Welcome 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:
Gathering and analysis of data
Data transformation and preparation
Training & development of models
Models validation and serving
Monitoring and then re-training of models.
Why does business needs MLops?
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:
MLOps for deployment - without properly deployed models, we won’t see the maximum benefits they can bring. Problems such as too large models backlog, a lot of time needed by data scientists for troubleshooting models in the deployment phase, and the elevating model process being inaccurate can be worked around with MLops.
MLOps for Model Governance - as far as they can be seen as separate processes, their integration can provide well-built foundations, that may be used to deploy successful ML models.
MLOps for Life Cycle Management - problems with lack of updates while in production, lots of involvement of Data Scientists time for these updates and more things that MLOps will work out.
MLOps for Monitoring - monitoring models that are in production, the monitoring process of deploying models across the organization, centralizing the view of model functionality through all departments.
The Role of MLOps in ML.
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!)
Predictions in ML - problems may arise already at the online predictions level, resulting in the loss of effectiveness of the entire process! It is enough that the transferred data will be delayed, or the process will not cope with a series of inquiries? The implementation of MLOps should help avoid these errors in real-data AI-based applications.
Data trapped! - if data gets lost between silos, can't be connected, data scientists and other interested parties waste time searching for data, and data warehouses do not refresh at the same time causing differences that can affect decisions ... Your business needs MLOps.
That’s a lot of clouds! - Without a properly implemented MLOps solution, data access management can be difficult and problematic, and, by so, less efficient, and worse, there is a possibility that the joining of two databases on different clouds could be impossible. And, of course, your data Scientists will have to spend their precious time dealing with it.
Fresh data? - Time To Leave (TTL) means, in short, how long data is good, and it’s a big problem if you work on expired data. You need to have info about TTL, while building ML models, to work on data properly. As data volume and velocity continue to increase, enterprises need to find ways to manage their growing volumes efficiently on a big scale.
Data in trouble - well, maybe, not data but the models, that business uses. They should, no, they need to be monitored, their quality and performance must be checked and repaired. For example, Features monitoring allows to detect a bug and reverts features to the last correct version.
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.
Finally, if you want to know more, subscribe to our newsletter! We'll have a lot more to say about MLOps in business soon!
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26 July 2022
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