Top 17 Platforms For Data Science and Machine Learning

According to a recent study, machine learning, big data, and data science are expected to play a major role in creating jobs in the Indian market. Thankfully, the demand in these fields is still higher than the supply, which paves the way for budding as well as experienced professionals. These fields provide multiple opportunities to new learners and businesses for enhancing their growth strategy.

Keeping this in mind, we have prepared a list of platforms, as suggested by the Gartner’s 2019 Magic Quadrant for Data Science and Machine Learning Platforms.

However, before we move forward, it is important to understand what is the Magic Quadrant Report by Gartner. In this report, Gartner has categorized machine learning and data science platforms in four sections: learners, challengers, visionaries, and niche players.

Gartner believes that a data science or machine learning platform is a cohesive application that provides the ability to learn basic components of data solutions and art of integrating these solutions in processes, infrastructure, services, and products of the business.

Check out the quadrant below:


Leaders play a significant role in machine learning and data science domain. They have the ability to explore the breadth and depth of data examination, model formation, and processes. Apart from providing additional support, the leaders’ platforms are quick to change according to the changing markets. These platforms sit in the middle of market growth. They cover most of the industries, geographies, use cases, and domains. Hence, these platforms allow you to have a solid understanding of the machine learning and data science market.

Here are the platforms falling under leaders’ category:

  • RapidMiner
  • SAS
  • TIBCO Software


Challenger platforms are known for their credibility, established presence, and viability of the product. However, these platforms don’t exhibit similar thought leadership capabilities as leaders.

Gartner has further divided challengers into two parts: long-established vendors and vendors in adjacent markets. The long-established machine learning and data science vendors have stability, great customer relationships, and predictability. The adjacent market vendors are actually in the analytics, BI, and developer tool fields. They want to enter the market to extend their outreach.

Challenger platforms are well-disposed in the market but due to short-term goals, these platforms may lack strategic and innovative growth.

Here are the platforms falling under this category:

  • Alteryx
  • Dataiku


Visionaries are relatively new platforms that are entering the market. But, this group has the ability to stabilize and shape the machine learning and data science market of the future. Although the capabilities of visionaries may be regularly judged due to inefficient market experience, these players have a strong hold on innovation. With innovative methods to approach market needs, they have the ability to provide industry competitiveness.

Here are the platforms under this category:

  • Databricks
  • DataRobot
  • Google
  • ai
  • IBM
  • MathWorks
  • Microsoft

Niche Players

Niche players are platforms that focus on certain aspects. However, they are often following the leaders than becoming leaders. They follow the trends set up by the leaders to become visionaries in the field. Others, execute in a manner to effectively increase momentum and become challengers.

Here are the platforms falling under this category:

  • Anaconda
  • Datawatch
  • Domino
  • SAP


Before choosing the right platform for your data science and machine learning needs, we recommend going through the full report offered by Gartner once. They have not only ranked various platforms but also explained the features of the products offered by these partners. Since every organization and the individual learner has different requirements, it is necessary to thoroughly explore the platform that can fulfill your requirements.