Best Data Science and Machine Learning Platforms - Page 2

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

Total Products under this Category: 1,647

Category Stats (Oct 2026)

  • Average Rating: 4.46/5 The average rating of products in this category, based on all submitted ratings
  • Top Trending Product: Palantir Foundry (+1.15%) - Among all products in this category, Palantir Foundry recorded the largest rating increase compared to last month

Last updated: October 13, 2026

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

Why You Can Trust G2's Software Rankings:

  • 30 Analysts and Data Experts
  • 14,600+ Authentic Reviews
  • 1,647+ Products
  • Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.

G2 Grid® for Data Science and Machine Learning Platforms

G2 Grid® for Data Science and Machine Learning Platforms plotting products by satisfaction and market presence

Highlighted products: Databricks, Gemini Enterprise Agent Platform, SAS Viya, Google Cloud AutoML, IBM watsonx.data, Snowflake, MATLAB, and Dataiku.

Underlying data: [Grid® JSON](https://www.g2.com/categories/data-science-and-machine-learning-platforms/grids.json?focus%5B%5D=databricks&focus%5B%5D=gemini-enterprise-agent-platform&focus%5B%5D=sas-sas-viya&focus%5B%5D=google-cloud-automl&focus%5B%5D=ibm-watsonx-data&focus%5B%5D=snowflake&focus%5B%5D=matlab&focus%5B%5D=dataiku)

Deep Learning Containers

Google's Deep Learning Containers are pre-configured Docker images designed to streamline the development and deployment of deep learning models. These containers come equipped with popular machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn, along with their dependencies, enabling data scientists and developers to focus on model development without the hassle of environment setup. Key Features and Functionality: - Pre-configured Environments: Each container includes essential deep learning frameworks and libraries, ensuring compatibility and reducing setup time. - Scalability: Seamless integration with Google Cloud services allows for efficient scaling of training and inference tasks. - Flexibility: Support for various hardware accelerators, including GPUs and TPUs, enhances performance for computationally intensive tasks. - Portability: Consistent environments across development, testing, and production stages facilitate smoother transitions and deployments. Primary Value and Problem Solved: Deep Learning Containers address the complexities associated with setting up and managing deep learning environments. By providing ready-to-use, optimized containers, they eliminate the need for manual installation and configuration of machine learning frameworks and dependencies. This accelerates the development process, ensures consistency across different stages of model deployment, and allows teams to allocate more resources toward innovation and model refinement rather than infrastructure management.

Average Rating: 4.5/5.0

Total Reviews: 12

How Do G2 Users Rate Deep Learning Containers?

  • Application: 9.2/10 (Category avg: 8.5/10)
  • Managed Service: 9.2/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 9.4/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.6/10 (Category avg: 8.6/10)

Who Is the Company Behind Deep Learning Containers?

  • Seller: Google
  • Year Founded: 1998
  • HQ Location: Mountain View, CA
  • Twitter: @google
    31,899,995 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    301,144 employees on LinkedIn®
  • Ownership: NASDAQ:GOOG

Who Uses This Product?

  • Top Industries: Information Technology and Services
  • Company Size: 42% Small, 33% Large

What Do G2 Reviewers Say About Deep Learning Containers?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the easy integrations with frameworks like PyTorch, TensorFlow, and Google Cloud Services for seamless workflows.
  • Users praise the seamless integration of Deep Learning Containers with PyTorch, TensorFlow, and Google Cloud Services.
Cons
  • Users find the complexity of Deep Learning Containers overwhelming, making initial use challenging and confusing.

What Are Recent G2 Reviews of Deep Learning Containers?

Deep Learning VM Image

Deep Learning VM Images are pre-configured virtual machine images optimized for data science and machine learning tasks. These images come with essential machine learning frameworks and tools pre-installed, enabling users to deploy and scale machine learning models efficiently on Google Cloud's infrastructure. Key Features and Functionality: - Pre-installed Frameworks: Support for TensorFlow Enterprise, TensorFlow, PyTorch, and generic high-performance computing, catering to various machine learning needs. - Operating System Options: Based on Debian 11 and Ubuntu 22.04, providing flexibility and compatibility with different environments. - Comprehensive Python Environment: Includes Python 3.10 with a suite of libraries such as NumPy, SciPy, Matplotlib, Pandas, NLTK, Pillow, scikit-image, OpenCV, and scikit-learn, facilitating a robust development experience. - JupyterLab Integration: Offers JupyterLab notebook environments for rapid prototyping and interactive development. - GPU Acceleration: Equipped with the latest NVIDIA drivers and packages, including CUDA 11.x and 12.x, CuDNN, and NCCL, to leverage GPU capabilities for accelerated computation. Primary Value and User Solutions: Deep Learning VM Images streamline the setup process for machine learning projects by providing ready-to-use environments with pre-installed frameworks and tools. This reduces the time and effort required for configuration, allowing data scientists and machine learning practitioners to focus on model development and experimentation. The integration with Google Cloud's scalable infrastructure ensures that users can efficiently manage and scale their machine learning workloads, whether they require CPU or GPU resources. Regular updates and community support further enhance the reliability and performance of these VM images, making them a valuable resource for accelerating machine learning initiatives.

