Machine Learning ML Platforms

Machine Learning ML Platforms Market Outlook

The global Machine Learning (ML) Platforms market is projected to reach a value of approximately $XX billion by 2035, growing at a remarkable compound annual growth rate (CAGR) of XX% from 2025 to 2035. This robust growth is fueled by the increasing demand for data-driven decision-making processes across various industries, the escalating need for automation in business operations, and the rising adoption of artificial intelligence technologies. The proliferation of big data analytics and cloud computing has further enhanced the capabilities of machine learning systems, allowing organizations to harness large datasets effectively. Additionally, the advancements in algorithms and computing power have made machine learning tools more accessible and efficient, thereby contributing to the market's expansion. The surge in investment in AI technologies by both startups and established companies is also promoting innovations within the ML platforms sector.

Growth Factor of the Market

The growth of the Machine Learning Platforms market is significantly impacted by multiple factors, including the pressing demand for automated analytics tools and the need to enhance operational efficiencies. Organizations are increasingly leveraging machine learning to process vast amounts of data, thereby enabling them to derive actionable insights that drive decision-making. This shift towards data-centric strategies is further supported by the ongoing digital transformation across industries, which emphasizes the importance of leveraging advanced technologies. Additionally, the growing trend of integrating machine learning with the Internet of Things (IoT) is expected to create new opportunities for businesses to optimize their operations. The ongoing advancements in cloud computing technologies are also facilitating the scalability and flexibility of machine learning solutions, making them more appealing to enterprises of all sizes. Furthermore, the rising importance of personalized customer experiences is pushing organizations to adopt machine learning tools that can analyze customer data and tailor offerings accordingly.

Key Highlights of the Market

• The global ML platforms market is projected to experience significant growth over the next decade.
• Advancements in algorithm development are making machine learning more effective and user-friendly.
• The integration of AI with various sectors is driving the demand for ML platforms.
• Cloud-based ML solutions are gaining traction due to their scalability and cost-effectiveness.
• The rise of big data analytics is pushing organizations to invest in machine learning technologies.

By Product Type

Software Tools:

Software tools form a crucial segment of the Machine Learning Platforms market, providing users with the necessary functionalities to build, train, and deploy machine learning models. These tools often include features like data preprocessing, algorithm selection, model training, and evaluation metrics. The growth of this segment is significantly influenced by the increasing number of developers and data scientists looking for efficient ways to implement machine learning solutions. The user-friendly interfaces and robust capabilities of these software tools allow businesses to create customized models tailored to their specific needs. Furthermore, ongoing innovations in software tools enhance their capabilities, making them integral to organizations aiming to leverage machine learning for competitive advantage.

Cloud-based Services:

Cloud-based services represent a rapidly growing segment within the ML platforms market, driven by the shift towards cloud computing and the demand for scalable solutions. These services enable organizations to access powerful machine learning tools and resources without the need for substantial upfront investment in infrastructure. The flexibility and scalability offered by cloud-based ML services allow companies to rapidly adjust their computing power based on project needs. This aspect is particularly appealing to small and medium-sized enterprises (SMEs) that may not possess the resources to maintain on-premises infrastructure. Additionally, cloud-based services often come with built-in security measures and compliance features, further enhancing their attractiveness to businesses concerned about data protection.

Managed Services:

Managed services in the Machine Learning Platforms market provide organizations with expert support and guidance in implementing and optimizing machine learning solutions. These services are particularly beneficial for businesses that may lack the in-house expertise or resources to manage complex ML environments. By partnering with managed service providers, organizations can leverage their technical expertise to ensure the successful deployment of machine learning applications. This segment is expected to grow as organizations increasingly recognize the value of outsourcing specialized tasks to enhance their operational efficiencies. The ability to focus on core business functions while relying on external experts for machine learning implementation is becoming a common strategy among businesses seeking to innovate.

APIs:

Application Programming Interfaces (APIs) play a significant role in the Machine Learning Platforms market by facilitating the integration of machine learning capabilities into various applications and systems. They enable developers to incorporate machine learning functions without needing extensive knowledge of underlying algorithms. The rise of APIs is particularly evident in sectors such as fintech and e-commerce, where companies leverage machine learning for tasks like fraud detection and customer segmentation. The convenience and flexibility offered by APIs are key drivers for their adoption, as they allow organizations to enhance their existing applications with sophisticated machine learning features quickly. This segment is expected to flourish as businesses seek to remain competitive by integrating advanced ML capabilities into their operations.

