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¼¼°èÀÇ MLOps ½ÃÀå ±Ô¸ð, Á¡À¯À², µ¿Ç⠺м® º¸°í¼­ : ÄÄÆ÷³ÍÆ®º°, Àü°³ Çüź°, Á¶Á÷ ±Ô¸ðº°, ¾÷°èº°, Áö¿ªº°, ºÎ¹®º° ¿¹Ãø(2025-2030³â)

MLOps Market Size, Share & Trends Analysis Report By Component (Platform, Service), By Deployment (Cloud, On-premises), By Organization Size, By Vertical (BFSI, Retail & E-commerce), By Region, And Segment Forecasts, 2025 - 2030

¹ßÇàÀÏ: | ¸®¼­Ä¡»ç: Grand View Research | ÆäÀÌÁö Á¤º¸: ¿µ¹® 100 Pages | ¹è¼Û¾È³» : 2-10ÀÏ (¿µ¾÷ÀÏ ±âÁØ)

    
    
    




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MLOps ½ÃÀå ¼ºÀå°ú µ¿Çâ

Grand View Research, Inc.ÀÇ ÃֽŠº¸°í¼­¿¡ µû¸£¸é, ¼¼°è MLOps ½ÃÀå ±Ô¸ð´Â 2030³â±îÁö 166¾ï 1,340¸¸ ´Þ·¯¿¡ ´ÞÇÒ °ÍÀ¸·Î ¿¹ÃøµË´Ï´Ù. ÀÌ ½ÃÀåÀº 2025-2030³â ¿¬Æò±Õ º¹ÇÕ ¼ºÀå·ü(CAGR) 40.5%¸¦ º¸ÀÏ °ÍÀ¸·Î ¿¹ÃøµË´Ï´Ù. Á¶Á÷Àº µ¥ÀÌÅͷκÎÅÍ ÀλçÀÌÆ®·ÂÀ» ¾ò°í °í°´ÀÇ ¿ä±¸¸¦ ÃÖÀûÀ¸·Î ÃæÁ·½Ã۱â À§ÇØ ML/AI ±â¹Ý ÇÁ·ÎÁ§Æ®¸¦ äÅÃÇÏ¿© ¼öÀÍ ±âȸ¸¦ ´Ã¸®°í ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ÇÁ·ÎÁ§Æ®´Â »ý»ê¼º°ú ¿î¿µÀ» °³¼±Çϱâ À§ÇØ Àü»çÀûÀ¸·Î ML°ú MLOps¸¦ äÅÃÇØ¾ß Çϸç, ÀÌ´Â ÇâÈÄ ¼ö¿ä¸¦ °­È­ÇÒ °ÍÀÔ´Ï´Ù. ÀÌ ÀÎÇÁ¶ó¿¡´Â ¸Ó½Å·¯´× ¸ðµ¨ÀÇ ÇнÀ, °³¹ß, ¿î¿µ¿¡ ÇÊ¿äÇÑ ÇÁ·Î¼¼½º, ¸®¼Ò½º, µµ±¸°¡ Æ÷ÇÔµÇ¾î º¹ÀâÇØÁö´Â IT ¿ä±¸»çÇ×À» ¿ÏÈ­ÇÕ´Ï´Ù.

