KuppingerCole Report
Market Compass
By Anne Bailey

AI Service Clouds

The KuppingerCole Market Compass provides an overview of the product or service offerings in a certain market segment. This Market Compass covers the Artificial Intelligence (AI) Service Cloud market, focusing on the modularity of the platform, the specific coverage of key areas of the AI/machine learning (ML) development process including lifecycle management, explainability, and bias mitigation. AI Service Clouds will play an important role in democratizing AI for organizations of all sizes, for nearly every use case.
By Anne Bailey

1 Management Summary

The KuppingerCole Market Compass provides an overview of a market segment and the vendors in that segment. It covers the trends that are influencing t ...

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2 Market Segment

This Market Compass covers AI Service Clouds, which are solutions that increase enterprise accessibility to artificial intelligence technologies. AI s ...

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2.1 Market Description

AI Service Clouds provide support for key phases of the AI development and operational lifecycle, like data preparation, model selection, model traini ...

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2.2 Major Use Cases

Vendors of AI Service Clouds are not only delivering ML capabilities to their customers, they are standardizing the process for transparency, repeatab ...

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2.3 Market Direction

There are many influences on the rise of AI in enterprise usage, but an overarching one is the reduction of uncertainty. This comes in many forms, ran ...

The KuppingerCole Trend Compass shows the rise and growth of AI Service Clouds. Development and proof of concepts on AI Service Clouds began in 2014, ...

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3 Capabilities

The Market Compass is designed to profile and compare vendors across numerous capabilities. This section details the capabilities that one should expe ...

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3.1 All Capabilities

The AI Service Cloud segment has a collection of standard capabilities that most solutions should include. These are listed in the table below.

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3.2 Capabilities Recommended per use case

The AI Service Cloud segment is evolving to meet several distinct use cases, as described in section 2.2. Below, the capabilities are displayed accord ...

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3.2.1 Quick Customization

For quick customization of AI models to be achieved, a modular approach is of high importance because work can be concentrated in the necessary phases ...

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3.2.2 Modular AI

Organizations in this use case are looking to stack different types of AI capabilities on each other, extend their AI solutions as data collection bec ...

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3.2.3 MLOps

For the MLOps use case, the most important capabilities are lifecycle management and explainability. Management of the model during its implementation ...

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3.2.4 Trust & Compliance

The Trust & Compliance use case requires a solution to provide adequate and transparent reporting on the model's development and implementation. This ...

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3.2.5 Diverse Collaboration

The Divers Collaboration use case aims to democratize AI for users of different backgrounds and promote multistakeholder teams of different background ...

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4 Ratings at a Glance

This chapter provides a comparative overview of the participating vendors for five categories: security, deployment, interoperability, usability, and ...

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4.1 General Product Ratings

Based on our evaluation, a comparative overview of the ratings of the general standing of all the products covered in this document is shown in the ta ...

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4.2 Noteworthy Vendors for Specific Capabilities

Some vendors are better positioned to meet narrow use cases, while others have stronger offerings across the range of this market segment. We have ide ...

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4.2.1 Outstanding in Innovation: Microsoft and Google Cloud

Microsoft Azure AI Platform displays an innovative approach towards protecting data as it is used in model training and operations. Microsoft's resear ...

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4.2.2 Outstanding in a Modular Approach: AWS

The AWS ML Stack Amazon has broken down the AI/ML development process into modular steps, pathways for users of different areas of expertise, and pre- ...

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4.2.3 Outstanding for Cloud Environment: Cognino

Cognino, working primarily with generative models that builds contextual knowledge on a topic, provides a ready-made AI Cloud pre-trained with context ...

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5 Product/ Service Details

In the following section, each participating vendor is profiled with particular attention paid to the functionality of its product. The important capa ...

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5.1 Amazon Web Services

Amazon Web Services (AWS) is a multinational cloud service provider headquartered in Seattle, USA. It provides a complete set of machine learning and ...

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5.2 Cognino

Cognino was founded in 2018, and is based in London. The founders bring their backgrounds in AI and experience from working with Microsoft and KPMG to ...

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5.3 Google

Google, based in Mountain View, USA, has made Vertex AI, a managed machine learning platform that allows companies to accelerate the deployment and ma ...

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5.4 IBM

IBM is a global information technology company with operations in over 170 countries. IBM Cloud Pak for Data is an AI service cloud stack, comprising ...

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5.5 Kortical

Kortical was founded in 2016 in the U.K. by experienced machine learning practitioners with the philosophy of automating as much of the data science p ...

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5.6 Microsoft

Microsoft, founded in 1975 and based in Redmond, Washington, USA, is a multinational technology company. Azure AI comprises several pre-designed horiz ...

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5.7 Salesforce

Salesforce, based in San Francisco, USA and founded in 1999 is a widespread CRM solution with an AI offering: Einstein. Salesforce Einstein is designe ...

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6 Vendors to Watch

Besides the vendors covered in detail in this document, we observe some other vendors in the market that readers should be aware of. These vendors do ...

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