Insight

Part 2: Three DataOps Challenges That Most Computer Vision Teams Struggle With

In this part 2, we will examine the three data-related challenges that any computer vision teams have to deal with. Furthermore, we will propose specific functions of an ideal DataOps platform in order to address these challenges.

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WHAT I LEARNED FROM ATTENDING REWORK MLOPS AND ML FAIRNESS SUMMITS

Last month, I attended two great summits organized by REWORK: The MLOps summit that discovers how to optimize the ML lifecycle & streamline ML pipeline for better production and the ML Fairness summit that discovers strategies to ensure ML models are accountable & fair to build secure & responsible AI. As a previous attendee of REWORK’s in-person summit, I have always enjoyed the unique mix of academia and industry, enabling attendees to meet with AI pioneers at the forefront of research and explore real-world case studies to discover the business value of AI.

Insight

Part 1: An Overview of DataOps For Computer Vision

Computer vision applications, in specific, and machine learning applications, in general, rely heavily on data to train the models. low-quality data can cause a massive negative impact on the system quality. Then, what solutions should we use to improve data quality?

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What I Learned From Attending TWIMLCON 2021

Learnings from a leading MLOps & Enterprise ML/AI virtual conference.