MLOps Market: size was valued at USD 1.06 Bn in 2022 and is expected to reach USD 22.1 Bn by 2029, at a CAGR of 38.7%
MLOps Market Overview:
the MLOps Market research report published by Maximize Market Research (MMR), provides extensive and top-notch market studies, offering transparency in its findings. The report includes a list of major competitors, strategic insights, and an analysis of key factors shaping the MLOps industry. This market research encompasses a wide range of markets, marketing strategies, trends, future product possibilities, and burgeoning opportunities, all contributing to the generation of comprehensive market reports.
MLOps Market Dyanmics:
Machine learning is becoming more widely used in a variety of areas, including healthcare, retail, BFSI, and telecommunications. The need for MLOps platforms and services that can assist enterprises in managing and deploying their machine learning models has increased as a result of this. Updating models in fields like fraud, underwriting, customer management, etc. is made possible by using outdated machine learning models.
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MLOps Market Report Scope and Research Methodology
The MLOps Market report aims to forecast market size by considering segments, regional distribution, and industry competition. The report estimates and forecasts market size and growth using a bottom-up approach. It conducts a comprehensive examination of key players in the MLOps industry, including revenue figures. The report insights into the global, regional, and local level analysis of the MLOps Market, highlighting the factors that restrain, drive, and challenge market growth throughout the forecast period.
MLOps Market Regional Insights
North America is expected to lead the global MLOps market and is expected to continue its dominance during the forecast period. Europe holds a second position in the MLOps market, accounting for a substantial revenue share in 2022. Asia-Pacific region expected significant growth in the MLOps market.
MLOps Market Segmentation:
Deployment Mode: MLOps platforms can be set up in the cloud or on-premises. On-premises deployment entails setting up MLOps infrastructure and software on the client’s property. As the software is licensed and the complete instance of the software resides on-site, on-premise software necessitates that an enterprise obtain a license or a copy of the software in order to utilize it. On the other hand, cloud-based deployment involves hosting and managing the MLOps platform using cloud infrastructure and services. A cloud-based server makes use of virtual technologies to host programs remotely for businesses. Data is routinely backed up, there are no capital expenses, and businesses only pay for the services they consume.
MLOps Market Key Players:
• Microsoft
1. Amazon
2. Google
3. IBM
4. Dataiku
5. Lguazio
6. Databricks
7. DataRobot, Inc.
8. Cloudera
9. Modzy
10. Algorithmia
11. HPE
12. Valohai
13. Allegro AI
14. Comet
15. FloydHub
16. Paperpace
17. Cnvrg.io
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Key questions answered in MLOps Market are:
- What is the expected MLOps market size by 2029?
- What is the growth rate of the MLOps Market?
- Which are the factors expected to drive the MLOps market growth?
- Which segment dominated the MLOps market growth?
- Which region held the largest share in the MLOps market?
Key Offerings:
- Past Market Size and Competitive Landscape (2018 to 2021)
- Past Pricing and price curve by region (2018 to 2021)
- Market Size, Share, Size & Forecast by different segment | 2022−2029
- Market Dynamics – Growth Drivers, Restraints, Opportunities, and Key Trends by region
- Market Segmentation – A detailed analysis by segment with their sub-segments and Region
- Competitive Landscape – Profiles of selected key players by region from a strategic perspective
- Competitive landscape – Market Leaders, Market Followers, Regional player
- Competitive benchmarking of key players by region
- PESTLE Analysis
- PORTER’s analysis
- Value chain and supply chain analysis
- Legal Aspects of business by region
- Lucrative business opportunities with SWOT analysis
- Recommendations
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