ML Ops Market to be dominated by BFSI segment till 2026
Growing concerns to manage
complex business operations and proliferation of the data generation is
expected to drive the global ML Ops market for the forecast period.
According to TechSci Research report, “ML Ops
Market - Global Industry Size, Share, Trends, Opportunity and Forecast,
2016-2026 Segmented By Solutions (Data Management, Modelling, Continuous
Deployment, Computing and Resource), By Product Focus (Data-Centric, Model
Centric), By Task (Model Lifecycle Management, Model Versioning &
Iteration, Model Monitoring & Management, Model Governance, Model
Security), By Component (Platform, Services (Professional, Managed)), By Type
(Public Cloud, Private Cloud, Hybrid Cloud), By Organization Size (Large Enterprises,
Small & Medium Sized Enterprises), By End Use (BFSI, IT & Telecom,
Retail, Manufacturing, Public Sector, Others) and By Region”, the global ML
Ops market is expected to grow at a rate of steady CAGR for the forecast
period, 2022-2026. ML Ops stands for machine learning operations and makes the
use of machine learning models by the development/operations team. The main
objective of ML Ops is to manage the deployment and development of machine
learning models by stating the process to make machine learning more dependable
and productive. The development of the machine learning model is different from
the conventional application development as the machine learning (ML) models
rely upon the data, including the training data, test data, real-time data, and
validation data. Organizations actively
adopt ML Ops to efficiently manage the ML models and handle the range of
ML-model specific management needs. ML Ops manages the process required for the
model creation and monitors the data used at the training and for real-time
applications.
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Global
ML Ops market is segmented into solutions, product focus, task, component,
type, organization size, end use, regional distribution, and company. Based on
task, the market is divided into model lifecycle management, model versioning
& iteration, model monitoring & management, model governance, and model
security. The ML Ops tools are required to manage the model lifecycle,
training, deployment, and operationalization. ML Ops provides a reliable
process to move models from data science management to production management.
Model versioning & iteration helps in iteration and versioning of the
machine learning models to deal with new requirements as the ML models can
change based on the real-world data. Model monitoring and management aids in
monitoring and managing the model usage, consumption and check the accuracy and
performance of the generated results. Model governance helps in model access
control, enhancing transparency in the working of the ML models, and model
security is used to provide protection to model from security threats,
cyber-attacks and prevent the access of unauthorized users. Based on type, the
market is divided into public cloud, private cloud, and hybrid cloud. The
hybrid cloud is expected to witness the fastest incremental growth for the next
five years. The growing adoption of the hybrid cloud model by enterprises for
big data processing, enhanced flexibility, and scalability influences the
market demand. Hybrid cloud also provides increased security and can
efficiently optimize the workload resources.
Microsoft
Corporation, Amazon Web Services, Inc., Google, LLC, IBM Corporation, Dataiku
SAS, Iguazio Ltd, Databricks Inc., DataRobot, Inc., Cloudera, Inc., Modzy,
Algorithmia, Inc., HP Enterprises Co., Valohai, Allegro AI Ltd., Comet ML Inc. are
the leading players operating in global ML Ops market. Service providers are
increasingly focusing on research and development process to fuel higher growth
in the market.
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“The
ongoing technological advancements and the adoption of advanced technologies by
the enterprises to provide an enhanced experience to the consumers and boost
the profit margin is generating the need for advanced software and services.
Machine learning helps businesses deploy solutions, save cost, optimize
workflow, and use data analytics technology to make smart decisions. ML Ops is
used for the deployment of machine learning model to lower the operational
costs and minimize time. High-end investments by the market players and the
growing IT sector is expected to propel the global ML Ops market growth till
2026” said Mr. Karan Chechi, Research Director with TechSci Research,
a research based global management consulting firm.
“ML Ops Market - Global
Industry Size, Share, Trends, Opportunity and Forecast, 2016-2026 Segmented By Solutions (Data Management,
Modelling, Continuous Deployment, Computing and Resource), By Product Focus
(Data-Centric, Model Centric), By Task (Model Lifecycle Management, Model
Versioning & Iteration, Model Monitoring & Management, Model
Governance, Model Security), By Component (Platform, Services (Professional,
Managed)), By Type (Public Cloud, Private Cloud, Hybrid Cloud), By Organization
Size (Large Enterprises, Small & Medium Sized Enterprises), By End Use
(BFSI, IT & Telecom, Retail, Manufacturing, Public Sector, Others) and By
Region” has evaluated the future growth potential of global ML
Ops market and provided statistics & information on market size, shares, structure
and future market growth. The report intends to provide cutting-edge market
intelligence and help decision makers take sound investment decisions. Besides,
the report also identifies and analyzes the emerging trends along with
essential drivers, challenges, and opportunities in the of global ML Ops market.
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