Documenting Model Development in the Banking Industry

Vectice + Git: Documenting ML Beyond the Code

Git is the best tool for keeping track of code changes…

But AI projects are more than just code — they rely heavily on datasets, models, and computational graphs. While Git excels at managing code, it falls short when it comes to tracking these…

The Ultimate Guide to Data Science Frameworks

Data science is an ever-evolving field that uses various tools and techniques to…

How to Use Status Reports and Project Reviews in ML

‍‍Status reports and project reviews are two management tools that drive progress and increase the team learning curve after project completion. In this article, we’ll provide some best practices and apply these tools to machine learning…

White Paper: Model Risk Management

The rapid adoption of machine learning (ML) models in financial institutions has brought new challenges in model risk management (MRM). This paper addresses the key challenges financial institutions face in managing model risk and introduces a solution called…

The AI Act passed. Here’s what’s next.

The EU AI Act is the first major law to regulate AI/ML models and has major implications for organizations operating within the EU or providing AI technologies to EU markets. Compliance with the act is mandatory, and non-compliance will result in significant…

Deliver AI Faster With The [2x3] Methodology

How to Future-Proof Your AI Projects and Implement Governance

MLflow & Vectice: Auto-Documenting Beyond Model Data

White Paper: Evaluating Data Science Infrastructure During M&A Deals

In the world of mergers and acquisitions, the game is changing. As businesses increasingly recognize the value of data and AI, evaluating a company’s data infrastructure has become a pivotal aspect of any…