Data Science and Machine Learning Platforms Resources
Articles, Glossary Terms, Discussions, and Reports to expand your knowledge on Data Science and Machine Learning Platforms
Resource pages are designed to give you a cross-section of information we have on specific categories. You'll find articles from our experts, feature definitions, discussions from users like you, and reports from industry data.
Data Science and Machine Learning Platforms Articles
Seq2Seq Models: How They Work and Why They Matter in AI
Imagine effortlessly translating an entire book from one language to another or condensing pages of dense text into a few clear sentences – all with just a few clicks.
by Chayanika Sen
10 Best Data Labeling Software With G2 User Reviews
As the prominence of AI grows, it is being commercialized at a lightning-fast speed.
by Shreya Mattoo
What Is Artificial Intelligence (AI)? Types, Definition And Examples
Remember Sophia, the humanoid that appeared on the late-night show with Jimmy Fallon?
by Amal Joby
What Is Artificial General Intelligence (AGI)? The Future Is Here
Artificial general intelligence (AGI) could be the best or worst thing ever happening to us.
by Amal Joby
2023 Trends in AI: Cheaper, Easier-to-Use AI to the Rescue
This post is part of G2's 2023 digital trends series. Read more about G2’s perspective on digital transformation trends in an introduction from Emily Malis Greathouse, director, market research, and additional coverage on trends identified by G2’s analysts.
by Matthew Miller
Barriers Toward Adopting AI and Analytics in the Supply Chain
I recently attended the Tableau Conference, where I indulged my nerdiness for four days. As a self-described data science evangelist, I was thrilled to see autoML, natural language generation, and other advanced automation features be added to Tableau, one of the world’s leading data visualization and business intelligence platforms.
by Anthony Orso
The Importance of Data Quality and Commoditization of Algorithms
Algorithms. Algorithmic. Machine learning. Deep learning. If you’re reading this piece, there is a good chance you have come across these terms at some point. An algorithm probably recommended this article to you. The umbrella term for all of the above is artificial intelligence (AI), which takes data of different flavors and provides you with predictions or answers based on that. There is a good chance you have benefited from this technology in some way, whether in a map application, image search from your favorite retailer, or intelligent autocomplete.
by Matthew Miller
How to Choose a Data Science and Machine Learning Platform That’s Right For Your Business
Big data is the zeitgeist of the 21st century. The sheer volume of data available to businesses, government agencies, educational institutions, and consumers is virtually limitless compared to the days when computers were the size of computer science labs.
by Anthony Orso
Data Trends in 2022
This post is part of G2's 2022 digital trends series. Read more about G2’s perspective on digital transformation trends in an introduction from Tom Pringle, VP, market research, and additional coverage on trends identified by G2’s analysts.
by Matthew Miller
How to Make Algorithms Which Explain Themselves
Back in 2019, I wrote my predictions of advancements we'd see in AI in 2020. In one of those predictions, I discussed the perennial problem of algorithmic explainability, or the ability for algorithms to explain themselves, and how that will come to the fore this year. Solving this problem is key to business success, as the general public is becoming increasingly uncomfortable with black-box algorithms.
by Matthew Miller
Artificial Intelligence in Healthcare: Benefits, Myths, and Limitations
Artificial intelligence (AI) is reinventing and reinvigorating the modern healthcare system by finding new links between genetic codes or driving robots that assist with surgery.
by Rachael Altman
The Role of Artificial Intelligence in Accounting
Accounting is one of the most important, yet daunting and expensive departments in almost all companies.
Accountants oversee all financial operations of a business to help it run smoothly and efficiently. These include preparing and analyzing financial statements (e.g., cash flow, income statement, balance sheet), paying taxes on time, and maintaining the companies’ general ledger (GL). All these tasks require a great deal of human interaction that takes time and money; no matter how careful an employee may be, there is always the chance for human error, which could snowball and lead to devastating financial results in the future.
by Nathan Calabrese
Tech Companies Bridging the Gap Between AI and Automation
Automation and artificial intelligence (AI) are important, interrelated tools that help organizations streamline their processes and add intelligence to their workflows.
