Global MLOps Market Size By Industry Vertical (BFSI, Media And Entertainment), By Component (Platform, Software), By Deployment Mode (On-premise, Cloud), By Organization Size (Large Enterprise, Smes), By Geography Scope And Forecast

Published Date: August - 2024 | Publisher: MIR | No of Pages: 320 | Industry: latest updates trending Report | Format: Report available in PDF / Excel Format

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MLOps Market Size And Forecast

MLOps Market size was valued at USD 1,902.50 Million in 2023 and is projected to reach USD 23,945.95 Million by 2030. The Market is projected to grow at a CAGR of 37.22% from 2024 to 2030.

Improved efficiency through increased monitorability and increased productivity and quicker ai implementation are the factors driving market growth. The Global MLOps Market report provides a holistic market evaluation. The report offers a comprehensive analysis of key segments, trends, drivers, restraints, competitive landscape, and factors that are playing a substantial role in the market.

Global MLOps Market Introduction

In recent years, the field of machine learning (ML) has undergone rapid advancements, ushering in a new era of possibilities and applications across various industries. However, with the proliferation of ML models, the need for effective deployment and management has become increasingly evident. This is where MLOps, or Machine Learning Operations, emerges as a crucial discipline, providing a systematic approach to streamline the end-to-end lifecycle of machine learning models.

MLOps can be defined as a set of practices and tools that seek to enhance and automate the processes associated with deploying, managing, and monitoring machine learning models in a production environment. It acts as a bridge between the traditionally separate domains of data science and IT operations, ensuring a seamless transition from model development to deployment and maintenance.

MLOps? It's pretty much everywhere you look in the machine learning world, from when a model's just an idea to keeping it running smoothly after it's live. It's all about getting data scientists, software folks, and ops teams working together. Think of it as making sure everyone's on the same page, so your fancy model actually works in the real world. Like coding, version control is super important. It helps you keep track of all the tweaks you make to your code and your data, so you can always go back if something breaks. MLOps also uses CI/CD to automate all the boring stuff, like testing and getting your models out there. That means you can update your models faster and without pulling your hair out. Plus, it treats your infrastructure like code, so you can easily spin up and manage all the servers and stuff you need.

MLOps helps you keep an eye on your models in the real world, watching how they're doing and spotting when things start to drift. It also helps you manage all those different versions you might have. This way, your models keep giving you those accurate and reliable predictions, even when things change around them. Need to make your ML systems bigger? MLOps has your back! It helps manage resources super efficiently, like getting the most out of your computing power, storage, and everything else you need, no matter how busy things get. And because everyone's worried about keeping data safe, MLOps makes sure security is built right into the process. That means both your data and models are following the rules and keeping sensitive info locked down. Finally, MLOps wants you to keep learning and improving. It helps you set up feedback loops so you can tweak your models based on what's happening out there and what users are saying. It's all about making your models more adaptable and better over time.

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Global MLOps Market Overview

In the dynamic landscape of machine learning (ML), where teams of data scientists, engineers, and operations professionals collaborate to bring models from development to production, the standardization of ML processes plays a pivotal role. This trend towards standardization not only enhances teamwork but also serves as a market driver for the MLOps sector.

Think of standardization as your ML team's secret weapon for keeping things consistent! It basically means everyone's on the same page, cutting down on mistakes and making it way easier to do things again and again. This is super important when you've got lots of people working on different parts of a project. Imagine data scientists and IT folks using the same version control system – that can stop a ton of headaches when it's time to launch that model. Reproducibility is a big deal in science, and ML is no different. By standardizing things like data prep, model training, and how you measure results, your team can actually reproduce their findings. This is key for proving your model works, running experiments, and even just getting everyone to work better together.

While the field of MLOps is gaining traction as an essential component for successfully deploying machine learning (ML) models, the market faces a significant restraint – the lack of expertise among personnel. This challenge revolves around the scarcity of skilled professionals who possess the interdisciplinary knowledge required to navigate the complexities of MLOps effectively.