Model Deployment and Integration:

Model deployment and integration services are essential for organizations looking to operationalize their machine learning models effectively. This segment encompasses the processes involved in taking a model developed in a testing environment and deploying it into a production system. The ability to integrate these models with existing workflows and applications is crucial for maximizing the value derived from machine learning initiatives. As organizations continue to invest in machine learning, the demand for seamless model deployment and integration solutions will grow. This segment is increasingly recognized for its importance, as proper deployment ensures that models perform optimally and deliver the expected outcomes in real-world applications.

By Application

Predictive Maintenance:

Predictive maintenance is an application of machine learning that enables organizations to foresee potential equipment failures and schedule maintenance accordingly. By analyzing historical data, machine learning algorithms can identify patterns and predict when a machine is likely to fail, thus allowing businesses to take proactive measures. This application is particularly valuable in industries such as manufacturing and transportation, where unplanned downtime can lead to significant losses. The increasing focus on operational efficiency and cost reduction is driving the adoption of predictive maintenance solutions, as organizations seek to minimize disruptions and maximize uptime through proactive maintenance strategies.

Fraud Detection:

Fraud detection is another critical application of machine learning, especially in sectors like banking, finance, and e-commerce. Machine learning algorithms can analyze vast amounts of transaction data in real-time to identify unusual patterns or anomalies that may indicate fraudulent activity. The adaptability of machine learning models allows them to improve over time, learning from new data and enhancing their detection capabilities. As cyber threats continue to evolve, the need for robust fraud detection systems is becoming increasingly urgent, prompting organizations to invest in machine learning solutions that offer advanced analytics to safeguard against fraud.

Sales and Marketing Optimization:

Sales and marketing optimization through machine learning involves leveraging data to enhance customer targeting, improve campaign effectiveness, and ultimately increase revenue. By analyzing customer behavior and preferences, machine learning models can inform strategies that drive higher conversion rates and greater customer satisfaction. This application is particularly valuable in industries like retail and e-commerce, where understanding consumer behavior is crucial for success. The growing emphasis on personalized marketing strategies is propelling the demand for machine learning solutions that can analyze customer data and deliver tailored marketing messages, thereby optimizing sales funnels.

Customer Segmentation:

Customer segmentation is a vital application of machine learning that allows businesses to categorize their customer base into distinct groups based on behaviors, preferences, and demographics. This segmentation enables organizations to tailor their marketing efforts and product offerings to meet the specific needs of different customer segments. The insights derived from machine learning algorithms facilitate targeted marketing campaigns, leading to improved customer engagement and retention. The increasing focus on personalized customer experiences is driving the adoption of customer segmentation tools powered by machine learning, as organizations strive to enhance their marketing effectiveness and customer loyalty.

Image Recognition:

Image recognition is an advanced application of machine learning that involves analyzing and interpreting visual data. This technology is utilized across various sectors, including healthcare for medical imaging, retail for inventory management, and social media for content tagging. The capabilities of machine learning in image recognition are advancing rapidly, enabling organizations to automate processes that were once labor-intensive. The growing demand for visual data analysis and the proliferation of image-driven content are driving investments in machine learning solutions that offer robust image recognition capabilities. As a result, this application is expected to see substantial growth in the coming years.

By Distribution Channel

Direct Sales:

Direct sales represent a primary distribution channel for machine learning platforms, allowing organizations to purchase solutions straight from vendors. This channel is favored by businesses that prefer personalized service and direct interactions with the provider, facilitating tailored solutions that meet specific needs. Direct sales often involve comprehensive consultations, enabling vendors to understand client requirements fully and provide customized solutions. Organizations appreciate the ability to negotiate terms directly and establish relationships with vendors, which can lead to ongoing support and tailored updates post-purchase. As businesses increasingly recognize the value of machine learning, the demand for direct sales channels is anticipated to grow significantly.

Indirect Sales:

Indirect sales channels encompass various partnerships, including resellers, system integrators, and value-added distributors, to market machine learning platforms. These channels are essential for reaching a broader audience, particularly for vendors aiming to penetrate new markets or regions quickly. Indirect sales can provide businesses with additional resources and expertise that enhance the implementation of machine learning solutions. By leveraging established networks and relationships, vendors can expand their reach and increase market penetration, ultimately driving sales growth. The growing complexity of machine learning solutions is fostering greater reliance on indirect sales channels as organizations seek trusted partners to guide them through implementation.