Average Rating: 4.3/5.0

Total Reviews: 67

How Do G2 Users Rate Deep Learning VM Image?

  • Application: 8.8/10 (Category avg: 8.5/10)
  • Managed Service: 8.4/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 8.6/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.8/10 (Category avg: 8.6/10)

Who Is the Company Behind Deep Learning VM Image?

  • Seller: Google
  • Year Founded: 1998
  • HQ Location: Mountain View, CA
  • Twitter: @google
    31,899,995 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    301,144 employees on LinkedIn®
  • Ownership: NASDAQ:GOOG

Who Uses This Product?

  • Who Uses This: Software Engineer
  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 46% Small, 37% Medium

What Do G2 Reviewers Say About Deep Learning VM Image?

AI-generated summary from verified user reviews

Pros
  • Users find the ease of use of Deep Learning VM Image beneficial, enabling focus on development without manual setup.
  • Users appreciate the setup ease of Deep Learning VM Image, enabling quick start on machine learning projects without hassle.
  • Users appreciate the pre-configured setup and GPU optimization of Deep Learning VM Image, enhancing productivity and efficiency.
  • Users appreciate the easy integrations of Deep Learning VM Image, enhancing their workflow and optimizing machine learning models.
  • Users find the easy setup of Deep Learning VM Image eliminates headaches and allows immediate work on models.
Cons
  • Users note the high cost of Deep Learning VM Image compared to general-purpose options, impacting budget considerations.
  • Users highlight the high costs associated with Deep Learning VM Image, particularly for GPU/TPU usage and continuous operations.
  • Users report a steep learning curve for Google Deep Learning VM, making it challenging for newcomers to adapt.
  • Users find the difficult learning curve challenging, especially for beginners navigating the complex features of Deep Learning VM Image.
  • Users find the dependency on cloud providers limits flexibility and complicates multi-cloud strategies with Deep Learning VM Image.

What Are Recent G2 Reviews of Deep Learning VM Image?

Google Cloud AI Hub

Google Cloud’s Artificial Intelligence (AI) Hub is a catalog of plug-and-play AI components, including end-to-end AI pipelines and out-of-the-box algorithms.

Average Rating: 4.4/5.0

Total Reviews: 41

How Do G2 Users Rate Google Cloud AI Hub?

  • Application: 8.5/10 (Category avg: 8.5/10)
  • Managed Service: 8.3/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 8.3/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.0/10 (Category avg: 8.6/10)

Who Is the Company Behind Google Cloud AI Hub?

  • Seller: Google
  • Year Founded: 1998
  • HQ Location: Mountain View, CA
  • Twitter: @google
    31,899,995 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    301,144 employees on LinkedIn®
  • Ownership: NASDAQ:GOOG

Who Uses This Product?

  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 43% Small, 30% Large

What Are Recent G2 Reviews of Google Cloud AI Hub?

What Are G2 Users Discussing About Google Cloud AI Hub?

AI can help you find the answers. G2 helps you trust them.

Connect G2 to Claude or ChatGPT for answers grounded in G2's trusted reviews, comparisons, and pricing from real user insights.

How it works
Connect G2 to ChatGPTConnect G2 to Claude

KNIME

KNIME helps everybody make sense of data. Its free and open source KNIME Analytics Platform enables anyone — whether they come from a business, technical or data background — to intuitively work with data, every day. KNIME Business Hub is the commercial complement to KNIME Analytics Platform and enables users to collaborate on data science and share insights across the organization. Together, the products support the complete data science lifecycle, allowing teams at all levels of analytics readiness to support the operationalization of data and to build a scalable data science practice.

Average Rating: 4.5/5.0

Total Reviews: 125

How Do G2 Users Rate KNIME?

