What are our data sources?
We use the data sources on the side for ranking solutions and awarding badges in data science / ml / ai platform category. Our data sources in data science / ml / ai platform category include;
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:
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calculated based on objective data.
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We use the data sources on the side for ranking solutions and awarding badges in data science / ml / ai platform category. Our data sources in data science / ml / ai platform category include;
review websites
social media websites
search engine data for branded queries
According to the weighted combination of 7 data sources
RStudio
TensorFlow
Alteryx
RapidMiner
Pega
Taking into account the latest metrics outlined below, these are the current data science / ml / ai platform market leaders. Market leaders are not the overall leaders since market leadership doesn’t take into account growth rate.
RStudio
Alteryx
TensorFlow
RapidMiner
Pega
These are the number of queries on search engines which include the brand name of the solution. Compared to other AI Solutions categories, Data science / ML / AI platform is more concentrated in terms of top 3 companies’ share of search queries. Top 3 companies receive 47%, 30% more than the average of search queries in this area.
81 employees work for a typical company in this solution category which is 60 more than the number of employees for a typical company in the average solution category.
In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. 82 companies with >10 employees are offering data science / ml / ai platform. Top 3 products are developed by companies with a total of 800k employees. The largest company building data science / ml / ai platform is EdgeVerve Systems with more than 300,000 employees.
Taking into account the latest metrics outlined below, these are the fastest growing solutions:
TensorFlow
RStudio
Alteryx
RapidMiner
Pega
We have analyzed reviews published in the last months. These were published in 4 review platforms as well as vendor websites where the vendor had provided a testimonial from a client whom we could connect to a real person.
These solutions have the best combination of high ratings from reviews and number of reviews when we take into account all their recent reviews.
This data is collected from customer reviews for all Data science / ML / AI platform companies. The most positive word describing Data science / ML / AI platform is “Easy to use” that is used in 5% of the reviews. The most negative one is “Difficult” with which is used in 3.00% of all the Data science / ML / AI platform reviews.
According to customer reviews, most common company size for data science / ml / ai platform customers is 1,001+ employees. Customers with 1,001+ employees make up 40% of data science / ml / ai platform customers. For an average AI Solutions solution, customers with 1,001+ employees make up 36% of total customers.
These scores are the average scores collected from customer reviews for all Data science / ML / AI platforms. Data science / ML / AI platforms is most positively evaluated in terms of "Overall" but falls behind in "Ease of Use".
This category was searched on average for 3k times per month on search engines in 2022. This number has increased to 8k in 2023. If we compare with other ai solutions solutions, a typical solution was searched 3k times in 2022 and this increased to 4.1k in 2023.
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.
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:
You can read the related section of our AI platforms guide to learn more about these layers.
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.
To learn more about the future of AI, feel free to read our in-depth guide.
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.
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.
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:
These machine learning techniques can be combined with symbolic (i.e. human-readable) approaches to solve problems in various domains: