Google Data Studio on BigQuery - usage and cost control
Data Studio is a reporting tool that comes along with other Google Cloud Platform products to bring out a simple yet reliable BI platform. There are…
Read moreWelcome to the next instalment of the “Power of Big Data” series. The entire series aims to make readers aware of how much Big Data is needed and how popular it is becoming in the modern world. In an era in which information, and thus data, has become one of the most important fuels for business development, solutions in the field of management, analysis, storage and use of data have become indispensable.
Before we get into today's text, we encourage you to visit the previous parts of the series if you haven't already, where you can read about the various fields in which Big Data solutions have been beneficial:-
This time, however, we will not focus on how broadly understood Big Data solutions can benefit certain industries. Today we are addressing a specific Big Data part, MLOps, and we do it from a business perspective. We have already indicated in earlier parts of this series that more and more data appears with development. So much that without appropriate solutions it is impossible to take full advantage of the possibilities. This is where the role of MLops comes in - to release this data potential in ML processes.
But, before we’ll go further, let's ask a simple, but important question - what is MLops? It can be said that there is a set of rules and activities related to communication and cooperation between entities operating around Machine Learning. In truth, it can be described even simpler: “MLOps is responsible for optimizing and maintaining the maximum effectiveness of Machine Learning. MLOps is a set of practices which are the solution to ML challenges”.
If we were to describe the life cycle or phases of MLOps, the following could be mentioned:
So why does MLOps matter, and why does a machine learning business need it? Because with MLOps, the actions taken are smarter, and faster, and the cost of them is lower. And these are the three most fundamental issues in modern business. To the point, however, here are four things MLOps will be of help to:
So, above we indicated what MLOps can do for business, while below we present to you the moments when the implementation of MLOps processes into Machine Learning is essential! (if you want to achieve good results!)
We hope that the above article introduces you to the issue of the need to implement MLOps solutions in business, with particular emphasis on those businesses that have a real-time data management system, powered by A. MLoPS is undoubtedly a necessity in many cases of implementing ML processes.
Interested in ML and MLOps solutions? How to improve ML processes and scale project deliverability? Watch our MLOps demo and sign up for a free consultation.
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