Data Science / ML / AI Platform

AI platforms (also called machine learning platforms or data science platforms) allow users to analyze data and process data, build machine learning models, deploy and maintain these models.

To be categorized as an AI platform, a product must be able to:

  • Work with a variety of use cases, should not be specific to one industry
  • Allow users to build, deploy and maintain models that power business decisions
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Compare Data Science / ML / AI Platforms
Results: 61

AIMultiple is data driven. Evaluate 61 products based on comprehensive, transparent and objective AIMultiple scores. For any of our scores, click the icon to learn how it is calculated based on objective data.

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72.54450319256041
94.05232254262928
0.6486482463722604
100
1.095436507936508
51.03668384249156
top10
4star
Pega Platform
4.23
100%
100%
100%
= 3 reviews
= 10 employees
= 100,000 visitors

A recognized leader in artificial intelligence, digital process automation, and customer engagement, Pega powers enterprise digital transformation with a unified, no-code platform

54.49298128635421
71.2361452399114
0.09459465820375536
74.58538907819003
37.43472222222222
37.74981733279702
top10
true
4star
Google Cloud Machine Learning Engine Free trial available
4.32
100%
100%
14%
= 3 reviews
= 10 employees
= 100,000 visitors

Start building your machine learning projects using AI Platform Notebooks.

54.49141733469331
71.75614135788585
1.7567565965441574
73.08876453190376
100
37.22669331150076
top10
top5 , top10
true
4star
IBM Watson Studio Free trial available
4.10
100%
100%
100%
= 3 reviews
= 10 employees
= 100,000 visitors

Together with IBM Watson Machine Learning, IBM Watson Studio is a leading data science and machine learning platform

50.62005148551317
67.08263072164485
100
66.9782862287002
37.43472222222222
34.157472249381506
top5 , top10
top10
5star
TensorFlow
4.50
100%
100%
100%
= 3 reviews
= 10 employees
= 100,000 visitors

49.33535338595778
65.0088877199228
0.18918931640751072
65.96086387279848
100
33.661819051992765
top5 , top10
4star
IBM Watson Machine Learning
4.10
100%
100%
29%
= 3 reviews
= 10 employees
= 100,000 visitors

IBM Watson Machine Learning (WML) Service enables you to create, train, and deploy self-learning models using an automated, collaborative workflow.

39.72753787608514
51.07252530856295
5.945945584277899
53.88445849856773
8.091865079365078
28.382550443607336
top5 , top10
4star
Salesforce Einstein
4.20
100%
100%
100%
= 3 reviews
= 10 employees
= 100,000 visitors

Work smarter with artificial intelligence that's built right into Salesforce. Get more done with Einstein AI, your smart CRM assistant.

37.85258940709736
48.73563095359175
0.4324325555264692
51.83203862793543
0.018055555555555557
26.969547860602965
5star
Starmind
4.80
93%
100%
66%
= 3 reviews
= 10 employees
= 100,000 visitors

37.16130998962507
47.91845675916511
0.18918931640751072
50.9709550492974
0.002777777777777778
26.40416322008503
5star
Deep Cognition
4.60
100%
37%
29%
= 3 reviews
= 10 employees
= 100,000 visitors

With Deep Cognition you can choose from a simple but powerful GUI where you can drag and drop neural networks and create Deep Learning models

36.27754234198982
46.93606848954016
2.16216160379746
48.754826568893165
34.72222222222222
25.61901619443949
top10
4star
Azure Machine Learning Studio
4.40
100%
100%
100%
= 3 reviews
= 10 employees
= 100,000 visitors

33.931157757123046
43.80364258108812
4.86486478237917
44.15205822644132
71.82539682539682
24.05867293315798
top5 , top10
top5 , top10
5star
Amazon SageMaker
4.50
73%
100%
100%
= 3 reviews
= 10 employees
= 100,000 visitors

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models

Market Presence Metrics

Popularity

Searches with brand name

These are the number of queries on search engines which include the brand name of the product. Compared to other product based solutions, Data science / ML / AI platform is more concentrated in terms of top 3 companies' share of search queries. Top 3 companies receive 88% (14% more than average) of search queries in this area.

Web Traffic

Data science / ML / AI platform is a highly concentrated solution category in terms of web traffic. Top 3 companies receive 82% (9% more than average solution category) of the online visitors on Data science / ML / AI platform company websites.

Satisfaction

Data science / ML / AI platform is highly concentrated than average in terms of user reviews. Top 3 companies receive 71% (13% more than average solution category) of the reviews on Data science / ML / AI platform company websites. Product satisfaction tends to be slightly higher for more popular Data science / ML / AI platform products. Average rating for top 3 products is 4.2 vs 4.1 for average Data science / ML / AI platform product review.