¶ÇÇÑ, ÀÌ·¯ÇÑ Ç÷§ÆûÀº ML ¶óÀÌÇÁ»çÀÌŬ Àü¹ÝÀÇ ºñ¿ë Àý°¨À» Áö¿øÇÏ¿© ½ÃÀå ¼ºÀåÀ» À§ÇÑ ¸¹Àº Àü¸ÁÀ» âÃâÇϰí ÀÖ½À´Ï´Ù. Àü ¼¼°è ±â¾÷µéÀº ´õ ³ªÀº °í°´ °í·Á¿Í Á÷¿ø »ý»ê¼º Çâ»óÀ» ÅëÇØ °æÀï ¿ìÀ§¸¦ È®º¸Çϱâ À§ÇØ AI/ML ½Ã½ºÅÛ¿¡ ´ëÇÑ ÅõÀÚ¸¦ ´Ã¸®µµ·Ï ¼³µæ´çÇϰí ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ÀÚµ¿È­ ½Ã½ºÅÛÀº µ¥ÀÌÅ͸¦ Á¤È®ÇÏ°í ºü¸£°Ô ºÐ¼®ÇÏ°í º¹ÀâÇÑ ¾Ë°í¸®ÁòÀ» »ç¿ëÇÏ¿© ¹Ì·¡ÀÇ ´Ü°è¸¦ ¿¹ÃøÇÏ¿© ±Ã±ØÀûÀ¸·Î ±â¾÷ÀÇ »ý»ê¼ºÀ» Çâ»ó½Ãų ¼ö ÀÖ½À´Ï´Ù. ¿¹¸¦ µé¾î, 2023³â 1¿ù, ÀÎÁö ÄÄÇ»ÆÃ ±â¾÷ Semantics´Â Ŭ¶ó¿ìµå °ü¸®Çü MLOps Ç÷§ÆûÀ» Á¦°øÇÏ´Â ElemenoÀÇ Àμö¸¦ ¹ßÇ¥Çß½À´Ï´Ù.

¿¤·¹¸Þ³ë´Â ML ¼ÒÇÁÆ®¿þ¾îÀÇ ¹èÆ÷, °³¹ß, °ü¸®¸¦ »ç¿ëÇϱ⠽¬¿î ÀÎÅÍÆäÀ̽º·Î ÀÚµ¿È­ÇÏ´Â °ÍÀ» ºÐ¸íÇÑ ¸ñÇ¥·Î »ï°í ÀÖ½À´Ï´Ù. ¶ÇÇÑ ±â¾÷ ¹× Á¶Á÷ÀÇ ÀΰøÁö´É µµÀÔÀ» °¡¼ÓÈ­ÇÏ´Â °ÍÀ» ¸ñÇ¥·Î Çϰí ÀÖ½À´Ï´Ù. ¶ÇÇÑ, MLOps´Â ¿öÅ©ÇÃ·Î¿ì °ü¸® µµ±¸¿Í Æ®·»µå ¿¹ÃøÀ» ÅëÇÕÇÏ¿© ±â¾÷ °ü¸®¿¡ Çõ¸íÀ» ÀÏÀ¸Ä×½À´Ï´Ù. ¶ÇÇÑ, ML ±â¼ú¿¡ ´ëÇÑ ÅõÀÚ Áõ°¡°¡ ¼ö¿ä Áõ°¡¿¡ Å©°Ô ±â¿©Çϰí ÀÖ½À´Ï´Ù. ¶ÇÇÑ, ¸¹Àº ÇÏÀÌÅ×Å© ±â¾÷°ú ¼Ò±Ô¸ð ½ºÅ¸Æ®¾÷µéÀÌ ¹ë·ùüÀÎÀÇ È¿À²¼ºÀ» ³ôÀ̱â À§ÇØ ºñµ¶Á¡Àû MLOps Ç÷§Æû µµÀÔ¿¡ ÅõÀÚÇϰí ÀÖÀ¸¸ç, ÀÌ´Â ¼¼°è ¼ºÀåÀ» °¡¼ÓÇϰí ÀÖ½À´Ï´Ù. ¶ÇÇÑ, °íǰÁúÀÇ Àú·ÅÇÑ ÀÚµ¿È­ ±â¼úÀÇ °¡¿ë¼º Áõ°¡´Â ÇâÈÄ ¸î ³â µ¿¾È ¼ºÀåÀ» °¡¼ÓÇÒ °ÍÀ¸·Î ¿¹ÃøµË´Ï´Ù.

