TOP 7 WIDELY USED DATA SCIENCE PLATFORMS
Various organizations keep floating data science platforms to simplify machine learning workflows. However, in the ever-changing data science landscape, only a few draw the attention of practitioners. Besides, due to fierce competition in the market, oftentimes platforms keep replacing one another as and when it brings new capabilities to improve organizations’ productivity.
Here are the top 7 data science platforms that are widely adopted by organizations in 2020:-
Built by the founder of Apache Spark, Databricks provides a unified analytics platform that allows data scientists to manage end-to-end machine learning workflows. The one-size-fits-all platform not only enables practitioners to explore, visualize and build superior machine learning models, but also allows them to scale it quickly with the help of collaboration.
The platforms support a wide range of languages, IDEs and notebooks. Data scientists do not have to worry about adopting new technologies as it integrates with different popular platforms like Alteryx, Azure, DataRobot, AWS SageMaker, and Dataiku. Such capabilities have helped the platform to gain a place in Gartner’s magic chart for data science as a leader.
DataRobot is a unicorn in data science that helps companies automate the workflows of machine learning through its feature-rich solutions. The company continuously strives to enhance its platform by either acquiring various companies, or by developing in-house solutions.
Apart from assisting the regular analytics workflows, DataRobot is among the best in the AutoML space. More recently, it equipped the platform with Visual AI to simplify the incorporation of image data into ML models alongside tabular and text-based data types.
Apache Spark is an open-source unified analytics engine for large-scale data processing and analyzing. It is similar to Hadoop MapReduce; it works on cluster computing, but due to exceptional speed – which is believed to be 100x faster in memory and 10x faster on disk than Hadoop – it has become popular among data scientists.
Launched in 2009, Apache Spark has emerged as a big data platform due to its superior performance. In the last ten years, the platform has been evolving by integrating with other tools to ensure better user experience.
This is another famous enterprise AI and machine learning platform that helps businesses in minimizing data processes to expedite the development of machine learning-based solutions. With Dataiku, companies can bring data analysts, engineers, and scientists together to achieve shared goals through collaboration. It also provides instant visual and statistical feedback on model performance to manage models’ lifecycle effectively.
IBM Cloud Pak for Data
Built on Red Hat OpneShift container platform, IBM Cloud Pak for Data is a fully-integrated AI platform to meet the changing needs of enterprises. It allows data scientists to unlock insights and eliminate data silos quickly. The platform has a high degree of enterprise readiness and delivers business value by enabling practitioners to integrate with other platforms using APIs. Besides, it also empowers data scientists to accelerate their development and deployment in containerized environments to improve the flexibility of AI-based solutions.
Alteryx is a self-service analytics platform that can be utilized across organizations to democratize data. The platform caters to every need of analytics professionals, such as business intelligence, data analyst, data scientist, and non-experts to assist them in quickly solving business problems. It supports analytics modelling without code and advanced modelling with algorithms.
TIBCO Software acts as a foundation for digital innovation for data-driven companies, thereby gaining a place in Gartner’s magic quadrant for 2020 as a leader. Integration among platforms has been one of the longest standing predicament for organizations. Thus, TIBCO offers a suite of products like Connect, API-Led Integration, Data Fabric, Unify, Data Science & Streaming, and more, to eliminate challenges for a streamlined data science workflow.