Leaders Average Review Score Number of Reviews

Maturity

IBM
Amazon Web Services (AWS)
Infosys
wipro

Number of Employees

Median number of employees that provide Data science / ML / AI platform is 71 which is 17 more than the median number of employees for the average solution category.

In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. 45 companies (4 less than average solution category) with >10 employees are offering Data science / ML / AI platform. Top 3 products are developed by companies with a total of 0.5-1M employees. However, all of these top 3 companies have multiple products so only a portion of this workforce is actually working on these top 3 products.

Learn More About Data Science / ML / AI Platform

How do we define artificial intelligence?

We can define artificial intelligence (AI) as the machines that can mimic human intelligence to perform tasks and learn from them. These tasks require human capabilities like decision-making, visual perception, and speech recognition. You can read more about this in the related section of our in-depth AI guide.

Which technologies do AI platforms involve?

By using AI platforms, businesses can create machine learning models with ease. For example, these are some of the common machine learning approaches that businesses rely on while using AI platforms:

  • Neural Networks: Neural networks are a set of algorithms and mathematical models that aim to mimic the human brain. It performs a particular task without using explicit instructions, relying on patterns and inferences. To create successful neural network models, businesses should identify what they want to do and decide if their available data is reliable enough.
  • Transfer Learning: AI platforms can be used as a tool for transfer learning instead of creating a new model from scratch. When there is not enough data or time to train data, transfer learning enables businesses to benefit from a previously used AI model for a different task.
  • Explainable AI: The advances of AI technologies also require creating understandable models. With Explainable AI, businesses can generate self-explanatory models that help them understand how their AI algorithms work and why they come up with particular results.
  • Reinforcement Learning: Rather than traditional learning, reinforcement learning doesn’t look for patterns to make predictions. It makes subsequent decisions to maximize its reward, and it learns by experience. AI platforms can also benefit from this technology while creating new algorithms or models.

These machine learning techniques can be combined with symbolic (i.e. human-readable) approaches to solve problems in various domains:

  • Natural Language Processing (NLP): This technology helps businesses to process and evaluate large volumes of data with natural language understanding, natural language generation, and speech recognition.
  • Computer Vision: Businesses can automate specific tasks that require visual perception as humans do. Computer vision tasks include methods for acquiring, processing, analyzing, and understanding digital images, and extraction of high-dimensional data from the real world. Object recognition, motion estimation, and image restoration are a few examples of this technology.
  • Cloud Systems: A robust cloud infrastructure provides improved scalability and access to resources for the implementation of complex AI and machine learning solutions. Considering large amounts of data, businesses need to combine both AI and cloud to make full use of their advantages.

How does it work?

AI platforms can be separated into three main layers for enabling businesses to deploy machine learning models from a broad range of frameworks, languages, platforms, and tools. These three layers are:

  • Data and Integration
  • Experimentation
  • Operations and Deployment

You can read the related section of our AI platforms guide to learn more about these layers.

Why is it important now?

With the increasing number of citizen data scientists, and increasing data availability, accessibility and ease-of-use of advanced analytical resources become critical. AI platforms are valuable resources for democratizing building and maintenance of ML models (i.e. offering solutions for handling the end-to-end machine learning development cycle). Without these platforms, companies would need to spend a significant share of resources on developing and maintaining machine learning models.

What are typical AI platform use cases?

These platforms can be implemented in any situation where machine learning is involved. Some common use cases are include:

Feel free to visit our AI use cases/applications article for 100+ examples.

How will AI platforms evolve in the future?

The evolution of AI platforms is highly connected to the future of AI. We are bullish about AI approaches becoming more accurate and effective due to the factors listed below. In addition, we expect AI platforms to further automate manual aspects of machine learning such as feature engineering by incorporating mature capabilities of auto ML software. Feel free to visit our AutoML vendor list, if you are interested.

  • Advances in computing power: AI platforms will be able to handle more complicated machine learning models with advances in computing power. These advances include AI-powered chips, quantum computing, and intelligent GPUs.
  • The growing amount of data: The amount of data available for businesses rapidly grows every day.
  • Advances in algorithm design: With better algorithm designs, AI platforms will offer more accurate AI-powered solutions to improve business performance. To achieve this, research on Explainable AI, transfer learning, and reinforcement learning is still ongoing.
  • Advances in tools that enable AI model development: With new technologies like automated machine learning (AutoML), AI platforms can create new machine learning models automatically and continuously improve their performance without human intervention.

To learn more about the future of AI, feel free to read our in-depth guide.