MLOps ½ÃÀå º¸°í¼­ ÇÏÀ̶óÀÌÆ®

  • Ç÷§Æû ºÎ¹®Àº ML ¸ðµ¨ °ü¸® ¹× ¿î¿µÀ» ÃÖÀûÈ­ÇÏ´Â ±â´ÉÀ¸·Î ÀÎÇØ 2024³â ¼¼°è ¸ÅÃâ¿¡¼­ °¡Àå ³ôÀº Á¡À¯À²À» Â÷ÁöÇÏ¸ç ½ÃÀåÀ» ÁÖµµÇß½À´Ï´Ù.
  • On-Premise ºÎ¹®Àº 2024³â °¡Àå Å« ¸ÅÃâ Á¡À¯À²À» Â÷ÁöÇß½À´Ï´Ù. ÀÌ´Â ³ôÀº µ¥ÀÌÅÍ ¾ÈÀü¼º°ú º¸¾È µî On-Premise ±¸ÃàÀÌ Á¦°øÇÏ´Â ¿©·¯ °¡Áö ÀåÁ¡¿¡ ±âÀÎÇÕ´Ï´Ù.
  • ´ë±â¾÷ ºÎ¹®Àº 2024³â ¼¼°è ¸ÅÃâÀÇ Å« ºñÁßÀ» Â÷ÁöÇÏ¸ç ½ÃÀåÀ» ÁÖµµÇß½À´Ï´Ù. ÀÌ´Â ´ë±â¾÷ Àü¹Ý¿¡¼­ AI ±â¼ú°ú µ¥ÀÌÅÍ »çÀ̾𽺠µµÀÔÀÌ È®´ëµÇ¾î ¾÷¹«¿¡ ´ëÇÑ Á¤·®Àû ÀλçÀÌÆ®¸¦ ¾òÀ» ¼ö ÀÖ°Ô µÇ¾ú±â ¶§¹®ÀÔ´Ï´Ù.
  • ÀºÇà, ±ÝÀ¶¼­ºñ½º ¹× º¸Çè(BFSI) ºÎ¹®Àº 2024³â °¡Àå Å« ¼öÀÍ Á¡À¯À²À» Â÷ÁöÇß½À´Ï´Ù. ±ÝÀ¶ ºÎ¹®ÀÇ ¸Ó½Å·¯´× µµÀÔÀº ÁÖ·Î ¼öÀÍ·ü °ü¸®, ·¹°Å½Ã ½Ã½ºÅÛÀÇ ¿¹Ãø À¯Áöº¸¼ö, »ç±â °¨Áö µî ƯÁ¤ °úÁ¦¸¦ ÇØ°áÇϱâ À§ÇØ Áõ°¡Çϰí ÀÖ½À´Ï´Ù.
  • ºÏ¹Ì´Â 2024³â ¼¼°è ¸ÅÃâÀÇ 40.8% ÀÌ»óÀ» Â÷ÁöÇÏ¸ç ½ÃÀåÀ» Àå¾ÇÇß½À´Ï´Ù. ÀÌ´Â ½ÅÈï °æÁ¦ ±¹°¡, R&D ±â°ü ¹× ÀÌ Áö¿ª¿¡ ±â¹ÝÀ» µÐ ´Ù¾çÇÑ ÁÖ¿ä AI ±â¾÷ÀÇ °­·ÂÇÑ R&D ¿ª·®¿¡ ±âÀÎÇÕ´Ï´Ù.

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Á¦1Àå Á¶»ç ¹æ¹ý°ú ¹üÀ§

Á¦2Àå ÁÖ¿ä ¿ä¾à

Á¦3Àå MLOps ½ÃÀå º¯¼ö, µ¿Çâ, ¹üÀ§

  • ½ÃÀå °èÅë Àü¸Á
  • ½ÃÀå ¿ªÇÐ
    • ½ÃÀå ¼ºÀå ÃËÁø¿äÀÎ ºÐ¼®
    • ½ÃÀå ¼ºÀå ¾ïÁ¦¿äÀÎ ºÐ¼®
    • »ê¾÷ °úÁ¦
  • MLOps ½ÃÀå ºÐ¼® Åø
    • »ê¾÷ ºÐ¼® - PorterÀÇ Five Forces ºÐ¼®
    • PESTEL ºÐ¼®
  • ÆäÀÎÆ÷ÀÎÆ® ºÐ¼®