MLOps is all about juggling a lot of different things – getting the data ready, training your models, putting them to work, keeping an eye on them, and always trying to make them better. But, if your team doesn't have the right skills, things can get tricky when trying to make all these moving parts work together. Think about itYou need your data science team and your IT folks to play nice, and that means knowing a bit about what each other does; otherwise, you're looking at a lot of wasted effort. And let's not forget about model governance, which is super important. We're talking about ethics, making sure you're following the rules, and using AI responsibly. If your team is lacking in this area, you could end up with problems like biased models or even breaking the law. Basically, organizations need smart people who understand both data science and how to keep things on the up-and-up to really nail MLOps.

The Banking, Financial Services, and Insurance (BFSI) sector is undergoing a significant transformation with the expanded use of machine learning (ML) applications. This evolution presents a substantial market opportunity for MLOps – the practices and tools that streamline the deployment, monitoring, and management of ML models.

In the world of banking and finance (BFSI), machine learning (ML) algorithms are really stepping up to fight fraud. They're like super-sleuths, digging through transaction histories, watching how people behave online, and looking for anything that seems out of the ordinary – potential fraudulent activity. And to keep these ML models running smoothly at a large scale, we need MLOps. Think of it as the behind-the-scenes team that makes sure everything's monitored and ready to react to new threats as they pop up. Speaking of changing things, machine learning is also transforming how credit scores and risk are managed. ML models can now look at a wide variety of information to better understand how likely someone is to pay back a loan, whether it's a person or a business. Again, MLOps is key, smoothly integrating these models into existing systems so financial institutions can make smart, data-backed decisions quickly and reliably.

Chatbots and virtual assistants, powered by some pretty smart machine learning, are now a big part of how banks and financial companies handle customer service. They use natural language processing to actually understand what you're asking and give you help that's tailored just for you. And to make sure these AI helpers keep getting better and better, they're using something called MLOps. When it comes to investment banking, machine learning is also changing the game, like with algorithmic trading and creating super smart investment plans. These models look at everything – market trends, news, and even past data – to make really good trading decisions. Again, MLOps plays a vital role in getting these models up and running quickly, keeping them running smoothly, and making sure they're dependable, especially when dealing with high-speed trading.

Global MLOps MarketSegmentation Analysis

The Global MLOps Market is segmented based on Industry Vertical, Component, Deployment Mode, Organization Size, and Geography.

MLOps Market, By Industry Vertical

  • BFSI
  • Media & Entertainment
  • It & Telecom
  • Manufacturing
  • Healthcare
  • Retail & E-commerce
  • Energy & Utility
  • Others

Based on Industry Vertical, the BFSI segment accounted for the largest market share of 26.52% in 2022 and is projected to grow at a CAGR of 40.53% during the forecast period. In the Banking, Financial Services, and Insurance (BFSI) sector, MLOps is proving to be a transformative force, leveraging the capabilities of machine learning (ML) to enhance various aspects of operations. The marriage of machine learning and operations in BFSI is not merely a technological integration but a strategic approach that streamlines processes, enhances decision-making, and mitigates risks.

MLOps is instrumental in developing and deploying advanced fraud detection models that continuously analyze transaction patterns, user behavior, and historical data to identify anomalies indicative of fraudulent activities. Revolut, a fintech company, employs MLOps to power its fraud detection system. By monitoring transactions in real-time, the system can identify unusual patterns and promptly flag potential fraudulent activities, enhancing security and protecting users’ financial assets.

MLOps Market, By Component

  • Platform
  • Software

When we look at the platform Component, it really stood out, grabbing a huge 81.77% of the market in 2022. And get this – it's expected to keep growing like crazy, with a projected annual growth rate of 38.03%! Think of MLOps Platforms as the foundation for any company diving into the complex world of Machine Learning Operations. They're like a complete toolbox, full of features to make building and deploying machine learning models a whole lot smoother. These platforms help teams work together better, automate all those tedious tasks, and make sure your machine learning stuff is deployed and managed without a hitch. Basically, MLOps Platforms are key to unlocking the full potential of machine learning. They give companies the resources they need to turn those cool data science experiments into real-world apps that can handle a lot of use. And they're useful for all kinds of industries, helping them innovate and become more efficient throughout the entire machine learning process.