By Industry Vertical

IT and Telecom:

The IT and telecom sector is one of the frontrunners in adopting machine learning platforms, leveraging the technology to enhance operational efficiencies and improve customer service. Machine learning is used for various applications, including network optimization, predictive maintenance, and customer support automation. The ability to analyze vast amounts of data generated by telecom networks enables companies to identify patterns, predict failures, and optimize service delivery. Furthermore, with the increasing complexities of IT infrastructures, organizations are increasingly relying on machine learning solutions to gain actionable insights that drive innovation and streamline operations, making this sector a significant contributor to the overall ML platforms market.

Healthcare:

Machine learning platforms are revolutionizing the healthcare industry by improving diagnostic accuracy, optimizing treatment plans, and enhancing patient outcomes. Hospitals and research institutions are utilizing machine learning algorithms to analyze patient data, identify trends, and predict health risks. This application is critical in developing personalized medicine strategies that cater to individual patient needs. Additionally, machine learning is proving invaluable in drug discovery and clinical trials, helping researchers sift through vast datasets more efficiently. As the healthcare sector continues to embrace digital transformation, the demand for innovative machine learning solutions is expected to soar, significantly impacting the market landscape.

Retail:

The retail industry is increasingly adopting machine learning platforms to enhance customer experiences and streamline operations. Retailers leverage machine learning for customer analytics, inventory management, and personalized marketing strategies. By analyzing consumer behavior and preferences, retailers can optimize their product offerings and marketing campaigns, resulting in improved sales performance. Machine learning also aids in demand forecasting, ensuring that retailers maintain optimal inventory levels and minimize stockouts. The growing trend towards e-commerce and the demand for personalized shopping experiences are driving the adoption of machine learning technologies in the retail sector, making it a crucial market vertical.

Manufacturing:

In the manufacturing sector, machine learning platforms are playing a pivotal role in enhancing productivity and operational efficiency. By implementing predictive maintenance and quality control systems powered by machine learning, manufacturers can minimize equipment downtime and ensure product quality. The ability to analyze production data in real-time allows for quick adjustments and optimizations in manufacturing processes. This sector's ongoing trend towards automation and smart factories is further driving the demand for machine learning solutions that can provide valuable insights and enhance decision-making. As manufacturers look to innovate and remain competitive in a rapidly evolving market, the adoption of machine learning technologies is expected to grow substantially.

BFSI:

The Banking, Financial Services, and Insurance (BFSI) sector is at the forefront of machine learning adoption, utilizing the technology to improve risk assessment, fraud detection, and customer service. Financial institutions are employing machine learning algorithms to analyze transaction data, enabling them to identify fraudulent activities and assess creditworthiness more accurately. The ability to leverage large datasets for predictive analytics is transforming how financial services operate, allowing for more personalized customer experiences and enhanced operational efficiency. As regulatory pressures increase and cybersecurity threats evolve, the demand for advanced machine learning solutions in the BFSI sector is anticipated to rise significantly, shaping the future landscape of financial services.

By Region

The regional analysis of the Machine Learning Platforms market reveals distinct growth patterns across various geographies. North America is currently leading the market, holding a significant share due to the presence of key technology players and a robust investment landscape in artificial intelligence. The market in this region is projected to grow at a CAGR of XX% from 2025 to 2035, driven by the increasing adoption of machine learning technologies across multiple industries, including healthcare, finance, and retail. Moreover, the regional emphasis on digital transformation and innovation is expected to further enhance the uptake of ML platforms, solidifying North America's position as a dominant force in the global market.

In comparison, the Asia Pacific region is witnessing rapid growth in the machine learning platforms market, driven by burgeoning economies and increasing investments in advanced technologies. This region is anticipated to experience a CAGR of XX% during the forecast period, propelled by the rising demand for machine learning solutions in sectors such as IT, healthcare, and manufacturing. The growing penetration of the internet and mobile technologies in countries like India and China is further accelerating the adoption of machine learning, as businesses leverage these solutions for competitive advantage. As organizations seek to enhance productivity and efficiency, the Asia Pacific market is expected to contribute significantly to the global machine learning platforms landscape.

