What are data science and machine learning (DSML) platforms?

The amount of data being produced within companies is increasing rapidly. Businesses are realizing its importance and are leveraging this accumulated data to gain a competitive advantage. Companies are turning their data into insights to drive business decisions and improve product offerings. With data science, of which artificial intelligence (AI) is a part, users can mine vast amounts of data. Whether structured or unstructured, it uncovers patterns and makes data-driven predictions. One crucial aspect of data science is the development of machine learning models. Users leverage data science and machine learning engineering platforms that facilitate the entire process, from data integration to model management. With this single platform, data scientists, engineers, developers, and other business stakeholders collaborate to ensure that the data is appropriately managed and mined for meaning.

Types of DSML platforms

Not all data science and machine learning software platforms are designed equal. These tools allow developers and data scientists to build, train, and deploy machine learning models. However, they differ in terms of the data types supported and the method and manner of deployment. Cloud data science and machine learning platforms With the ability to store data in remote servers and easily access it, businesses can focus less on building infrastructure and more on their data, both in terms of how to derive insight from it and to ensure its quality. Cloud-based DSML platforms afford them the ability to both train and deploy the models in the cloud. This also helps when these models are being built into various applications, as it provides easier access to change and tweak the models that have been deployed. On-premises data science and machine learning platforms Cloud is not always the answer, as it is not always a viable solution. Not all data experts have the luxury of working in the cloud for several reasons, including data security and issues related to latency. In cases like health care, strict regulations, such as HIPAA, require data to be secure. Therefore, on-premises DSML solutions can be vital for some professionals, such as those in the healthcare industry and government sector, where privacy compliance is stringent and sometimes necessary. Edge platforms Some DSML tools and software allow for spinning up algorithms on the edge, consisting of a mesh network of data centers that process and store data locally before being sent to a centralized storage center or cloud. Edge computing optimizes cloud computing systems to avoid disruptions or slowing in the sending and receiving of data.

What are the common features of data science and machine learning solutions?

The following are some core features within data science and machine learning platforms that can help users prepare data and train, manage, and deploy models. Data preparation: Data ingestion features allow users to integrate and ingest data from various internal or external sources, such as enterprise applications, databases, or Internet of Things (IoT) devices. Dirty data (i.e., incomplete, inaccurate, or incoherent data) is a nonstarter for building machine learning models. Bad AI training begets bad models, which in turn begets bad predictions that may be useful at best and detrimental at worst. Therefore, data preparation capabilities allow for data cleansing and data augmentation (in which related datasets are brought to bear on company data) to ensure that the data journey gets off to a good start. Model training: Feature engineering transforms raw data into features that better represent the underlying problem to the predictive models. It is a key step in building a model and improves model accuracy on unseen data. Building a model requires training it by feeding it data. Training a model is the process of determining the proper values for all the weights and the bias from the inputted data. Two key methods used for this purpose are supervised learning and unsupervised learning. The former is a method in which the input is labeled, whereas the latter deals with unlabeled data. Model management: The process does not end once the model is released. Businesses must monitor and manage their models to ensure that they remain accurate and updated. Model comparison allows users to quickly compare models to a baseline or to a previous result to determine the quality of the model built. Many of these platforms also have tools for tracking metrics, such as accuracy and loss. Model deployment: The deployment of machine learning models is the process of making them available in production environments, where they provide predictions to other software systems. Methods of deployment include REST APIs, GUI for on-demand analysis, and more.

Page 9 | Best Data Science and Machine Learning Platforms: User Reviews from September 2026

The Cognite AI & Data Platform

The Cognite AI and Data Platform™ is a sophisticated Industrial DataOps solution specifically designed for asset-intensive industries seeking to harness the power of their operational and engineering data. Founded in 2016 and based in Tempe, Arizona, Cognite aims to facilitate the transformation of complex data environments into actionable insights that drive efficiency and innovation across various sectors. This cloud-native platform excels in ingesting and contextualizing data from a multitude of sources, including Information Technology (IT), Operational Technology (OT), and engineering systems. By creating a unified industrial knowledge graph, the Cognite AI and Data Platform integrates data from historians, Enterprise Resource Planning (ERP) systems, Computerized Maintenance Management Systems (CMMS), and even 3D models. This comprehensive approach allows organizations to standardize their data models and utilize robust APIs, enabling secure workspaces that support advanced analytics, interactive dashboards, and AI-driven applications. Targeted primarily at industries that rely heavily on operational data, such as manufacturing, energy, and utilities, the Cognite AI and Data Platform addresses specific use cases that enhance productivity and operational efficiency. For instance, organizations can leverage the platform for production optimization, where real-time data insights lead to improved throughput and reduced operational bottlenecks. Additionally, the platform supports predictive maintenance initiatives, allowing companies to anticipate equipment failures before they occur, thereby minimizing downtime and associated costs. Key features of the Cognite AI and Data Platform include its ability to transform fragmented data into a trusted and contextual foundation, which is crucial for making informed decisions. By providing a centralized repository of data, users gain full ownership and control over their information, facilitating compliance and security. Moreover, the platform’s scalability enables organizations to implement AI initiatives that can evolve with their operational needs, ensuring that they remain competitive in a rapidly changing industrial landscape. Overall, the Cognite AI and Data Platform stands out in the DataOps category by offering a comprehensive solution that not only integrates disparate data sources but also empowers organizations to unlock the full potential of their industrial data. Through its focus on contextualization and user-friendly interfaces, it provides significant value to companies looking to enhance their operational capabilities and drive long-term growth.

Average Rating: 4.8/5.0

Total Reviews: 3

Who Is the Company Behind The Cognite AI & Data Platform?

Seller: Cognite
Company Website:
Year Founded: 2016
HQ Location: Tempe, Arizona, United States
LinkedIn® Page: www.linkedin.com
760 employees on LinkedIn®

Who Uses This Product?

Company Size: 100% Large

What Are Recent G2 Reviews of The Cognite AI & Data Platform?

"Connects Nearly Any Industrial Data Source into a Common Source of Truth"

Rating: 5.0/5.0 stars

— Adam G.

Best Data Science and Machine Learning Platforms - Page 9

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