  • Application: 8.5/10 (Category avg: 8.5/10)
  • Managed Service: 7.6/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 8.1/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.2/10 (Category avg: 8.6/10)

Who Is the Company Behind KNIME?

  • Seller: KNIME
  • Company Website:
  • Year Founded: 2008
  • HQ Location: Zurich, Switzerland
  • Twitter: @knime
    7,998 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    245 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Information Technology and Services, Higher Education
  • Company Size: 43% Large, 31% Medium

What Do G2 Reviewers Say About KNIME?

AI-generated summary from verified user reviews

Pros
  • Users find KNIME's ease of use enhances productivity, enabling even non-technical individuals to build workflows effortlessly.
  • Users find KNIME's coding ease remarkable, enabling effortless workflows without requiring extensive technical skills.
  • Users find KNIME to be an easy-to-learn platform, enabling quick workflow creation without coding skills.
  • Users find KNIME easy to learn and start delivering results, thanks to its intuitive visual workflow interface.
  • Users love the easy and intuitive data visualization capabilities of KNIME, enhancing understanding and communication of data insights.
Cons
  • Users find the initial learning curve challenging, especially for those unfamiliar with data science concepts and visual programming.
  • Users face memory usage issues with KNIME, leading to slow performance, especially with larger files and operations.
  • Users report storage limitations with KNIME, leading to performance issues and difficulties with large datasets.
  • Users note that data management issues hinder their experience, especially with file handling and certain databases.
  • Users find the lack of learning resources for KNIME to be a significant barrier to effective usage.

What Are Recent G2 Reviews of KNIME?

What Are G2 Users Discussing About KNIME?

Rapidminer AI Studio

Rapidminer AI Studio is a data science tool that anyone can use to design and prototype highly explainable AI and machine learning models that help build trust throughout an organization. Rapidminer AI Studio includes: - Full generative AI functionality with access to hundreds of large language models (LLMs). - Intuitive and powerful drag-and-drop canvases that give users code-like control without complexity. - Award-winning auto ML with automated clustering, predictive modeling, feature engineering, and time series forecasting. - Data connectivity, exploration, and preparation. - Deploy and manage AI projects and models at enterprise scale. - Collaborate with team members in the same environment without having to worry about overwriting each other's work. - Unify the entire data science lifecycle from data exploration and machine learning to model operations and visualization and deploy in the cloud. Rapidminer AI Studio helps users make powerful insights accessible to the entire organization and can scale seamlessly for users and enterprises. Rapidminer AI studio enables organizations to derive significant value from AI with minimal cost and operational impact.

Average Rating: 4.6/5.0

Total Reviews: 512

How Do G2 Users Rate Rapidminer AI Studio?

  • Application: 8.3/10 (Category avg: 8.5/10)
  • Managed Service: 8.2/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 7.6/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.5/10 (Category avg: 8.6/10)

Who Is the Company Behind Rapidminer AI Studio?

Who Uses This Product?

  • Who Uses This: Student, Professor
  • Top Industries: Higher Education, Education Management
  • Company Size: 41% Small, 31% Large

What Do G2 Reviewers Say About Rapidminer AI Studio?

AI-generated summary from verified user reviews

Pros
  • Users praise the ease of use of Altair AI Studio, finding its interface intuitive and beneficial for data tasks.
  • Users value the no-code machine learning capabilities of Altair AI Studio, facilitating easy model creation and analysis.
  • Users value the seamless AI integration of Altair AI Studio, enhancing decision-making and improving efficiency across organizations.
  • Users appreciate the advanced machine learning and data analytics in Altair AI Studio for smarter decision-making and efficiency.
  • Users value the automation capabilities of Altair AI Studio, enhancing efficiency in data processing and decision making.
Cons
  • Users find the complexity of Altair AI Studio challenging, particularly with language support and integrating legacy systems.
  • Users experience slower performance when handling large datasets in Altair AI Studio, affecting their efficiency and experience.
  • Users experience slow performance when handling large datasets, leading to slowdowns or freezing during usage.
  • Users find the complexity issues of Altair AI Studio frustrating, especially due to limited support resources.
  • Users find the complex usage of Altair AI Studio challenging, especially due to limited documentation and steep learning curve.

What Are Recent G2 Reviews of Rapidminer AI Studio?

What Are G2 Users Discussing About Rapidminer AI Studio?