Á¦4Àå MLOps ½ÃÀå : ÄÄÆ÷³ÍÆ®º°, ÃßÁ¤, µ¿Ç⠺м®

  • ºÎ¹® ´ë½Ãº¸µå
  • MLOps ½ÃÀå : ÄÄÆ÷³ÍÆ® º¯µ¿ ºÐ¼®, 2024³â ¹× 2030³â
  • Ç÷§Æû
  • ¼­ºñ½º

Á¦5Àå MLOps ½ÃÀå : Àü°³ Çüź°, ÃßÁ¤, µ¿Ç⠺м®

  • ºÎ¹® ´ë½Ãº¸µå
  • MLOps ½ÃÀå : Àü°³ º¯µ¿ ºÐ¼®, 2024³â ¹× 2030³â
  • Ŭ¶ó¿ìµå
  • On-Premise

Á¦6Àå MLOps ½ÃÀå : Á¶Á÷ ±Ô¸ðº°, ÃßÁ¤, µ¿Ç⠺м®

  • ºÎ¹® ´ë½Ãº¸µå
  • MLOps ½ÃÀå : Á¶Á÷ ±Ô¸ð º¯µ¿ ºÐ¼®, 2024³â ¹× 2030³â
  • Áß¼Ò±â¾÷
  • ´ë±â¾÷

Á¦7Àå MLOps ½ÃÀå : ¾÷°èº°, ÃßÁ¤, µ¿Ç⠺м®

  • ºÎ¹® ´ë½Ãº¸µå
  • MLOps ½ÃÀå : ¾÷°è º¯µ¿ ºÐ¼®, 2024³â ¹× 2030³â
  • ÀºÇà, ±ÝÀ¶¼­ºñ½º ¹× º¸Çè(BFSI)
  • ÇコÄÉ¾î ¹× »ý¸í°úÇÐ
  • ¼Ò¸Å ¹× E-Commerce
  • IT ¹× Åë½Å
  • ¿¡³ÊÁö ¹× À¯Æ¿¸®Æ¼
  • Á¤ºÎ ¹× °ø°ø ºÎ¹®
  • ¹Ìµð¾î ¹× ¿£ÅÍÅ×ÀÎ¸ÕÆ®
  • ±âŸ

Á¦8Àå MLOps ½ÃÀå : Áö¿ªº°, ÃßÁ¤, µ¿Ç⠺м®

  • MLOps ½ÃÀå Á¡À¯À²(Áö¿ªº°, 2024³â ¹× 2030³â)
  • ºÏ¹Ì
    • ¹Ì±¹
    • ij³ª´Ù
    • ¸ß½ÃÄÚ
  • À¯·´
    • ¿µ±¹
    • µ¶ÀÏ
    • ÇÁ¶û½º
  • ¾Æ½Ã¾ÆÅÂÆò¾ç
    • Áß±¹
    • ÀϺ»
    • Àεµ
    • Çѱ¹
    • È£ÁÖ
  • ¶óƾ¾Æ¸Þ¸®Ä«
    • ºê¶óÁú
  • Áßµ¿ ¹× ¾ÆÇÁ¸®Ä«
    • ³²¾ÆÇÁ¸®Ä«°øÈ­±¹
    • ¾Æ¶ø¿¡¹Ì¸®Æ®(UAE)
    • »ç¿ìµð¾Æ¶óºñ¾Æ

Á¦9Àå °æÀï ±¸µµ

  • ±â¾÷ ºÐ·ù
  • ±â¾÷ÀÇ ½ÃÀå Æ÷Áö¼Å´×
  • Âü¿© ±â¾÷ °³¿ä
  • ±â¾÷ È÷Æ®¸Ê ºÐ¼®
  • Àü·« ¸ÅÇÎ
  • ±â¾÷ °³¿ä/»óÀå±â¾÷
    • IBM Corporation
    • Microsoft
    • Google LLC
    • Amazon Web Services, Inc.
    • Hewlett Packard Enterprise Development LP
    • Neptune Labs, Inc.
    • DataRobot, Inc.
    • Dataiku.
    • ALTERYX, INC.
    • GAVS Technologies NA, Inc
LSH

