MLOps is a new discipline. It is a core function of machine learning engineering focused on taking machine learning models to production and managing them. Once a Data Scientist comes up with an algorithm, or combination of algorithms to make a prediction, this algorithm along with a machine learning model must go into production.
What a gift: on 25th December 2022, Christmas Day for many people, the one millionth DataOps.live pipeline was run by the team at Roche Diagnostics.
CloudOps & DataOps Engineer Luke Crumley describes the lightbulb moment when he fully grasped the potential of DataOps to deliver new levels of speed, innovation and quality for business teams. Data engineering is about solving problems: helping organizations to make decisions based on facts rather than guesswork. As a result, businesses need increasingly powerful tools to help them to gather, sort, manage, manipulate and present insights to support those decisions – solutions like DataOps.live.
The toolset around implementing a Data Vault continues to grow. Once a decision has been made to implement Data Vault, there are still further choices to be made to make the Data Vault implementation successful. Using the DataOps.live Data Platform can make these choices simpler to implement. Our Data Platform can abstract the various tools such that the only thing needed in our platform is configuration information showing the tools what to do. The Automation of Snowflake is built into our platform. Running a pipeline will produce the proper environment around the Data Vault that it needs.
When you run a pipeline, you need a runner: a piece of code that does the job on your behalf. Someone has to set this up. It’s not very hard, of course but the problem is that you only have access to the resources of one machine where the runner runs. This brings limitations, including the size and cost of machine available to rent. It’s far from easy to scale up and down in a flexible way and the big ‘monster machines’ can get very expensive very quickly.
Should you include DataOps.live when evaluating Snowflake? Is it a platform you could benefit from? At what point does it make sense to take a serious look at DataOps.live?
When you think about Data Operations, or “DataOps” - what comes to mind?
This just in from the folks at Gartner: DataOps is a market! Wait, haven’t we known this all along? Didn't we name our company DataOps? Read on, and I’ll share my takeaways from this key milestone in one analyst firm’s coverage of the DataOps space.
And if you're in Orlando for the 2023 Gartner Data & Analytics Summit, happening March 20-22nd, swing by Booth #1528 to learn firsthand (I'll be there!) how you can build, test + deploy data products and applications on Snowflake!
DataOps.Live and ‘The Data Warrior’ lead the debate on the present and future of DataOps
Justin + Guy, founders of DataOps.live (The Data Products Company), have been thought leaders in the Data market since they started developing the DataOps philosophy and the DataOps.live SaaS platform over four years ago.