Amazon SageMaker

Amazon SageMaker is a fully managed service that enables data scientists and developers to build, train, and deploy machine learning (ML) models at scale. It provides a comprehensive suite of tools and infrastructure, streamlining the entire ML workflow from data preparation to model deployment. With SageMaker, users can quickly connect to training data, select and optimize algorithms, and deploy models in a secure and scalable environment. Key Features and Functionality: - Integrated Development Environments (IDEs): SageMaker offers a unified, web-based interface with built-in IDEs, including JupyterLab and RStudio, facilitating seamless development and collaboration. - Pre-built Algorithms and Frameworks: It includes a selection of optimized ML algorithms and supports popular frameworks like TensorFlow, PyTorch, and Apache MXNet, allowing flexibility in model development. - Automated Model Tuning: SageMaker can automatically tune models to achieve optimal accuracy, reducing the time and effort required for manual adjustments. - Scalable Training and Deployment: The service manages the underlying infrastructure, enabling efficient training of models on large datasets and deploying them across auto-scaling clusters for high availability. - MLOps and Governance: SageMaker provides tools for monitoring, debugging, and managing ML models, ensuring robust operations and compliance with enterprise security standards. Primary Value and Problem Solved: Amazon SageMaker addresses the complexity and resource-intensive nature of developing and deploying ML models. By offering a fully managed environment with integrated tools and scalable infrastructure, it accelerates the ML lifecycle, reduces operational overhead, and enables organizations to derive insights and value from their data more efficiently. This empowers businesses to innovate rapidly and implement AI solutions without the need for extensive in-house expertise or infrastructure management.

Average Rating: 4.3/5.0

Total Reviews: 54

How Do G2 Users Rate Amazon SageMaker?

  • Application: 8.6/10 (Category avg: 8.5/10)
  • Managed Service: 9.1/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 9.3/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.4/10 (Category avg: 8.6/10)

Who Is the Company Behind Amazon SageMaker?

  • Seller: Amazon Web Services (AWS)
  • Year Founded: 2006
  • HQ Location: Seattle, WA
  • Twitter: @awscloud
    2,232,483 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    148,266 employees on LinkedIn®
  • Ownership: NASDAQ: AMZN

Who Uses This Product?

  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 33% Large, 33% Medium

What Do G2 Reviewers Say About Amazon SageMaker?

AI-generated summary from verified user reviews

Pros
  • Users find the ease of use of Amazon SageMaker exceptional, allowing quick adaptation and straightforward model training.
  • Users value the seamless AI integration of Amazon SageMaker, streamlining the entire machine learning lifecycle efficiently.
  • Users appreciate the superior computing power of Amazon SageMaker, significantly reducing model training time and enhancing productivity.
  • Users praise Amazon SageMaker for its efficient training process, drastically reducing model training time and simplifying functionality.
  • Users highlight the fast processing of Amazon SageMaker, significantly reducing model training time and enhancing productivity.
Cons
  • Users find that Amazon SageMaker can become expensive, particularly with long-running jobs and complex pricing structures.
  • Users find the complex pricing structure of Amazon SageMaker can lead to unexpected costs and confusion.
  • Users find the complexity of pricing in SageMaker challenging, often leading to unexpected costs and confusion.
  • Users find the steep learning curve for Amazon SageMaker challenging, particularly for those new to AWS services.
  • Users find a difficult learning curve during the initial setup of Amazon SageMaker, impacting usability.

What Are Recent G2 Reviews of Amazon SageMaker?

What Are G2 Users Discussing About Amazon SageMaker?

TensorFlow

TensorFlow is an open-source machine learning library developed by the Google Brain Team, designed to facilitate the creation, training, and deployment of machine learning models across various platforms. It provides a comprehensive ecosystem that supports tasks ranging from simple data flow graphs to complex neural networks, enabling developers and researchers to build and deploy machine learning applications efficiently. Key Features and Functionality: - Flexible Architecture: TensorFlow's architecture allows for deployment across multiple platforms, including CPUs, GPUs, and TPUs, and supports various operating systems such as Linux, macOS, Windows, Android, and JavaScript. - Multiple Language Support: While primarily offering a Python API, TensorFlow also provides support for other languages, including C++, Java, and JavaScript, catering to a diverse developer community. - High-Level APIs: TensorFlow includes high-level APIs like Keras, which simplify the process of building and training models, making machine learning more accessible to beginners and efficient for experts. - Eager Execution: This feature allows for immediate evaluation of operations, facilitating intuitive debugging and dynamic graph building. - Distributed Computing: TensorFlow supports distributed training, enabling the scaling of machine learning models across multiple devices and servers without significant code modifications. Primary Value and Solutions Provided: TensorFlow addresses the challenges of developing and deploying machine learning models by offering a unified, scalable, and flexible platform. It streamlines the workflow from model conception to deployment, reducing the complexity associated with machine learning projects. By supporting a wide range of platforms and languages, TensorFlow empowers users to implement machine learning solutions in diverse environments, from research labs to production systems. Its comprehensive suite of tools and libraries accelerates the development process, fosters innovation, and enables the creation of sophisticated models that can tackle real-world problems effectively.

