It’s no secret that artificial intelligence (AI) and machine learning (ML) are used by modern companies for countless use cases where data-driven insights may benefit users. What often does remain a secret is how ML algorithms arrive at their recommendations. If asked...

With the rise of the data-centric AI movement (of which computer vision is a subset), the spotlight has been shifting from algorithm design to dataset development. Data is the highest contributor to model performance for many modern neural network architectures....

When an organization commits to DevSecOps, a fundamental shift takes place across teams. Security becomes everyone’s responsibility. From the beginning of the development cycle, code is reviewed, audited, and tested for security issues. Those issues can be resolved...

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...