The Forrester Wave™: AI/ML Platforms, Q3 2022

AI/ML Platforms Vendor Product Comparisons 2022

Key features and pros and cons of top AI/ML Platforms products:

C3 AI - Leaders

Pros:

Data ingestion, preparation, training, deployment, and monitoring capabilities
C3 'type system' enables sophisticated data models for business use cases with extreme reuse
Scalability of platform brings together data science, legacy IT, and operational technology
Industry solution accelerators for quick implementation
Runs on every cloud platform

Cons:

Model evaluation and explainability need improvement
Needs additional differentiated algorithmic methods
Requires more scalable training architecture

DataRobot - Leaders

Pros:

Data preparation tools and functionality
Model evaluation and explanation capabilities
ModelOps functionality
Application building capabilities
Low barrier to entry and ease of use
Model documentation and explainability
Go-to-market approach covering companies just starting with AI to those accelerating AI initiatives

Cons:

Needs more tooling to augment and enrich data for modeling
Requires plug-in framework for partners to add platform capabilities
Must deliver on recent industry cloud launch to keep up with competitors

Palantir - Leaders

Pros:

Data exploration, preparation, annotation, and training capabilities
Workflow includes tools for business users to define requirements upfront
Breadth of capabilities within the platform
Security and governance of ML
Solution-centric vision for quick AI implementation
Expanding technology and implementation partnerships

Cons:

Needs plug-in framework to enable partners to add platform capabilities
Pricing transparency needs improvement for both initial implementation and additional use cases

AWS - Strong Performers

Pros:

AI as a service for text and image analysis
Data annotation tools
Inferencing capabilities
Security features
Breadth of features to build AI applications
Flexibility from being built on AWS
No-code model building tool for extended AI teams

Cons:

Needs better integration of other AWS services for app development and model ops
Requires plug-in framework for partners to add platform capabilities
Vision needs to expand to create unified AI umbrella service

Databricks - Strong Performers

Pros:

Data exploration and preparation
Training capabilities
Performance
ModelOps functionality
Flexibility to integrate into existing data pipelines
Ability to scale ML projects
Unified data and machine learning platform

Cons:

Needs more solution accelerators
Requires additional tools for extended AI teams
Data annotation capabilities need improvement
Vision must address broader personas beyond data-centric workloads

Dataiku - Strong Performers

Pros:

Tooling for data exploration and model development for both code and GUI interfaces
Thoughtful governance capabilities connecting collaboration on model approvals, monitoring, and compliance
Self-service and collaboration for many personas across ML lifecycle
Platform designed for all roles on AI teams from business users to IT ops

Cons:

Native security capabilities need improvement
Model inferencing targets and performance need enhancement
Planned enhancements needed to deepen appeal for data scientists, ML engineers, and application developers

Google - Strong Performers

Pros:

Data preparation capabilities
World-class deep learning ML including AutoML
AI as a service offerings
Integration with broader GCP
Scalable infrastructure
Benefits from AI research investments

Cons:

Needs deeper link between cloud service tools for data exploration
Requires more GUI-based tooling for model and application development
Planned enhancements to enable extended AI team members beyond data scientists are still needed

IBM - Strong Performers

Pros:

Data management capabilities
Comprehensive tools and solution accelerators
Security features
Runtime flexibility
Tooling for building customized models
Capability to build business decisions within platform
Years of innovation and deep understanding of AI development lifecycle

Cons:

Watson brand needs refreshing or replacement
Needs more integrated tooling for building applications
Requires plug-in framework for partners to add platform capabilities

Microsoft - Strong Performers

Pros:

AI-as-a-service offerings
Novel training methods
Data annotation tools
Inferencing capabilities
Security features
Breadth of features to build AI applications

Cons:

Needs better integration with other Azure services for app development, data management, and partner plug-ins
Must better clarify product vision to show how platform capabilities will be unified
Multiple entry points create confusion (Azure Machine Learning, Cognitive Services, OpenAI Service)

