Setu Kumar Chaturvedi

The comprehensive data engineering platform provides a foundational architecture that reinforces existing ops disciplines like DataOps, MLOps, and XOps under a single, well-managed umbrella. It enables continuous operation and data availability, as well as aids in compliance with governance and regulatory policies. The main aim of the union is the reliable and efficient deployment and maintenance of various solutions like machine learning systems, NLP Systems etc for the next generation of business digitalization. MLOps is a set of practices that combines Machine Learning, DevOps and data engineering. MLOps aims to deploy and maintain ML systems in production reliably and efficiently. DataOps helps data teams respond to changes automatically, make shifts to new cloud platforms, and handle breakage easily. When a business adopts a continuous operations strategy, it allows for changes within pipelines to be deployed automatically through on-premises and/or cloud platforms. The pipelines are also intentionally separated whenever possible, making them easier to modify. MLOps & DataOps with XOps data availability and an always-on Mission Control Panel continuous data observability eliminates blind spots, makes information within the data more easily understandable, and helps data teams comply with governance and regulatory policies.