Large Language Models (LLMs) and Generative AI are all the rage right now but will only work for organizations that have a solid grasp on the quality of their data and the series of operations acting upon that data to augment the base LLM.
Eight Top DataOps Trends for 2022
DataOps adoption continues to expand as a perfect storm of social, economic, and technological factors drive enterprises to invest in process-driven innovation. From our unique vantage point in the evolution toward DataOps automation, we publish an annual prediction...
What Is ‘Equity As Code,’ And How Can It Eliminate AI Bias?
This article was originally published in Forbes. Engineers unleashed artificial intelligence (AI) bias, and it will be engineers who design the solutions that eliminate it. Authors of an article published by McKinsey Global Institute assert that “more human vigilance...
The DataOps Vendor Landscape, 2021
Download the 2021 DataOps Vendor Landscape here. Read the complete blog below for a more detailed description of the vendors and their capabilities. DataOps is a hot topic in 2021. This is not surprising given that DataOps enables enterprise data teams to generate...
Gartner: Operational AI Requires Data Engineering, DataOps, and Data-AI Role Alignment
Recommendations for Further Reading
Your Model is Not An Island: Operationalize Machine Learning at Scale with ModelOps
ModelOps orchestrates your entire ML pipeline for seamless integration of all the heterogeneous data centers, tools, infrastructure, and workflows required for successful model development, deployment, and production.
Deliver ML and AI Models at Scale with ModelOps
Data scientists work tirelessly to build and train a model then face the daunting challenge of deploying it into production. The model itself is only a fraction of the overall ML system.
Evaluating Machine Learning Models with MLOps and the DataKitchen Platform
Most projects naively assume that most of the time and resources will be spent in the “black box,” building the machine learning (ML) model, whereas a majority of the project time is actually needed in the green boxes – the ML system.
Why Are There So Many *Ops Terms?
A Guide to Ops Terms and Whether We Need Them It is challenging to coordinate a group of people working toward a shared goal. Work involving large teams and complex processes is even more complicated. Technology-driven companies face these challenges with the added...
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The Race For Data Quality in a Medallion ArchitectureNovember 5, 2024