They allow businesses to reach organizational goals by automating business processes, whereby they can increase efficiency and adapt to new business procedures.
by Matthew Miller
How COVID-19 Is Impacting Data Professionals
Remote work isn't the future. It's a current reality, with nearly 75% of U.S. workers working remotely at least some of the time, according to Owl Labs' State of Remote Work 2019 Report. Data scientists and other data professionals are no exception to the rule and are able to bring their work home with them if and when the need, or desire, arises. However, a switch to remote work isn't as straightforward as simply taking a work laptop home.
by Matthew Miller
True Data Protection Demands More Than Just Regulation
I’ll let you in on a (poorly kept) secret: The use of advanced analytics and other AI-powered capabilities that help users manage and interrogate data isn't new. The practice has been around far longer than the current bubble of hype surrounding AI has been inflating.
by Tom Pringle
What Is the Future of Machine Learning? We Asked 5 Experts
Forget what you may have heard. Machine learning isn’t some new concept or study in its infancy.
by Devin Pickell
Data Science and Machine Learning Platforms Glossary Terms
Data Science and Machine Learning Platforms Discussions
0
Question on: IBM SPSS Modeler
What is IBM SPSS Modeler used for?
What is IBM SPSS Modeler used for?
Data Exploration and Preparation:
SPSS Modeler allows users to explore and understand their data through visualization and summary statistics.
It provides tools for data cleaning, handling missing values, and transforming variables.
Predictive Modeling:
Users can build predictive models to identify patterns, trends, and relationships in the data.
Various algorithms are available, including decision trees, regression analysis, neural networks, and support vector machines.
Classification and Regression:
SPSS Modeler is used for building models that classify cases into predefined categories (classification) or predict numerical outcomes (regression).
Clustering:
Clustering algorithms help identify natural groupings or clusters within the data.
Association Rules:
SPSS Modeler can identify associations and relationships between variables in the data.
Text Analytics:
It supports the analysis of unstructured text data, extracting valuable insights from textual information.
Time Series Analysis:
Time series algorithms are available for analyzing data with a temporal component, such as financial or stock market data.
Model Evaluation and Deployment:
Users can assess the performance of their models using various evaluation metrics.
Once a model is deemed satisfactory, it can be deployed for making predictions on new, unseen data.
Automated Machine Learning (AutoML):
SPSS Modeler includes automated machine learning capabilities to simplify the model-building process for users with varying levels of expertise.
Integration with Other IBM Products:
It can integrate with other IBM products, such as IBM Watson Studio, for a more comprehensive data science and analytics environment.
Visualization and Reporting:
SPSS Modeler provides tools for visualizing model outputs and creating reports to communicate findings.
Geospatial Analytics:
Geospatial analysis features allow users to incorporate location-based information into their models.
0
Question on: H2O Driverless AI
Is H2O driverless AI free?
Is H2O driverless AI free?
Driverless AI is commercial software so it requires a license or purchase to use it. There is a free trial offered at https://h2o.ai/freetrial/
0
Question on: Domino Enterprise AI Platform
What is Domino Python?
What is Domino Python?
Python is one of the languages supported natively in Domino, along with R, MATLAB, and others. Full details about Python support in Domino can be found at https://docs.dominodatalab.com/en/latest/user_guide/9a69d9/get-started-with-python/.
Data Science and Machine Learning Platforms Reports
Mid-Market Grid® Report for Data Science and Machine Learning Platforms
Fall 2026
G2 Report: Grid® Report
Grid® Report for Data Science and Machine Learning Platforms
Fall 2026
G2 Report: Grid® Report
Enterprise Grid® Report for Data Science and Machine Learning Platforms
Fall 2026
G2 Report: Grid® Report
Momentum Grid® Report for Data Science and Machine Learning Platforms
Fall 2026
G2 Report: Momentum Grid® Report
Small-Business Grid® Report for Data Science and Machine Learning Platforms
Fall 2026
G2 Report: Grid® Report
Enterprise Grid® Report for Data Science and Machine Learning Platforms
Summer 2026
G2 Report: Grid® Report
Small-Business Grid® Report for Data Science and Machine Learning Platforms
Summer 2026
G2 Report: Grid® Report
Mid-Market Grid® Report for Data Science and Machine Learning Platforms
Summer 2026
G2 Report: Grid® Report
Grid® Report for Data Science and Machine Learning Platforms
Summer 2026
G2 Report: Grid® Report
Momentum Grid® Report for Data Science and Machine Learning Platforms
Summer 2026
G2 Report: Momentum Grid® Report



















