This month, independent analyst firm Omdia (formerly Ovum) named a Leader in Enterprise Machine Learning Development Platforms.

The Omdia Decision Matrix, 2020 (ODM) evaluated eight platform providers and named a leader with the #1 ranked technology.

What sets a technology leader in ML Development Platforms apart is the ability to tackle the entire ML lifecycle including data science, ML development, deployment, performance, scalability, and production monitoring, security, and integration.

What Omdia Says in the Report: delivers ML solutions at scale – “ has created a cross-vertical ML development platform capable of not only competing alongside global hyperscale players but also disrupting giants by affording large-scale, real-world business execution that can be performed by both experts and non-experts alike.”

The ODM report also notes that, “the value of is in making it easy for the customer to focus on value generation. Organizations that try to build their own solution out of the open source components have the challenge of integrating the pieces together, taking time away from building AI applications, which is the real value to the business. A customer cannot scale with such a DIY approach.” differentiates with a model-driven architecture

In comparison to the competition, the key differentiator noted by the Omdia report is’s model-driven architecture. No other solution provides a model-driven architecture and type system that simplifies and accelerates development of AI-based applications — from data integration and data management to model development, validation, and deployment.

The model driven architecture allows AI applications to be expressed through the following model artifacts:

  • Meta data – declares objects and their attributes
  • Annotations – indicate the underlying data stores to persist different forms of data
  • Expressions – represent functions which perform mathematical operations on input data without programming logic
  • Programming Logic – used when necessary to express logic beyond metadata, annotations and expressions

These artifacts are used to represent the following interdependent ‘layers’ of an application:

  • Data Exchange Object Model, Mappings and Transformations – metadata representing a data model optimized/denormalized for data interchange between systems, mappings and data transformations to target an application object model.
  • Application Object Model –meta data representing a data model optimized for development of applications and machine learning models.
  • Methods – business logic representing actions (i.e. enroll a customer, maintain equipment) on objects expressed in code
  • Analytic Features – expressions which perform spatial and/or temporal mathematical operations on input data
  • AI Machine Learning Model – combination of analytic features and AI-ML algorithm (i.e. NLP pipeline, logistic regression, tensor flow …)
  • Security Services – annotations and expressions applied to Objects, Methods, Features and Machine Learning Models.
  • User Interface (Optional) – metadata representing user interface fields, their bindings to objects and object properties, and user interface objects (charts, grids, etc.)

This model-driven architecture results in the following benefits to customers looking to deploy AI based applications:

Data Integration and Management

  • Consistent mechanism (using business objects) to connect and source data from various data management tools (SQL, NoSQL, File systems, Object Stores, MQTT …)
  • Mechanism for creation of a data marketplace by leveraging type system used to define business objects around data assets

Application Development and Maintenance

  • Simplify application definition by providing a unified view of infrastructure and data services and defining infrastructure requirements using a data object centric type system
  • Definition of consistent object relationships that handle interdependencies across application modules
  • Reduced code requirements via re-usable business objects (e.g. Customer, Product) defined and standardized as types
  • Benefits from inherent automation using embedded transformations as part of business objects
  • Ability to leverage out of the box connectors
  • Faster time to market due to code reuse

Scale-up and Production of AI/ML

  • Ability to tie in infrastructure requirements as part of defining ML/AI pipelines
  • Ability to encapsulate Machine Learning/AI models as types, hiding the complexity of individual AI algorithms and promoting standardization/reuse of analytic techniques
  • Ability to change underlying libraries to keep up with new innovations with little to no change in the analytics pipelines
  • Ability to allow data scientists to have a consistent view of the business/data regardless of the tools/technology preferences

Business Enablement

  • Ability to mask the complexity of the underlying infrastructure and middleware tiers for business users
  • Less dependence on IT developers to create, update and maintain business applications
  • Ability to define the business process/application, reducing the potential for miscommunication with IT and the associated rework required in creating/updating business application

What makes us proud of the Omdia rating is not only the recognition of the work the team has put in to build the C3 AI Platform‚ but more importantly‚ of the value and benefits it brings our customers and partners.

Download the Omdia evaluation of