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

Pyramid

Pyramid è un sistema operativo di analisi di livello uno, di classe enterprise, che si adatta da analisi self-service per singolo utente a implementazioni centralizzate per migliaia di utenti, coprendo visualizzazioni di dati semplici ma efficaci fino a capacità avanzate di machine learning. Il sistema operativo di analisi agnostico presenta un client universale per qualsiasi dispositivo e sistema operativo. Può essere installato sulla maggior parte delle piattaforme, sia on-premises che nel cloud, e può operare contro e con la maggior parte degli stack di dati più popolari. Pyramid consente agli utenti e alle organizzazioni di bilanciare la produttività self-service e la governance. È una piattaforma analitica adattiva che fornisce diverse capacità ed esperienze basate sulle esigenze e competenze degli utenti, gestendo al contempo i contenuti come una risorsa condivisa. È progettata per supportare l'intero flusso di lavoro decisionale della tua organizzazione e fornire a tutti gli strumenti per condurre analisi self-service. Importante, il sistema operativo di analisi risolve il "problema dell'ultimo miglio", colmando il divario tra la strategia analitica dichiarata della tua organizzazione e l'effettiva implementazione analitica. Pyramid presenta sei moduli analitici distinti (Model, Formulate, Discover, Illustrate, Present e Publish), oltre a una Console Amministrativa e un Sistema di Gestione dei Contenuti, per offrire un'esperienza analitica veramente universale attraverso l'intero flusso di lavoro analitico.

Average Rating: 4.1/5.0

Total Reviews: 15

How Do G2 Users Rate Pyramid?

Applicazione: 8.3/10 (Category avg: 8.5/10)
Servizio gestito: 10.0/10 (Category avg: 8.3/10)
Comprensione del linguaggio naturale: 10.0/10 (Category avg: 8.3/10)
Facilità di amministrazione: 9.0/10 (Category avg: 8.6/10)

Who Is the Company Behind Pyramid?

Venditore: Pyramid Analytics
Anno di Fondazione: 2008
Sede centrale: Amsterdam
Twitter: @PyramidAnalytic
10,219 follower su Twitter
Pagina LinkedIn®: www.linkedin.com
104 dipendenti su LinkedIn®

Who Uses This Product?

Company Size: 47% Large, 35% Small

What Are Recent G2 Reviews of Pyramid?

"Pyramid ha molto da offrire e molti strumenti rapidi per la presentazione"

Rating: 5.0/5.0 stars

— Paul H.

Best Data Science and Machine Learning Platforms - Page 6

How Many Data Science and Machine Learning Platforms Products Does G2 Track?

Category Stats (Sep 2026)

How Does G2 Rank Data Science and Machine Learning Platforms Products?

G2 Grid® for Data Science and Machine Learning Platforms

How Do G2 Users Rate Pyramid?

Who Is the Company Behind Pyramid?

Who Uses This Product?

What Are Recent G2 Reviews of Pyramid?

How Do G2 Users Rate SF AI Labs?

Who Is the Company Behind SF AI Labs?

Who Uses This Product?

What Are Recent G2 Reviews of SF AI Labs?

How Do G2 Users Rate Braze AI Decisioning Studio™?

Who Is the Company Behind Braze AI Decisioning Studio™?

Who Uses This Product?

What Do G2 Reviewers Say About Braze AI Decisioning Studio™?

Pros

Cons

What Are Recent G2 Reviews of Braze AI Decisioning Studio™?

How Do G2 Users Rate Datagran?

Who Is the Company Behind Datagran?

Who Uses This Product?

What Are Recent G2 Reviews of Datagran?

What Are G2 Users Discussing About Datagran?

How Do G2 Users Rate Edge Impulse?

Who Is the Company Behind Edge Impulse?

Who Uses This Product?

What Do G2 Reviewers Say About Edge Impulse?

Pros

Cons

What Are Recent G2 Reviews of Edge Impulse?

How Do G2 Users Rate H2O Driverless AI?

Who Is the Company Behind H2O Driverless AI?

Who Uses This Product?

What Do G2 Reviewers Say About H2O Driverless AI?

Pros

Cons

What Are Recent G2 Reviews of H2O Driverless AI?

What Are G2 Users Discussing About H2O Driverless AI?

How Do G2 Users Rate JADBio AutoML?

Who Is the Company Behind JADBio AutoML?

Who Uses This Product?

What Are Recent G2 Reviews of JADBio AutoML?

What Are G2 Users Discussing About JADBio AutoML?

How Do G2 Users Rate Labelbox?

Who Is the Company Behind Labelbox?

Who Uses This Product?

What Do G2 Reviewers Say About Labelbox?

Pros

Cons

What Are Recent G2 Reviews of Labelbox?

What Are G2 Users Discussing About Labelbox?

How Do G2 Users Rate Lightning AI?

Who Is the Company Behind Lightning AI?

Who Uses This Product?

What Do G2 Reviewers Say About Lightning AI?

Pros

Cons

What Are Recent G2 Reviews of Lightning AI?

How Do G2 Users Rate Machine Learning Platform for AI?

Who Is the Company Behind Machine Learning Platform for AI?

Who Uses This Product?

What Do G2 Reviewers Say About Machine Learning Platform for AI?

Pros

Cons

What Are Recent G2 Reviews of Machine Learning Platform for AI?

What Are G2 Users Discussing About Machine Learning Platform for AI?

How Do G2 Users Rate Myriade?

Who Is the Company Behind Myriade?

Who Uses This Product?

What Are Recent G2 Reviews of Myriade?

Who Is the Company Behind Polyture?

Who Uses This Product?

What Are Recent G2 Reviews of Polyture?

What Are G2 Users Discussing About Polyture?

How Do G2 Users Rate Rainbird?

Who Is the Company Behind Rainbird?

Who Uses This Product?

What Are Recent G2 Reviews of Rainbird?