MLOps Market Growth & Trends:

The global MLOps market size is expected to reach USD 16,613.4 million by 2030, according to a new report by Grand View Research, Inc. The market is anticipated to grow at a CAGR of 40.5% from 2025 to 2030. To gain insights from the data and best catering the customers' needs, organizations are adopting ML/AI-based projects to increase their revenue opportunities. These projects necessitate the adoption of ML and MLOps across businesses to improve their productivity and operations, which, in turn, strengthens the demand in the future. Enterprises have also invested in MLOps infrastructure, which includes the processes, resources, and tooling needed to train, develop, and operate machine learning models to ease the growing complexity of their IT needs.

Moreover, these platforms assist in reducing costs over the entire ML lifecycle and produce numerous prospects for the growth of the market. Enterprises globally are persuaded to increase their investment in AI/ML systems to achieve a competitive edge through better customer insights and increased employee productivity. Such automation systems can accurately and quickly analyze data and use complex algorithms to forecast future steps, eventually enhancing enterprises' productivity. For instance, in January 2023, Semantix, a cognitive computing company, announced the acquisition of Elemeno Inc., a cloud-managed MLOps platform provider.

Elemeno aims explicitly to automate ML software's deployment, development, and management with an easy-to-use interface. It also intends to accelerate artificial intelligence adoption for enterprises and organizations. Moreover, MLOps have revolutionized enterprise management by consolidating workflow management tools and trend forecasting. Furthermore, a rise in investment in ML technology remarkably contributes to demand expansion. In addition, many tech organizations and small start-ups have invested in adopting non-proprietary MLOps platforms to improve efficiency in their value chains, propelling global growth. Moreover, increasing of high-quality, affordable automated technology availability is expected to drive growth in the coming years.

MLOps Market Report Highlights:

  • The platform segment led the market in 2024, accounting for the highest share of the global revenue owing to its feature of optimizing the management and operation of ML models.
  • The On-premises segment held the largest revenue share in 2024. This is attributed to the numerous benefits offered by the On-premises deployment, such as high data safety and security.
  • The large enterprises segment led the market in 2024, accounting for significant share of the global revenue owing to the growing implementation of AI technology and data science across large enterprises to present quantitative insights into their operations.
  • The BFSI segment held the largest revenue share in 2024. The adoption of machine learning by the financial sector has been on the rise, primarily for solving specific challenges such as yield management, predictive maintenance of legacy systems, and fraud detection.
  • North America dominated the market in 2024, accounting for over 40.8% share of the global revenue owing to Al's intense research and development competencies in the developed economies, research institutes, and various leading AI companies based in this region.

Table of Contents

Chapter 1. Methodology and Scope

  • 1.1. Market Segmentation and Scope
  • 1.2. Research Methodology
    • 1.2.1. Information Procurement
  • 1.3. Information or Data Analysis
  • 1.4. Methodology
  • 1.5. Research Scope and Assumptions
  • 1.6. Market Formulation & Validation
  • 1.7. Country Based Segment Share Calculation
  • 1.8. List of Data Sources

Chapter 2. Executive Summary

  • 2.1. Market Outlook
  • 2.2. Segment Outlook
  • 2.3. Competitive Insights

Chapter 3. MLOps market Variables, Trends, & Scope

  • 3.1. Market Lineage Outlook
  • 3.2. Market Dynamics
    • 3.2.1. Market Driver Analysis
    • 3.2.2. Market Restraint Analysis
    • 3.2.3. Industry Challenge
  • 3.3. MLOps market Analysis Tools
    • 3.3.1. Industry Analysis - Porter's
      • 3.3.1.1. Bargaining power of the suppliers
      • 3.3.1.2. Bargaining power of the buyers
      • 3.3.1.3. Threats of substitution
      • 3.3.1.4. Threats from new entrants
      • 3.3.1.5. Competitive rivalry
    • 3.3.2. PESTEL Analysis
      • 3.3.2.1. Political landscape
      • 3.3.2.2. Economic and Social landscape
      • 3.3.2.3. Technological landscape
  • 3.4. Pain Point Analysis