SAS - Strong Performers

Pros:

Capabilities for exploring and augmenting data
Outstanding tools and methods for training and evaluating models
Reputation for deep knowledge in analytics and machine learning
Platform performance capabilities
Vision for responsible and effective AI
Support for advanced AI needs like synthetic data

Cons:

Platform architecture needs increased ease of extensibility
Requires more comprehensive tooling to build applications
Must provide more application development capabilities to appeal to extended AI teams including developers, architects, and IT operations
Lacks momentum in market

TIBCO Software - Strong Performers

Pros:

Tooling for data exploration and preparation
ModelOps capabilities
Security features
Integration with TIBCO Streaming and Spotfire
Code, no-code, and AutoML options
Deep experience in high-value AI use cases like manufacturing and financial services

Cons:

Needs more tooling and functionality for performance in model training
Model explanation and evaluation capabilities need improvement
Should recruit more partners to build industry-specific AI solutions on platform

Cloudera - Contenders

Pros:

Data management capabilities
Tools for coding-first data scientists
Runtime flexibility
Security features
Ease of use for data engineer/data scientist collaboration
Unified data platform foundation

Cons:

Needs tooling for no-code AI team members, particularly for augmenting data and building/managing features
ModelOps capabilities need improvement
Vision for AI is still data-centric rather than AI application-centric
Should develop pluggable app framework for AI solutions and address broader user personas

H2O.ai - Contenders

Pros:

Training capabilities
Inferencing functionality
ModelOps features
Driverless AI functionality for accelerating AI projects
World-class AutoML for classification and deep learning
Partnership approach for ensuring customer success

Cons:

Data management needs improvement
Security capabilities need enhancement
Requires pluggable architecture for partners
Must build tooling for extended AI teams beyond data scientists
Needs to shift focus to platform buyers

RapidMiner - Contenders

Pros:

Strong capabilities for training models
Operationalizing models in production
Ease of use and comprehensive analytical methods
Easy to acquire and implement across multiple environments
Straightforward integrations into other tools

Cons:

Needs more comprehensive tooling for building applications
Performance potential for model training needs improvement
Faces competition from well-funded rivals with larger teams
Must make additional across-the-board investments to compete as enterprise AI platform

RStudio - Contenders

Pros:

Tooling for code-first data scientists
Design and runtime options for insights and applications
Ease of bridging gap from data science to production applications
Enterprise support for open source environment
Attractive price point
Support for both R and Python

Cons:

Needs tooling for more enterprise personas beyond code-first data scientists
Performance for training and inferencing needs improvement
Must expand platform pluggability to maintain competitiveness
Requires more platform capabilities beyond what's emerging from open source

FAQs

Q: Which vendors are Leaders in 2022?

A: C3 AI, DataRobot, Palantir

Q: What does "Leader" mean in Forrester Wave terms?

A: Leaders in the Forrester Wave demonstrate strong current offerings and strategy. They have the most comprehensive and differentiated AI/ML platform solutions that provide tools and capabilities for AI teams to build, deploy, orchestrate, and manage ML models. Leaders excel in key areas such as data management, training, inferencing, applications, and architecture. They also show vision for the future of enterprise AI, strong market presence, robust partner ecosystems, and proven customer success. Leaders are best positioned to help enterprises scale AI from pilots to enterprise-wide implementations with tools for both data science teams and extended AI teams including business users, developers, architects, and IT operations.

Q: How do I interpret a Forrester Wave?

A: https://www.forrester.com/policies/forrester-wave-methodology/

Report:

Top Vendor Products for AI/ML Platforms in 2022

AI/ML Platforms Vendor Product Comparisons 2022

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FAQs

Q: Which vendors are Leaders in 2022?

Q: What does "Leader" mean in Forrester Wave terms?

Q: How do I interpret a Forrester Wave?

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