Data Science and Machine Learning Platforms

In addition to China, Alibaba has a substantial consumer base in the South and Southeast Asian markets, but not worldwide. Its current platform focuses on retail, internet, and data services applications. The product and strategy of Alibaba Cloud are ideal for skilled data scientists and data engineers in industries such as information technology, data services, retail, and government. Alibaba Cloud prioritises assistance for augmenting some DSML workflow tasks, but its platform lacks functionality and usability for citizen data scientists, which hinders its adoption by less developed businesses.

Knowledge Studio's capabilities for automated machine learning (AutoML), XAI, and increased open-source integration have been improved, but it still lags behind other manufacturers' offerings in terms of its native capabilities for delivery and deployment and model maintenance.

The extensive redesign of Alteryx is in progress. The recently announced Alteryx Analytics Hub offers a single method for organising workflow and collaboration when managing analytics and data connectivity setups.

Amazon SageMaker continues to display impressive market momentum, backed by a robust ecosystem and substantial resources.

Anaconda has achieved significant advancements in model governance and scalability. Open-source technologies are used to fuel DSML innovation through partnerships with Google, IBM, and Microsoft.

Geographically varied, Cloudera's clientele includes numerous industries and business activities.

The inclusion of Cloudera's ML product in the CDP reflects the firm's history as a big data company. The vendor's vision centres on integrating machine learning (ML) workflows across data warehousing, data engineering, DSML, and operationalization.

As evidenced by the renaming of its Spark + AI Summits to Data + AI Summits, the firm is growing beyond its perception as solely the leader of the Apache Spark community. Databricks continues to contribute to the open-source community, such as through directing the Delta Lake and MLflow projects. It has also expanded its portfolio by acquiring Redash, which enables customers to more quickly query and view data using SQL.

In August of 2020, the firm announced a Series D fundraising round of $100 million. In addition, the company has built collaborations with worldwide system integrators and vendors, such as Tableau, Snowflake, and UIPath. It has a solid plan and vision in the areas of responsible AI, collaboration, and partnership.

DataRobot provides trustworthy AI with its Humble AI programme, which enables the regulation of prediction quality. With its Use Case Value Tracker, a centralised hub for tracking ROI, the vendor prioritises quantifying business value. In November 2020, a Series F fundraising round and additional investors raised $330 million.

The release of Domino Model Monitor in 2020 demonstrates the vendor's dedication to business MLOps. Domino's market positioning is calculated, and platform R&D will continue to be centred on huge, code-first data science teams.

Google's Completeness of Vision is bolstered by its thought leadership in ML research and responsible AI, as well as its AI Platform road map. Monitoring the coherence and learning curve of Google's platform will be crucial in the following year.

H2O.ai received the highest overall grade for Completeness of Vision due to its plan and innovation. H2O.ai is an industry leader in DSML automation and enhancement, including time-series analysis.

IBM has spent years revamping its product, and competition will stay tough. Nonetheless, IBM currently offers a modern and comprehensive solution that draws on SPSS, ILOG CPLEX Optimization Studio, and prior products, as well as a steady stream of advancements from IBM Research. These demonstrate a well-rounded perspective.

KNIME continues to improve and extend its vision for bridging the gap between development and production, as well as giving additional collaboration options for data scientists and end users.

MathWorks exhibits clear vision and intellectual leadership in asset-centric sectors. It applies its inventions at scale to broad use cases designed to tackle real-world challenges. MathWorks is one of the few DSML market suppliers capable of handling big, distributed, real-time IoT deployments with a continuous environment from the edge to the cloud, as well as from development to simulation and operationalization and back.

Microsoft achieves the greatest Ability to Execute score among the major cloud service providers. Microsoft possesses a powerful blend of vision and specialised functionality for the entire range of data science experts that contribute to cross-functional teams.

The most recent capabilities and roadmap of RapidMiner are representative of significant market developments, such as multipersona cooperation, XAI, and model governance.

Brightics AI is an intuitive platform for both professionals and amateur data scientists. Due to its emphasis on data management, it is also accessible to data engineers and industrial users.

SAS is the Leader with the longest tenure in this data science and machine learning segment. Given its acute awareness of the market and its thought leadership in key areas such as composite AI, MLOps, and decision intelligence, it retains a strong and adaptable position. The firm just announced a cooperation with Microsoft to facilitate Azure integration.

Originating in the middleware industry gives TIBCO an advantage when it comes to model deployment and production in any centralised or distributed environment and for a wide range of use cases.