Opportunities

The opportunities within the Machine Learning Platforms market are vast and varied, driven by technological innovations and evolving business needs. One of the most significant opportunities is the integration of machine learning with emerging technologies such as the Internet of Things (IoT) and blockchain. The synergy between machine learning and IoT presents organizations with the ability to gather and analyze real-time data, enabling them to make informed decisions swiftly. As more devices become interconnected, the volume of data generated will increase exponentially, creating a greater need for sophisticated machine learning solutions to process and extract insights from this data. Furthermore, as businesses transition towards more automated systems, the demand for machine learning platforms that facilitate automation is expected to rise, providing vendors with new avenues for growth.

Another promising opportunity lies in the increasing emphasis on sustainability and ethical AI. As organizations become more aware of their environmental impact, there is a growing demand for machine learning solutions that support sustainable practices. Companies are seeking ways to utilize machine learning to optimize resource consumption, reduce waste, and enhance supply chain efficiencies. Additionally, the focus on ethical AI is creating opportunities for vendors to develop machine learning solutions that prioritize transparency, fairness, and accountability. Organizations are increasingly looking for technology partners who can help them navigate the complexities of ethical AI, leading to potential collaborations and innovations in this space. Overall, the evolving landscape presents numerous opportunities for stakeholders in the machine learning platforms market.

Threats

While the machine learning platforms market is poised for growth, it is not without its threats. One of the significant challenges is the issue of data privacy and security. As organizations increasingly rely on machine learning to process sensitive data, the risk of data breaches and misuse becomes a pressing concern. Regulatory frameworks such as GDPR and CCPA place stringent requirements on how data is managed and protected, and failure to comply can lead to severe penalties. Organizations must navigate these regulatory landscapes carefully, ensuring that their machine learning implementations prioritize data security and privacy. Additionally, as the complexity of machine learning models increases, organizations may face challenges in interpreting results and understanding the decision-making processes of these models, leading to potential mistrust and hesitancy in adoption.

Another notable threat is the shortage of skilled talent within the machine learning domain. Despite the growing demand for machine learning expertise, there remains a significant skills gap in the market. Organizations struggle to find qualified professionals with the necessary knowledge to develop and implement machine learning solutions effectively. This talent shortage can hinder innovation and slow down the adoption of machine learning technologies, limiting the overall growth of the market. Furthermore, as competition intensifies among vendors, there is a risk of market saturation, where companies may resort to undercutting prices, potentially leading to reduced profit margins. As the machine learning landscape continues to evolve, addressing these threats will be crucial for ensuring sustained market growth.

Competitor Outlook

• Google Cloud AI
• IBM Watson
• Microsoft Azure Machine Learning
• Amazon Web Services (AWS) Machine Learning
• DataRobot
• H2O.ai
• Alteryx
• Salesforce Einstein
• Oracle Cloud Infrastructure
• SAS Viya
• RapidMiner
• TensorFlow
• KNIME
• BigML
• Domino Data Lab

The competitive landscape of the Machine Learning Platforms market is characterized by a diverse array of players, ranging from established technology giants to innovative startups. Major corporations such as Google, Microsoft, and Amazon lead the charge, continually enhancing their machine learning offerings to maintain a competitive edge. These companies invest heavily in research and development to deliver advanced algorithms, user-friendly interfaces, and comprehensive analytics tools. Their cloud-based platforms allow organizations to access powerful machine learning capabilities without significant upfront investments, driving widespread adoption across industries. Additionally, these technology leaders are actively pursuing partnerships and collaborations to broaden their service offerings and expand their customer base.

In addition to the tech giants, a host of specialized companies are making significant strides in the machine learning platforms market. Firms like DataRobot and H2O.ai focus on automating machine learning processes, enabling organizations to build predictive models with minimal effort. These companies capitalize on the growing demand for user-friendly solutions that cater to organizations lacking in-house data science expertise. Moreover, established players like IBM and SAS leverage their extensive experience in analytics to offer robust machine learning platforms, providing clients with trusted, enterprise-grade solutions. The presence of these niche players enhances competition, driving innovation and forcing all market participants to continuously improve their offerings.