Chapter 4. MLOps market: Component Estimates & Trend Analysis

  • 4.1. Segment Dashboard
  • 4.2. MLOps market: Component Movement Analysis, 2024 & 2030 (USD Million)
  • 4.3. Platform
    • 4.3.1. Platform Market Revenue Estimates and Forecasts, 2018 - 2030 (USD Million)
  • 4.4. Services
    • 4.4.1. Services Market Revenue Estimates and Forecasts, 2018 - 2030 (USD Million)

Chapter 5. MLOps market: Deployment Estimates & Trend Analysis

  • 5.1. Segment Dashboard
  • 5.2. MLOps market: Deployment Movement Analysis, 2024 & 2030 (USD Million)
  • 5.3. Cloud
    • 5.3.1. Cloud MLOps market Revenue Estimates and Forecasts, 2018 - 2030 (USD Million)
  • 5.4. On-Premises
    • 5.4.1. On-Premises MLOps market Revenue Estimates and Forecasts, 2018 - 2030 (USD Million)

Chapter 6. MLOps market: Organization Size Estimates & Trend Analysis

  • 6.1. Segment Dashboard
  • 6.2. MLOps market: Deployment Movement Analysis, 2024 & 2030 (USD Million)
  • 6.3. SMEs
    • 6.3.1. SMEs MLOps market Revenue Estimates and Forecasts, 2018 - 2030 (USD Million)
  • 6.4. Large Enterprises
    • 6.4.1. Large Enterprises MLOps market Revenue Estimates and Forecasts, 2018 - 2030 (USD Million)

Chapter 7. MLOps market: Organization Size Estimates & Trend Analysis

  • 7.1. Segment Dashboard
  • 7.2. MLOps market: Organization Size Movement Analysis, 2024 & 2030 (USD Million)
  • 7.3. BFSI
    • 7.3.1. BFSI Market Revenue Estimates and Forecasts, 2018 - 2030 (USD Million)
  • 7.4. Healthcare & Life Sciences
    • 7.4.1. Healthcare & Life Sciences Market Revenue Estimates and Forecasts, 2018 - 2030 (USD Million)
  • 7.5. Retail & E-Commerce
    • 7.5.1. Retail & E-Commerce Market Revenue Estimates and Forecasts, 2018 - 2030 (USD Million)
  • 7.6. IT & Telecom
    • 7.6.1. IT & Telecom Market Revenue Estimates and Forecasts, 2018 - 2030 (USD Million)
  • 7.7. Energy & Utilities
    • 7.7.1. Energy & Utilities Market Revenue Estimates and Forecasts, 2018 - 2030 (USD Million)
  • 7.8. Government & Public Sector
    • 7.8.1. Government & Public Sector Market Revenue Estimates and Forecasts, 2018 - 2030 (USD Million)
  • 7.9. Media & Entertainment
    • 7.9.1. Media & Entertainment Market Revenue Estimates and Forecasts, 2018 - 2030 (USD Million)
  • 7.10. Others
    • 7.10.1. Others Market Revenue Estimates and Forecasts, 2018 - 2030 (USD Million)

