Data is continuing to prove to be a valuable asset for businesses of all sizes. I say that both from the fact that consulting firms like McKinsey have found that in their research companies that are using AI and analytics can attribute 20% of their earnings to it. Similarly, I have been able to consult for […]
Photo by Karo Kujanpaa on Unsplash By Travis Wolf Introduction — “What is MLOps? — DevOps for ML” — Kaz Sato Challenges arise as the production of machine learning models scale up to an enterprise level. MLOps plays a role in mitigating some of the challenges like handling scalability, automation, reducing dependencies, and streamlining decision making. Simply […]
Background In 2014 a group of Google researchers put out a paper titled Machine Learning: The High-Interest Credit Card of Technical Debt. This paper pointed out a growing problem that many companies might have been ignoring. Using the framework of technical debt, we note that it is remarkably easy to incur massive ongoing maintenance costs at the […]
Data warehousing was introduced in 1988 by IBM researchers Barry Devlin and Paul Murphy. Since then the concept has evolved and taken on a life of its own. Increasing challenges and complexities of business have forced data warehousing to become a distinct discipline. Over the years this has led to best business practices, improved technologies, and hundreds […]
There is a lot of talk about how data, data science and machine learning can all be applied to help make critical businesses decisions. As our team works with various companies across the US and in various industries, we have had a lot of opportunities to do more than just talk. We have helped many […]
Photo by Martin Adams on Unsplash Introduction Now that organizations are dealing with big data on a day-to-day basis to generate useful insights, we require more efficient software/data development lifecycles. The era of big data calls for some powerful data operation tools which can automate processes and reduce the cycle time of the data analytics for enormous datasets. […]
Photo by imgix on Unsplash Imagine what your organization would accomplish if it had accurate, detailed information regarding the processes, products, market, and customers. Well, this is the age of big data, and it demands organizations to bring data engineers and scientists on the same page to construct efficient and accurate insights to gain a competitive edge. A […]