ARTICLES

How Can I Measure Data Quality?

How Can I Measure Data Quality?

Flag all your data quality issues by priority in a few lines of code “Everyone wants to do the model work, not the data work” — Google Research According to Alation’s State of Data Culture Report, 87% of employees attribute poor data quality to why most organizations...

AI regulations are here. Are you ready?

AI regulations are here. Are you ready?

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...

How to Prioritize Data Quality for Computer Vision: An Expert Primer

How to Prioritize Data Quality for Computer Vision: An Expert Primer

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....

DevSecOps: Top 3 tenets to elevate security

DevSecOps: Top 3 tenets to elevate security

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...

The New 5-Step Approach to Model Governance for the Modern Enterprise

The New 5-Step Approach to Model Governance for the Modern Enterprise

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,...

Everything You Need to Know about Drift in Machine Learning

Everything You Need to Know about Drift in Machine Learning

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 and Crowdsourcing: Using Human-in-the-Loop Labeling

AI and Crowdsourcing: Using Human-in-the-Loop Labeling

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...

Data-driven Retraining with Production Observability Insights

Data-driven Retraining with Production Observability Insights

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...

MLOps Beyond Training: Simplifying and Automating the Operational Pipeline

MLOps Beyond Training: Simplifying and Automating the Operational Pipeline

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...

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