To begin with, a data science platform can be defined as a software hub. All the data science works such as exploring and integrating data utilizing different resources, coding and building models so as to leverage the new-found data, installing those models into the process of production, and serving up results through the reports or applications powered by models.
On a precise note, the data science platform works as a storage of diverse tools to accommodate the entire process of data modeling. These platforms not only empower data scientists to craft refined insights from collected data from different resources; but also helps them to communicate the probable results with the clients or stakeholders.
Businesses are opting for the data science platforms in order to incorporate smart decision-making process with data analytics and enhance customer satisfaction. With ceaseless advancements of technology, the data science platform is now capable of providing better flexibility and scalability.
A smart data science platform helps the data scientists offering the building blocks to create a solution. Also, such platforms create a comfortable environment for incorporating the solutions into products and business processes. Moreover, the best platforms supports the data scientists throughout the process of data and analytics tasks which encompass interactive exploration, visualization, deployment, performance engineering data preparation and data access.
We’ve brought an exclusive list of the best data science and machine-learning platforms:
1. Alteryx Analytics
Headquartered in Irvine, CA, Alteryx Analytics is machine-learning platform that helps data scientists in structuring models in a workflow. The company has acquired a data science enterprise, Yhat, to enhance its capabilities. Yhat, the data science platform focuses mainly on model management and disposition. Alteryx analytics helps companies in nurturing a successful data analytics culture without data scientists.
2. Databricks Unified Analytics Platform
This is an Apache Spark-based platform which offers patented features for performance, operations, real-time enablement, reliability, and security on Amazon Web Services (AWS). Based out of San Francisco, CA, the Databricks Unified Analytics Platform primarily serves the open source community.
3. H2O.ai
H2O.ai is a deep machine-learning platform specially envisioned for data scientists. Situated in Mountain View, California, the leader in machine-learning unified platform offers H20 Deep Water for deep-learning, H2O Sparkling Water for Spark integration, H2O Steam and H2O Flow.
Practically an open source, H2O.ai also offers a segment for predictive analytics. Currently, the open source ML of this platform is an industry standard.
4. Microsoft Azure Learning Studio
Microsoft is one of the world’s largest software vendors. It has made its presence in the domain of data science platform market with its Azure software products. The products include Power BI, Azure Machine-learning which is inclusive of Azure Machine-learning Studio, Azure HDInsight, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics.
5. KNIME Analytics Platform
Headquartered in Zurich, Switzerland, KNIME is quite helpful in end-to-end workflows for predictive analytics and ML. This platform collects great chunks of data from huge depositories such as Google which is often used as an enterprise solution. With over 100,000 global user, KNIME Analytics, as an open-source platform, promises enhanced performance, security and collaboration in organizations. Microsoft Azure and AWS has the cloud versions of this platform.
6. Cloudera
Another popular platform is Cloudera which is augmented for the cloud and enterprise data solutions. This sophisticated platform comprises automatic data pipelines. It also supports full Hadoop authentication and encryption. The excellent work that can be done with Cloudera is to run the different types of delicate data by allowing Spark queries within a safe environment.
7. RapidMiner
A product of a Boston-based company of same name, the RapidMiner platform comes with RapidMiner Radoop which helps in enhancing the functional competencies to a Hadoop environment. RapidMiner Studio is designed for model development, while RapidMiner Server enables the data scientists to share, collaborate and uphold the models. RapidMiner presents new performance and productivity capabilities to model development and execution in an excellent way.
Conclusion
With the overflow of data everywhere, data science platforms are the need of the hour. Many industries have opted for data science platform so as to maintain, manage, and preserve their data in recent years. Data science platforms are used by industries such as information technology, healthcare and life sciences, banking, financial services, and insurance (BFSI), Research, Manufacturing, and Energy and Utilities. The increasing adoption of data analytical tools has surged the data science platform market like never before.
Image Credit: Data Science Platforms
Source: https://datafloq.com/read/the-top-7-best-data-science-platforms-2020/8475