Average Rating: 4.5/5.0

Total Reviews: 136

How Do G2 Users Rate TensorFlow?

  • Application: 8.7/10 (Category avg: 8.5/10)
  • Managed Service: 8.5/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 8.6/10 (Category avg: 8.3/10)
  • Ease of Admin: 7.9/10 (Category avg: 8.6/10)

Who Is the Company Behind TensorFlow?

  • Seller: TensorFlow
  • Year Founded: 2016
  • HQ Location: Centre Urbain Nord, TN
  • Twitter: @TensorFlow
    377,398 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    1 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Software Engineer, Senior Software Engineer
  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 51% Small, 26% Medium

What Do G2 Reviewers Say About TensorFlow?

AI-generated summary from verified user reviews

Pros
  • Users celebrate the flexibility and power of TensorFlow, enabling complex machine learning projects with ease.
  • Users praise the end-to-end AI integration of TensorFlow, which enhances project efficiency and flexibility across platforms.
  • Users appreciate the ease of use of TensorFlow, benefiting from strong support and comprehensive guides for model training.
  • Users appreciate the model variety in TensorFlow, enabling efficient and versatile machine learning across different platforms.
  • Users appreciate the scalability of TensorFlow, enabling efficient distributed training across various hardware platforms.
Cons
  • Users find the steep learning curve of TensorFlow difficult, requiring significant time and effort to master.
  • Users find TensorFlow complex and hard to learn, especially when debugging or converting models for embedded applications.
  • Users find the difficult learning curve of TensorFlow frustrating, especially when dealing with high-level Keras and deprecated APIs.
  • Users find error handling frustrating due to complex messages and difficult debugging processes, especially for beginners.
  • Users experience slow performance with TensorFlow, especially when executing complicated models and training larger frameworks.

What Are Recent G2 Reviews of TensorFlow?

What Are G2 Users Discussing About TensorFlow?

AWS Trainium

Get high performance for deep learning and generative AI training while lowering costs

Average Rating: 4.5/5.0

Total Reviews: 16

How Do G2 Users Rate AWS Trainium?

  • Application: 8.8/10 (Category avg: 8.5/10)
  • Managed Service: 8.6/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 8.9/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.9/10 (Category avg: 8.6/10)

Who Is the Company Behind AWS Trainium?

  • Seller: Amazon Web Services (AWS)
  • Year Founded: 2006
  • HQ Location: Seattle, WA
  • Twitter: @awscloud
    2,232,483 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    148,266 employees on LinkedIn®
  • Ownership: NASDAQ: AMZN

Who Uses This Product?

  • Company Size: 53% Small, 29% Large

What Are Recent G2 Reviews of AWS Trainium?

Loxo

Loxo is the #1 Talent Intelligence Platform and global leader in recruiting software. The old-school, legacy ATS will not exist in 5 years. Loxo is the way of the future, managing the full recruitment life cycle through a single system-of-record software platform. It replaces your clunky legacy ATS and brings infinitely more to the table in the process. Because it shouldn't take 10+ tools to make one hire. Loxo's Talent Intelligence Platform includes: - A best-in-class ATS. - An AI-driven Recruiting CRM: Your recruitment database, which contains a combination of your data and Loxo's automatically-updating data to ensure you've always got the most accurate candidate information. - A multi-channel outreach tool with AI-powered campaign-building capabilities, so you never miss a follow-up. - A people and company search engine filled with over 1.2 billion people & millions of organizations. - Verified contact information like personal emails and cell phone numbers. - Instant AI sourcing, ranking, and matching thanks to Loxo's AI assistant, Copilot. Not to mention... Loxo is also the only recruitment software to feature a Sales CRM specifically designed for recruiters. You can conduct business development activities in the same place you do the rest of your workflow. The real win here? Each of these market-leading products are designed to work seamlessly together, making every step of your job more efficient and reducing room for error. More than 13,200 executive search, RPO, professional recruitment, and staffing teams across the globe have become hiring machines with Loxo. But what does that mean, exactly? - A 74% decrease in cost across the entire talent lifecycle, including costs to source and hire. - An 85% reduction in time-to-hire thanks to sourcing and workflow automation. - 98% customer satisfaction. But don't take our word for it. Check out our reviews to see how much our customers love working with Loxo.