The market also sees the emergence of startups that are introducing disruptive technologies and novel approaches to machine learning. These startups often focus on specific applications or industries, filling gaps left by larger players. For example, companies like RapidMiner and KNIME prioritize open-source solutions, catering to organizations that seek flexibility and cost-effectiveness in their machine learning deployments. This trend towards specialized and niche solutions is indicative of the broader market's evolution, where customization and adaptability are becoming increasingly important. As the competitive landscape continues to evolve, organizations will need to stay ahead of the curve by continuously monitoring market developments and identifying potential partnerships or acquisitions to enhance their offerings.

• 1 Appendix

  • 1.1 List of Tables
  • 1.2 List of Figures

• 2 Introduction

  • 2.1 Market Definition
  • 2.2 Scope of the Report
  • 2.3 Study Assumptions
  • 2.4 Base Currency & Forecast Periods

• 3 Market Dynamics

  • 3.1 Market Growth Factors
  • 3.2 Economic & Global Events
  • 3.3 Innovation Trends
  • 3.4 Supply Chain Analysis

• 4 Consumer Behavior

  • 4.1 Market Trends
  • 4.2 Pricing Analysis
  • 4.3 Buyer Insights

• 5 Key Player Profiles

  • 5.1 BigML
    • 5.1.1 Business Overview
    • 5.1.2 Products & Services
    • 5.1.3 Financials
    • 5.1.4 Recent Developments
    • 5.1.5 SWOT Analysis
  • 5.2 KNIME
    • 5.2.1 Business Overview
    • 5.2.2 Products & Services
    • 5.2.3 Financials
    • 5.2.4 Recent Developments
    • 5.2.5 SWOT Analysis
  • 5.3 H2O.ai
    • 5.3.1 Business Overview
    • 5.3.2 Products & Services
    • 5.3.3 Financials
    • 5.3.4 Recent Developments
    • 5.3.5 SWOT Analysis
  • 5.4 Alteryx
    • 5.4.1 Business Overview
    • 5.4.2 Products & Services
    • 5.4.3 Financials
    • 5.4.4 Recent Developments
    • 5.4.5 SWOT Analysis
  • 5.5 SAS Viya
    • 5.5.1 Business Overview
    • 5.5.2 Products & Services
    • 5.5.3 Financials
    • 5.5.4 Recent Developments
    • 5.5.5 SWOT Analysis
  • 5.6 DataRobot
    • 5.6.1 Business Overview
    • 5.6.2 Products & Services
    • 5.6.3 Financials
    • 5.6.4 Recent Developments
    • 5.6.5 SWOT Analysis
  • 5.7 IBM Watson
    • 5.7.1 Business Overview
    • 5.7.2 Products & Services
    • 5.7.3 Financials
    • 5.7.4 Recent Developments
    • 5.7.5 SWOT Analysis
  • 5.8 RapidMiner
    • 5.8.1 Business Overview
    • 5.8.2 Products & Services
    • 5.8.3 Financials
    • 5.8.4 Recent Developments
    • 5.8.5 SWOT Analysis
  • 5.9 TensorFlow
    • 5.9.1 Business Overview
    • 5.9.2 Products & Services
    • 5.9.3 Financials
    • 5.9.4 Recent Developments
    • 5.9.5 SWOT Analysis
  • 5.10 Domino Data Lab
    • 5.10.1 Business Overview
    • 5.10.2 Products & Services
    • 5.10.3 Financials
    • 5.10.4 Recent Developments
    • 5.10.5 SWOT Analysis
  • 5.11 Google Cloud AI
    • 5.11.1 Business Overview
    • 5.11.2 Products & Services
    • 5.11.3 Financials
    • 5.11.4 Recent Developments
    • 5.11.5 SWOT Analysis
  • 5.12 Salesforce Einstein
    • 5.12.1 Business Overview
    • 5.12.2 Products & Services
    • 5.12.3 Financials
    • 5.12.4 Recent Developments
    • 5.12.5 SWOT Analysis
  • 5.13 Oracle Cloud Infrastructure
    • 5.13.1 Business Overview
    • 5.13.2 Products & Services
    • 5.13.3 Financials
    • 5.13.4 Recent Developments
    • 5.13.5 SWOT Analysis
  • 5.14 Microsoft Azure Machine Learning
    • 5.14.1 Business Overview
    • 5.14.2 Products & Services
    • 5.14.3 Financials
    • 5.14.4 Recent Developments
    • 5.14.5 SWOT Analysis
  • 5.15 Amazon Web Services (AWS) Machine Learning
    • 5.15.1 Business Overview
    • 5.15.2 Products & Services
    • 5.15.3 Financials
    • 5.15.4 Recent Developments
    • 5.15.5 SWOT Analysis

