Report:
The Forrester Wave™: AI/ML Platforms, Q3 2022
How does Forrester define the AI/ML Platforms market in 2022?
As enterprises evolve AI from pilots to an integral part of their tech strategy, the scope of AI expands from core data science teams to business, software development, enterprise architecture, and IT ops teams. Enterprises need a platform to make extended AI teams more productive, implement more complex use cases, and harness the fast pace of new AI technologies. AI/ML platform vendors are responding by offering platform capabilities and tools for many roles within an enterprise so that teams can develop, operationalize, and manage a growing portfolio of AI solutions. The evaluation assessed 15 vendors across 25 criteria grouped into current offering, strategy, and market presence.
Key Facts for The Forrester Wave™: AI/ML Platforms, Q3 2022 in 2022
- Publication Date: 12-Jul-2022
- Document ID:
- Summary: In our 25-criterion evaluation of AI/ML platform providers, we identified the 15 most significant ones — Amazon Web Services, C3 AI, Cloudera, Databricks, Dataiku, DataRobot, Google, H2O.ai, IBM, Microsoft, Palantir, RapidMiner, RStudio, SAS, and TIBCO Software — and researched, analyzed, and scored them. This report shows how each provider measures up and helps technology executives select the right one for their needs.
- Authors: Mike Gualtieri, Rowan Curran, Srividya Sridharan, Caroline Provost, Jen Barton
How did the AI/ML Platforms market evolve in 2022?
- As enterprises evolve AI from pilots to an integral part of their tech strategy, the scope of AI expands from core data science teams to business, software development, enterprise architecture, and IT ops teams
- Enterprises need a platform to make extended AI teams more productive, implement more complex use cases, and harness the fast pace of new AI technologies
- AI/ML platform vendors are responding by offering platform capabilities and tools for many roles within an enterprise
- Next-generation enterprise AI projects will not rest on the value of a single ML model
- Enterprises will need AI solutions that leverage multiple models in conjunction with additional intelligence technologies such as mathematical solvers, engineering models, and knowledge-engineered human decision logic
- Productivity tools, solution accelerators, and extensibility matter most in AI/ML platform selection
What product features are required to be included in this year's evaluation?
- A comprehensive, differentiated AI/ML platform solution. Vendors must offer a platform that provides tools and capabilities for AI teams to build, deploy, orchestrate, and manage ML models that are the nuggets of intelligence needed to build AI applications.
- Actively marketed as an AI/ML platform for enterprise customers. Vendors offer solutions that are specifically marketed to target enterprise customers shopping for an AI/ML platform to build custom AI solutions for the broadest number of use cases.
- Install base and revenue requirements. The vendor must have at least 10 paying, named enterprise customers using the version of the AI/ML platform that we evaluated. The vendor must also have provided Forrester with three customer references that were willing to fill out a confidential survey. Included vendors must also have proven revenue generated by customer adoption of the vendor's AI/ML platform.
- Sparked client inquiries and/or has market momentum. Forrester clients have discussed the vendors and products through inquiries; alternatively, the vendor may, in Forrester's judgment, warrant inclusion or exclusion in this evaluation because of technology trends, market presence, or lack of client interest.
What are the common features of top products in the AI/ML Platforms space?
No common features specified.
Scope Exclusions
- Vendors without a comprehensive, differentiated AI/ML platform solution
- Solutions not actively marketed to enterprise customers
- Vendors with fewer than 10 paying, named enterprise customers
- Vendors unable to provide three customer references for confidential surveys
- Vendors without proven revenue from AI/ML platform adoption
- Vendors that have not sparked client inquiries or demonstrated market momentum
Inclusion Criteria
Vendors must, among other requirements:
- A comprehensive, differentiated AI/ML platform solution that provides tools and capabilities for AI teams to build, deploy, orchestrate, and manage ML models
- Actively marketed as an AI/ML platform for enterprise customers
- At least 10 paying, named enterprise customers using the evaluated version of the AI/ML platform
- Three customer references willing to fill out a confidential survey
- Proven revenue generated by customer adoption of the vendor's AI/ML platform
- Sparked client inquiries and/or has market momentum based on technology trends, market presence, or client interest
Offering Strengths — Relative Weighting
- Data — 20%
- Training — 20%
- Inferencing — 20%
- Applications — 20%
- Architecture — 20%
Strategy Strength — Relative Weighting
- Product vision — 20%
- Market approach — 20%
- Performance — 10%
- Planned enhancements — 20%
- Partner ecosystem — 20%
- Commercial model — 10%
FAQs
Q: What does this research cover?
