attention. The National Institute of Standards and Technology (NIST)

Understanding the NIST Artificial Intelligence Risk Management Framework: Part 1 of 6 The National Institute of Standards and Technology (NIST) released a comprehensive guide named the Artificial Intelligence Risk Management Framework (AI RMF 1.0), to help organizations navigate the complexities

Time Series Forecasting using Machine Learning. Addressing Risk, Enhancing Efficiency, and Navigating Future Pain Points in Media and Healthcare Time Series Forecasting using Machine Learning (ML) has witnessed a surge in its application across various sectors, not least the media

The Power of AI to Revolutionize Revenue Operations: What Executives Must Know to Choose the Right AI Vendor Revenue Operations (RevOps) holds the key to driving growth and maximizing profitability. As organizations seek innovative solutions to enhance their revenue generation,

Turbocharging Your Machine Learning & AI Projects with Squark’s Advanced & Automated Data Preperation, Cleaning, and Feature Engineering Data preparation and “feature” engineering is a critical aspect of any successful machine learning project, involving cleaning, preprocessing, and structuring the data.

Embracing the “Predict, Explain, Act” Cycle in AI for Customer Data Adopt the three-step cycle of “Predict, Explain, and Act” to better understand and benefit from AI. This cycle can be instrumental for executives who want to successfully leverage customer