ARTICLES

If you’re using machine learning to scale your business, do you also have a plan for Model Governance to protect against ethical, legal, and regulatory risks? When not addressed, these issues can lead to financial loss, lack of trust, negative publicity,...

What keeps you up at night? If you’re an ML engineer or data scientist, then drift is most likely right up there on the top of the list. But drift in machine learning comes in many forms and variations. Concept drift, data drift, and model drift all pop up on this...

AI today rests on three pillars – ML algorithms, the hardware on which they’re run, and the data for training and testing the models. While the first two pose no obstacle as such, obtaining high-quality up-to-date data at scale remains a challenge. One of the ways to...

We all know that our model’s best day in production will be its first day in production. It’s simply a fact of life that over time model performance degrades. ML attempts to predict real-world behavior based on observed patterns it has trained on and learned. But the...

The Evolving Meaning of ‘MLOps’ When you say ‘MLOps’, what do you mean? As the technology ecosystem around ML evolves, ‘MLOps’ now seems to have (at least) two very different meanings: One common usage of ‘MLOps’ refers to the cycle of training an AI model: preparing...

Version control is synonymous with software development. Most will automatically think of GitHub. Those superpowers gifted to software developers have made application builds quicker, more efficient, and more collaborative.  The database world has been slow to follow....

This blog highlights findings from Arize AI’s recent survey of ML teams. To see the full results, download a copy of the report. Compared to DevOps or data engineering, MLOps is still relatively young as a practice despite tremendous growth. While it’s tempting to...

According to the 2021 enterprise trends in machine learning report by Algorithmia, 83% of all organizations have increased their AI/ML budgets year-on-year, and the average number of data scientists employed has grown by 76% over the same period. However, the process...

There is a wide range of techniques that can be applied for detecting concept drift. Becoming familiar with these detection methods is key to using the right metric for each drift and model.  In the article below, I review four types of detection methods: Statistical,...

Photo by Markus Winkler on Unsplash Machine Learning (ML) has a number of applications in modern commerce, with Information Retrieval (IR) being one of the most common. Many e-businesses use it to gauge search quality relevance on their platforms to provide better...