A: This research evaluates 15 AI/ML platform providers across 25 criteria grouped into three categories: current offering (data, training, inferencing, applications, and architecture), strategy (product vision, market approach, performance, planned enhancements, partner ecosystem, and commercial model), and market presence (revenue, number of customers, and number of employees/engineers). The evaluation identifies Leaders, Strong Performers, and Contenders in the AI/ML platforms market.
Q: Who should use this research?
A: Technology executives and AI decision-makers should use this research to evaluate and select AI/ML platform providers that best fit their needs. The research is particularly valuable for enterprises looking to: expand AI capabilities beyond pilot projects, enable extended AI teams (data scientists, ML engineers, software developers, business users, IT ops), implement industry-specific AI solutions, and build scalable, production-ready AI applications. The downloadable Excel comparison tool allows buyers to customize weightings based on their specific requirements.
Q: What are the mandatory features of vendors included in this market?
A: Vendors included in this evaluation must offer a comprehensive AI/ML platform that provides tools and capabilities for AI teams to build, deploy, orchestrate, and manage ML models needed to build AI applications. The platform must be actively marketed to enterprise customers for building custom AI solutions across the broadest number of use cases. Vendors must demonstrate market presence with at least 10 paying enterprise customers using the evaluated platform version, provide three customer references, and show proven revenue from platform adoption. Finally, vendors must have sparked Forrester client inquiries or demonstrated market momentum based on technology trends and market presence.
Q: What are some reasons for not being included in this report?
A:
- Lack of a comprehensive, differentiated AI/ML platform solution with full lifecycle capabilities
- Not actively marketed as an AI/ML platform for enterprise customers
- Insufficient install base (fewer than 10 paying, named enterprise customers)
- Unable to provide required customer references
- Lack of proven revenue from AI/ML platform adoption
- Insufficient client inquiries or market momentum
- Vendor declined to participate in the evaluation process
- Vendor only partially participated in the evaluation process
Q: What should buyers consider when evaluating products in this market?
A:
- Look for providers that offer a broad set of tools for both data science and extended AI teams, ensuring the platform satisfies and improves productivity for data scientists while enabling collaboration across multiple roles
- Evaluate vendors that have industry-specific solution accelerators in the form of training materials, sample code/flows, or ready-to-use configurable modules, particularly those with experience in your industry and/or horizontal use cases
- Choose platforms with an extensible and interoperable approach to both tools and technologies, with architecture designed to quickly bring new tools and technologies from the vendor, open source, and partners
- Assess the vendor's product roadmap for extended AI team tooling, as this area is still less mature than tooling for data science teams
- Consider how the platform enables AI solutions that leverage multiple models in conjunction with additional intelligence technologies
- Evaluate the platform's ability to help implement more complex use cases beyond single ML model deployments
Q: How has the AI/ML Platforms market evolved in 2022?
A:
- Enterprises are evolving AI from pilots to an integral part of their tech strategy
- The scope of AI is expanding from core data science teams to business, software development, enterprise architecture, and IT ops teams
- Demand for productivity tools for both data science and extended AI teams is increasing
- Industry-specific solution accelerators are becoming more important to avoid starting from scratch
- Next-generation enterprise AI projects will leverage multiple models in conjunction with additional intelligence technologies such as mathematical solvers, engineering models, and knowledge-engineered human decision logic
- Platform architecture designed for extensibility and interoperability is critical to quickly bring new tools and technologies from vendors, open source, and partners
- Enterprises need platforms to implement more complex use cases and harness the fast pace of new AI technologies
Q: What differentiates Strength of Offering vs. Strength of Strategy?
A: Strength of Offering (vertical axis) evaluates the current capabilities of each vendor's AI/ML platform across five key technical categories: data management, model training, inferencing, application building, and platform architecture. Strength of Strategy (horizontal axis) assesses the vendor's future direction and market positioning across six categories: product vision, market approach, current performance, planned enhancements, partner ecosystem, and commercial model. Offering focuses on what the platform can do today, while Strategy evaluates where the vendor is headed and how they plan to compete in the market.
Reference
- Forrester, The Forrester Wave™: AI/ML Platforms, Q3 2022, 12-Jul-2022, ID
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