Omdia has named five leaders in its decision matrix for selecting vendors of artificial intelligence (AI) and machine-learning (ML) development platforms, citing C3.ai, Dataiku, IBM, Microsoft and SAS as the most prominent suppliers.
These companies all offer a wealth of AI and ML capabilities that will be essential as enterprises engage in the digital transformation of their businesses.
“At the center of the digital transformation sweeping through most enterprises resides the latest breakthroughs in ML, a branch of artificial intelligence (AI),” said Bradley Shimmin, distinguished analyst, data management and analytics, at Omdia. “ML and its sub-branch of deep learning (DL) have left the research laboratory and are being applied in real-world applications. The high-tech internet giants have taken the lead in this field and many other enterprises are now looking to reap the same benefits.”
Development platforms catering to AI and ML workloads can automate large-scale tasks that manual efforts can’t cope with effectively or manage at all, creating new business opportunities as well as transforming existing business practices. Such platforms accelerate the creation of AI- and ML-driven business outcomes through pre-built software as well as augmented and automated development tasks. For this decision matrix, Omdia is focusing on vendors that offer enterprises a platform for AI and ML development, with solutions that typically manage the entire development-to-deployment lifecycle.
AI and machine learning development platform provider matrix
Looking at the leaders in ML
C3. The company offers a comprehensive AI platform using a flexible modular approach known as MDA. This enables an abstraction layer to exist where new components inherit the rich attributes of the layer, with pre-built connections to infrastructure, data sources, and other components. C3.ai can extend and evolve custom solutions in a speedy and reliable way and at scale. This approach makes it easier for ML developers and data scientists to focus their work on core functional requirements.
Dataiku. The company’s solution is designed for enterprises, enabling multiple teams with thousands of users to access the same platform. The platform is also designed for multiple roles: technical coders and non-coders, data scientists and business domain analysts. Self-service analytics capabilities in the platform help break siloed department walls and provide a single point for accessing data, integrating with legacy data sources across hybrid infrastructure and reducing the time wasted accessing data.
IBM. With its AI and ML portfolio, IBM offers a complete end-to-end build-deploy-validate-monitor-govern lifecycle for ML applications, suitable for use by a range of technical and business roles. To get the best value out of ML in production it is important to monitor for drift, test against bias and ensure fairness in results, for which IBM offers via Watson OpenScale. IBM has the best of open source ML algorithms available curated under the hood, including all the most popular frameworks, such as TensorFlow and PyTorch, which means updates are taken care of by IBM.
Microsoft. ML model creation is a complex task requiring skills that are scarce in the market, so enterprises are looking for tools to automate it. Microsoft’s Automated ML fulfills this task by releasing data scientists to focus on the data input and output and deploying the application. Azure Machine Learning automated ML service enables enterprises to scale out their use of ML.
SAS. With a long history as a statistician’s tool of choice, SAS was well-positioned to tackle the rise of ML the role of data scientist, putting the company into a central role within the enterprise, building on the strength of its platform.