MLOps Market, By Deployment Mode

  • On-premise
  • Cloud

In 2022, if we're talking Deployment Mode, then going On-Premise was the big winner, grabbing a whopping 50.27% of the market! That's like USD 956.4 Million worth! And get this, it's expected to keep growing like crazy, maybe even hitting a CAGR of 34.88% over the next few years. What does "On-Premise MLOps" even mean? Well, it's basically setting up your entire machine learning operation – everything from building and training models to actually using and keeping an eye on them – all within your own company's buildings, using your own servers. Sure, everyone's talking about the cloud these days, but going on-premise is still a real possibility for companies that want to be totally in charge of their machine learning stuff. Thinking about going on-premise gives organizations a solid advantage, especially when they want top-notch control, security, and the ability to meet strict rules. We're seeing real-world examples everywhere, with on-premise MLOps popping up in all sorts of industries and proving itself as a way to tackle unique business needs while making sure data is super secure and under lock and key.

MLOps Market, By Organization Size

  • Large Enterprise
  • Smes

Based on Organization Size, the Large Enterprise segment accounted for the largest market share of 75.17% in 2022 and is projected to grow at the highest CAGR of 38.41% during the forecast period. Implementing MLOps (Machine Learning Operations) in large enterprises brings forth a multitude of benefits, driving efficiency, innovation, and business impact across various domains. From enhancing predictive analytics to optimizing operations, MLOps empowers large enterprises to harness the full potential of their machine learning workflows.

MLOps helps big companies get way better at predicting things, using machine learning to make smarter calls. This is super helpful in industries where knowing what's coming next really matters for making good decisions and running things smoothly. Take Walmart, for example. They use MLOps to figure out exactly how much of everything they need in each store. By predicting what people will buy, they make sure they've got the right stuff, without ending up with too much or too little. Basically, MLOps makes it easier to get machine learning models up and running and keep them running well. This means companies can automate boring tasks, keep an eye on how their models are doing, and make everything work better, saving time and money. General Electric (GE) does this in its aviation business. They use MLOps to predict when equipment might break down so they can fix it before it does, which means less downtime and a more efficient operation overall.

MLOps Market, By Geography

  • North America
  • Europe
  • Asia Pacific
  • Latin America
  • Middle East and Africa

Based on Geography, North America accounted for the largest market share of 41.04% in 2022 and is projected to grow at a CAGR of 32.26% during the forecast period. North America stands as the epicentre of MLOps innovation, showcasing a mature and dynamic market. The penetration of MLOps practices in this region is profound, with a vast majority of enterprises actively incorporating these methodologies into their machine learning workflows. Sectors such as finance, healthcare, and technology are at the forefront, recognizing the transformative potential of MLOps in optimizing model deployment and management.

The MLOps scene in North America is buzzing, with tons of companies offering seriously cool solutions. You've got the big guys like Google, Microsoft, and Amazon who've really shaped how things are done. But also, there are specialized players like DataRobot and Databricks who've become super important, giving companies full-on MLOps platforms and services. Right now, everyone's trying to smoothly weave MLOps into their current DevOps setups. Companies are really pushing for teamwork between data scientists and ops folks to get models deployed faster and more reliably. The goal is to automate everything from start to finish, make machine learning workflows simpler, and generally speed up the whole development process, making it more flexible and efficient.

Key Players

The global MLOps market study report will provide a valuable insight with an emphasis on the global market. The major players in the market include Cloudera, Databricks, Inc., Alteryx, Domino Data Lab, Inc., DataRobot, Inc., Seldon Technologies, Kubeflow, H2O.ai, ModelOp, Inc., PostgresML, Dotscience, Iguazio, Valohai, Comet, Weights & Biases, among others.

Report Scope

REPORT ATTRIBUTESDETAILS
STUDY PERIOD

2019-2030

BASE YEAR

2023

FORECAST PERIOD

2024-2030

HISTORICAL PERIOD

2020-2022

UNIT

Value (USD Million)

KEY COMPANIES PROFILED

loudera, Databricks, Inc., Alteryx, Domino Data Lab, Inc., DataRobot, Inc., Seldon Technologies, Kubeflow, H2O.ai, ModelOp, Inc., PostgresML

SEGMENTS COVERED

By Industry Vertical, By Component, By Deployment Mode, By Organization Size, and By Geography.

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