• 6 Market Segmentation

  • 6.1 Machine Learning ML Platforms Market, By Application
    • 6.1.1 Predictive Maintenance
    • 6.1.2 Fraud Detection
    • 6.1.3 Sales and Marketing Optimization
    • 6.1.4 Customer Segmentation
    • 6.1.5 Image Recognition
  • 6.2 Machine Learning ML Platforms Market, By Product Type
    • 6.2.1 Software Tools
    • 6.2.2 Cloud-based Services
    • 6.2.3 Managed Services
    • 6.2.4 APIs
    • 6.2.5 Model Deployment and Integration
  • 6.3 Machine Learning ML Platforms Market, By Industry Vertical
    • 6.3.1 IT and Telecom
    • 6.3.2 Healthcare
    • 6.3.3 Retail
    • 6.3.4 Manufacturing
    • 6.3.5 BFSI
  • 6.4 Machine Learning ML Platforms Market, By Distribution Channel
    • 6.4.1 Direct Sales
    • 6.4.2 Indirect Sales

• 7 Competitive Analysis

  • 7.1 Key Player Comparison
  • 7.2 Market Share Analysis
  • 7.3 Investment Trends
  • 7.4 SWOT Analysis

• 8 Research Methodology

  • 8.1 Analysis Design
  • 8.2 Research Phases
  • 8.3 Study Timeline

• 9 Future Market Outlook

  • 9.1 Growth Forecast
  • 9.2 Market Evolution

• 10 Geographical Overview

  • 10.1 Europe - Market Analysis
    • 10.1.1 By Country
      • 10.1.1.1 UK
      • 10.1.1.2 France
      • 10.1.1.3 Germany
      • 10.1.1.4 Spain
      • 10.1.1.5 Italy
  • 10.2 Asia Pacific - Market Analysis
    • 10.2.1 By Country
      • 10.2.1.1 India
      • 10.2.1.2 China
      • 10.2.1.3 Japan
      • 10.2.1.4 South Korea
  • 10.3 Latin America - Market Analysis
    • 10.3.1 By Country
      • 10.3.1.1 Brazil
      • 10.3.1.2 Argentina
      • 10.3.1.3 Mexico
  • 10.4 North America - Market Analysis
    • 10.4.1 By Country
      • 10.4.1.1 USA
      • 10.4.1.2 Canada
  • 10.5 Middle East & Africa - Market Analysis
    • 10.5.1 By Country
      • 10.5.1.1 Middle East
      • 10.5.1.2 Africa
  • 10.6 Machine Learning ML Platforms Market by Region

• 11 Global Economic Factors

  • 11.1 Inflation Impact
  • 11.2 Trade Policies

• 12 Technology & Innovation

  • 12.1 Emerging Technologies
  • 12.2 AI & Digital Trends
  • 12.3 Patent Research

• 13 Investment & Market Growth

  • 13.1 Funding Trends
  • 13.2 Future Market Projections

• 14 Market Overview & Key Insights

  • 14.1 Executive Summary
  • 14.2 Key Trends
  • 14.3 Market Challenges
  • 14.4 Regulatory Landscape

Segments Analyzed in the Report

The global Machine Learning ML Platforms market is categorized based on

By Product Type

• Software Tools
• Cloud-based Services
• Managed Services
• APIs
• Model Deployment and Integration

By Application

• Predictive Maintenance
• Fraud Detection
• Sales and Marketing Optimization
• Customer Segmentation
• Image Recognition

By Distribution Channel

• Direct Sales
• Indirect Sales

By Industry Vertical

• IT and Telecom
• Healthcare
• Retail
• Manufacturing
• BFSI

By Region

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

Key Players

• Google Cloud AI
• IBM Watson
• Microsoft Azure Machine Learning
• Amazon Web Services (AWS) Machine Learning
• DataRobot
• H2O.ai
• Alteryx
• Salesforce Einstein
• Oracle Cloud Infrastructure
• SAS Viya
• RapidMiner
• TensorFlow
• KNIME
• BigML
• Domino Data Lab