Hello Guy. We are elated to have you at AI Tech Park. Could you please tell us how DataOps.live came to fruition and give us a brief about your role as CTO at DataOps.live?

I’m delighted to be here! We came up with the inspiration for DataOps.live from our experience leading a tech-forward Systems Integration firm in England that developed advanced analytic solutions for larger enterprises, mostly using cloud data platforms and associated tools and infrastructure. In building these data applications, we noticed there was no methodology, such as how you would use DevOps to build software applications. So we came up with the concept of DataOps, which is essentially like DevOps for data. We further defined and established this concept by collaborating with a group of experts on a distinct website known as TrueDataOps.org, where we published the “7 Pillars of DataOps”. We also published the book DataOps for Dummies. And so DataOps.live was born!

What are the ways that can help Machine Learning practitioners to get better at understanding and grasping Data-Centric AI? How is DataOps.live implementing that?

Tools like DataOps.live, and Snowflake Data Cloud help machine learning practitioners operationalize augmented data management tasks. By blending data management with AI and ML workflows powered by Snowflake with the automation and lifecycle management of data products powered by DataOps.live, the practitioners can manage all their AI and ML workloads. That includes the management of data pipelines, automated data tests, the versionability of data and code, and the federated governance and compliance provided by the combined platforms. Tools like DataOps.live further offer real-time data pipeline observability, providing visibility into operations, identifying issues quickly, and troubleshooting them proactively before they hit production. Doing so ensures that high-quality data is always available for training.

Practitioners can rely on our augmented data management capabilities. Using Snowflake native features like data profiling and anomaly detection, DataOps.live can enforce quality checks, augment the development experience with its gen AI-powered copilot Assist, and use it, among other things, for synthetic data generation to improve model robustness.

Finally, you can benefit from feedback loops to continuously learn and improve your AI solution as a data team. Snowflake’s real time capabilities allow practitioners to incorporate feedback and adapt to changing data patterns. DataOps.live automates the training and retraining workflows by triggering updates when new data becomes available, or model performance is subpar.

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