Tutorial
5 min read

Power of Big Data: MLOps for business.

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:-

MLOps, what it is?

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.

MLOps implementation

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.

Did you like our post? If you want more, do not hesitate to download our free Ebook “MLOps: Power Up Machine Learning Process. Introduction to Vertex AI, Snowflake and dbt Cloud”.

Want to know more about MLOps?

Join our newsletter and do not miss anything!

The administrator of your personal data is GetInData Poland Sp. z o.o. with its registered seat in Warsaw (02-508), 39/20 Pulawska St. Your data is processed for the purpose of provision of electronic services in accordance with the Terms & Conditions. For more information on personal data processing and your rights please see Privacy Policy.

By submitting this form, you agree to our Terms & Conditions and Privacy Policy
big data
MLOps
MLOps Platform
implement MLOps
Introducing MLOps
MLOps process
26 July 2022

Want more? Check our articles

big data technology warsaw summit 2020 getindata
Big Data Event

Review of presentations on the Big Data Technology Warsaw Summit 2020

It’s been exactly two months since the last edition of the Big Data Technology Warsaw Summit 2020, so we decided to share some great statistics with…

Read more
getindata nifi ingestion universe made out flow files nifi architecture big data
Tutorial

NiFi Ingestion Blog Series. PART IV - Universe made out of flow files - NiFi architecture

Apache NiFi, a big data processing engine with graphical WebUI, was created to give non-programmers the ability to swiftly and codelessly create data…

Read more
transfer legacy pipeline modern gitlab cicd kubernetes kaniko
Tutorial

How we helped our client to transfer legacy pipeline to modern one using GitLab's CI/CD - Part 2

Please dive in the second part of a blog series based on a project delivered for one of our clients. If you miss the first part, please check it here…

Read more
podcast swedbank mlops cloud getindata
Radio DaTa Podcast

MLOps in the Cloud at Swedbank - Enterprise Analytics Platform

In this episode of the RadioData Podcast, Adama Kawa talks with Varun Bhatnagar from Swedbank. Mentioned topics include: Enterprise Analytics Platform…

Read more
getindata big data blog ml model mleap
Tutorial

Online ML Model serving using MLeap

Training ML models and using them in online prediction on production is not an easy task. Fortunately, there are more and more tools and libs that can…

Read more
getindata bigdatatech cfp
Big Data Event

How we evaluate the CfP submissions and build the conference agenda at Big Data Technology Warsaw Summit

Big Data Technology Warsaw Summit 2021 is fast approaching. Please save the date - February 25th, 2021. This time the conference will be organized as…

Read more

Contact us

Interested in our solutions?
Contact us!

Together, we will select the best Big Data solutions for your organization and build a project that will have a real impact on your organization.


What did you find most impressive about GetInData?

They did a very good job in finding people that fitted in Acast both technically as well as culturally.
Type the form or send a e-mail: hello@getindata.com
The administrator of your personal data is GetInData Poland Sp. z o.o. with its registered seat in Warsaw (02-508), 39/20 Pulawska St. Your data is processed for the purpose of provision of electronic services in accordance with the Terms & Conditions. For more information on personal data processing and your rights please see Privacy Policy.

By submitting this form, you agree to our Terms & Conditions and Privacy Policy