MLOps Projected to Reach US$5.9 Billion by 2027

MLOps market to experience unprecedented growth and reach US$5.9 billion by 2027

The incorporation of machine learning (ML) into operational procedures in the quickly developing IT industry has given rise to a new discipline called MLOps. With a nod to DevOps, MLOps seeks to standardize and optimize the ML model development and deployment lifecycle. With more businesses realizing MLOps' potential to boost productivity and efficiency, the IT industry is undergoing a paradigm change, as seen by its rise. Because of the growing acceptance of machine learning methods across numerous IT business sectors, the MLOps market size is expected to experience considerable expansion in the upcoming years.

Market Trends of MLOps

The process of developing and implementing machine learning (ML) applications using DevOps concepts and methodologies is known as MLOps. The goal is to automate and simplify the whole machine-learning lifecycle, from model training and data preparation to governance and monitoring. As more businesses use machine learning (ML) and artificial intelligence (AI) to improve their business outcomes and customer experience, MLOps is becoming more and more popular. The following are a few MLOps market trends as of late:

Growing need for serverless and cloud-based MLOps solutions that provide cost-effectiveness, scalability, and flexibility.

A growing number of industrial sectors, including healthcare, banking, retail, telecom, and manufacturing, are using MLOps.

The increasing need for MLOps solutions that tackle the difficulties and intricacies of machine learning models, including data integrity, version control, repeatability, security, and governance.

The growing amalgamation of MLOps with other technologies, like explainable AI, edge computing, and AutoML

Top Companies of MLOps

MLOps platforms and solutions are widely available from many organizations, either as stand-alone items or as components of their larger AI/ML offerings. Among the leading MLOps firms are:

Amazon Web Services (AWS): AWS offers a complete MLOps platform called Amazon SageMaker, which lets customers create, train, apply, and oversee machine learning models in the cloud.

Microsoft Azure: Azure provides a cloud-based MLOps platform called Azure Machine Learning, which covers the whole lifecycle of machine learning, from data intake and experimentation to deployment and monitoring.

Google Cloud Platform (GCP): Vertex AI, a single MLOps platform offered by GCP, makes it easier to create and maintain machine learning models on Google Cloud.

Algorithmia: This MLOps business focuses on large-scale ML model deployment and management, with features like model catalog, versioning, security, and governance.

DataRobot: This MLOps startup provides an automated machine learning end-to-end platform that includes features like data preparation, model construction, deployment, and monitoring.

Databricks: Databricks is an MLOps firm that provides a single platform for data and AI, including tools for managing the ML lifecycle including MLflow, Delta Lake, and Spark.

How MLOps Altered the Machine Learning

The subject of machine learning (ML) has seen a tremendous transformation because of MLOps, or machine learning operations. It has brought in a set of procedures that streamline and automate ML installations and workflows. This is the way that MLOps has changed Machine Learning:

1. Integration of Development and Operations: MLOps integrates the deployment and operations of ML systems with the development of ML applications. This indicates that it combines the steps involved in creating machine learning models and implementing them in real-world applications.

2. Automation and Standardization: Model creation, testing, integration, release, and infrastructure management are just a few of the ML lifecycle tasks that MLOps assists in automating and standardizing. This results in ML workflows that are more dependable and efficient.

3. Continuous Integration and Delivery: In a Continuous Integration and Delivery (CI/CD) environment, MLOps handles machine learning assets in the same way as other software assets. This implies that as part of a single release process, ML models are deployed alongside the apps and services they utilize.

4. Version Control: MLOps keeps track of modifications made to the ML assets so that outcomes may be duplicated and, if needed, rolled back to earlier iterations. As a result, ML model training is now auditable and repeatable.

5. Reproducibility: MLOps highlights the significance of reproducibility in an ML workflow at each stage, from the deployment of ML models to data processing.

Machine learning has changed as a result of MLOps' application of these techniques, which have simplified the development, deployment, and maintenance of ML models in real-world settings.

Estimated Future Market Size of MLOps

The growing demand and use of ML and AI solutions across sectors and geographies is likely to propel the size of the worldwide MLOps market to considerable growth in the coming years, according to several market research publications. Among the approximations are:

Global MLOps market size was estimated by Grand View Research to be worth US$1.19 billion in 2022, and the market is projected to increase at a compound annual growth rate (CAGR) of 39.7% between 2023 and 2030.

The Global MLOps market size was estimated by Allied Market Research to be US$1.4 billion in 2022 and is expected to expand at a CAGR of 39.3% from 2023 to 2032, reaching US$37.4 billion by that time.

The worldwide MLOps market is expected to reach US$ 13.11 billion by 2028, demonstrating a growth rate (CAGR) of 43.06% between 2023 and 2028, according to IMARC Group. The market was valued at US$ 1.52 billion in 2022.