Chapter 8. MLOps market: Regional Estimates & Trend Analysis

  • 8.1. MLOps market Share, By Region, 2024 & 2030 (USD Million)
  • 8.2. North America
    • 8.2.1. North America MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 8.2.2. U.S.
      • 8.2.2.1. U.S. MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 8.2.3. Canada
      • 8.2.3.1. Canada MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 8.2.4. Mexico
      • 8.2.4.1. Mexico MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • 8.3. Europe
    • 8.3.1. Europe MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 8.3.2. UK
      • 8.3.2.1. UK MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 8.3.3. Germany
      • 8.3.3.1. Germany MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 8.3.4. France
      • 8.3.4.1. France MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • 8.4. Asia Pacific
    • 8.4.1. Asia Pacific MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 8.4.2. China
      • 8.4.2.1. China MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 8.4.3. Japan
      • 8.4.3.1. Japan MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 8.4.4. India
      • 8.4.4.1. India MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 8.4.5. South Korea
      • 8.4.5.1. South Korea MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 8.4.6. Australia
      • 8.4.6.1. Australia MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • 8.5. Latin America
    • 8.5.1. Latin America MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 8.5.2. Brazil
      • 8.5.2.1. Brazil MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • 8.6. Middle East and Africa
    • 8.6.1. Middle East and Africa MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 8.6.2. South Africa
      • 8.6.2.1. South Africa MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 8.6.3. UAE
      • 8.6.3.1. UAE MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 8.6.4. KSA
      • 8.6.4.1. KSA MLOps market Estimates and Forecasts, 2018 - 2030 (USD Million)

Chapter 9. Competitive Landscape

  • 9.1. Company Categorization
  • 9.2. Company Market Positioning
  • 9.3. Participant's Overview
  • 9.4. Financial Performance
  • 9.5. Product Benchmarking
  • 9.6. Company Heat Map Analysis
  • 9.7. Strategy Mapping
  • 9.8. Company Profiles/Listing
    • 9.8.1. IBM Corporation
      • 9.8.1.1. Participant's Overview
      • 9.8.1.2. Financial Performance
      • 9.8.1.3. Product Benchmarking
      • 9.8.1.4. Recent Developments
    • 9.8.2. Microsoft
      • 9.8.2.1. Participant's Overview
      • 9.8.2.2. Financial Performance
      • 9.8.2.3. Product Benchmarking
      • 9.8.2.4. Recent Developments
    • 9.8.3. Google LLC
      • 9.8.3.1. Participant's Overview
      • 9.8.3.2. Financial Performance
      • 9.8.3.3. Product Benchmarking
      • 9.8.3.4. Recent Developments
    • 9.8.4. Amazon Web Services, Inc.
      • 9.8.4.1. Participant's Overview
      • 9.8.4.2. Financial Performance
      • 9.8.4.3. Product Benchmarking
      • 9.8.4.4. Recent Developments
    • 9.8.5. Hewlett Packard Enterprise Development LP
      • 9.8.5.1. Participant's Overview
      • 9.8.5.2. Financial Performance
      • 9.8.5.3. Product Benchmarking
      • 9.8.5.4. Recent Developments
    • 9.8.6. Neptune Labs, Inc.
      • 9.8.6.1. Participant's Overview
      • 9.8.6.2. Financial Performance
      • 9.8.6.3. Product Benchmarking
      • 9.8.6.4. Recent Developments
    • 9.8.7. DataRobot, Inc.
      • 9.8.7.1. Participant's Overview
      • 9.8.7.2. Financial Performance
      • 9.8.7.3. Product Benchmarking
      • 9.8.7.4. Recent Developments
    • 9.8.8. Dataiku.
      • 9.8.8.1. Participant's Overview
      • 9.8.8.2. Financial Performance
      • 9.8.8.3. Product Benchmarking
      • 9.8.8.4. Recent Developments
    • 9.8.9. ALTERYX, INC.
      • 9.8.9.1. Participant's Overview
      • 9.8.9.2. Financial Performance
      • 9.8.9.3. Product Benchmarking
      • 9.8.9.4. Recent Developments
    • 9.8.10. GAVS Technologies N.A., Inc
      • 9.8.10.1. Participant's Overview
      • 9.8.10.2. Financial Performance
      • 9.8.10.3. Product Benchmarking
      • 9.8.10.4. Recent Developments
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