Average Rating: 4.6/5.0

Total Reviews: 178

How Do G2 Users Rate Loxo?

  • Application: 6.7/10 (Category avg: 8.5/10)
  • Managed Service: 6.7/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 6.7/10 (Category avg: 8.3/10)
  • Ease of Admin: 9.0/10 (Category avg: 8.6/10)

Who Is the Company Behind Loxo?

  • Seller: Loxo
  • Year Founded: 2012
  • HQ Location: Austin, Texas
  • Twitter: @loxo
    71 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    159 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Managing Director, Founder
  • Top Industries: Staffing and Recruiting, Information Technology and Services
  • Company Size: 95% Small, 5% Medium

What Do G2 Reviewers Say About Loxo?

AI-generated summary from verified user reviews

Pros
  • Users love the intuitive and user-friendly interface of Loxo, making it easy and efficient to navigate.
  • Users praise the responsive support and user-friendly interface of Loxo, enhancing their overall experience significantly.
  • Users value the time-saving features of Loxo, making recruitment processes significantly more efficient and manageable.
  • Users value the responsive support and continuous updates of Loxo, enhancing usability and feature integration.
  • Users love Loxo's intuitive design, praising its seamless usability and superior integration of AI features.
Cons
  • Users find limited customization in Loxo, making it difficult to tailor the system to their specific needs.
  • Users find candidate management changes confusing and frustrating, leading to inefficiencies and a diminished overall experience.
  • Users often face email functionality issues with Loxo, leading to poor candidate communication and unreliable contact information.
  • Users express the need for improvements in functionality, communication, and usability across various features in Loxo.
  • Users express frustration over missing and unusable features in Loxo, impacting their overall satisfaction and experience.

What Are Recent G2 Reviews of Loxo?

What Are G2 Users Discussing About Loxo?

Wipro Holmes

Wipro HOLMES is an Artificial Intelligence Platform that provide services for the development of digital virtual agents, predictive systems, cognitive process automation, visual computing applications, knowledge virtualization, robotics and drones to deliver cognitive enhancement to experience and productivity, accelerate process through automation and at the highest stage of maturity reach autonomous abilities

Average Rating: 3.8/5.0

Total Reviews: 10

How Do G2 Users Rate Wipro Holmes?

  • Application: 7.1/10 (Category avg: 8.5/10)
  • Managed Service: 7.9/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 7.6/10 (Category avg: 8.3/10)
  • Ease of Admin: 6.1/10 (Category avg: 8.6/10)

Who Is the Company Behind Wipro Holmes?

  • Seller: Wipro
  • Year Founded: 1945
  • HQ Location: Bangalore
  • Twitter: @Wipro
    513,142 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    275,739 employees on LinkedIn®
  • Ownership: WIT

Who Uses This Product?

  • Company Size: 40% Large, 30% Medium

What Do G2 Reviewers Say About Wipro Holmes?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the seamless AI integration of Wipro Holmes, significantly enhancing efficiency and streamlining complex processes.
  • Users appreciate Wipro Holmes for its remarkable automation capabilities, enhancing efficiency and streamlining complex business processes significantly.
  • Users commend Wipro Holmes for its remarkable efficiency, enhancing automation and decision-making with seamless integration capabilities.
  • Users appreciate the analysis efficiency of Wipro HOLMES, which streamlines tasks and enhances decision-making capabilities.
  • Users value Wipro Holmes for its remarkable efficiency in automation and intelligent integration across systems, improving productivity.
Cons
  • Users suggest an improvement in the complex interface to enhance usability for non-technical team members.
  • Users desire more customization options in Wipro Holmes to enhance their experience and better meet their needs.
  • Users find the steep learning curve of Wipro Holmes challenging, often needing technical assistance to navigate advanced features.

What Are Recent G2 Reviews of Wipro Holmes?

What Are G2 Users Discussing About Wipro Holmes?

DataRobot

DataRobot’s enterprise AI platform democratizes data science with end-to-end automation for building, deploying, and managing machine learning models. This platform maximizes business value by delivering AI at scale and continuously optimizing performance over time. The company’s proven combination of cutting edge software and world-class AI implementation, training, and support services, empowers any organization – regardless of size, industry, or resources – to drive better business outcomes with AI.

Average Rating: 4.4/5.0

Total Reviews: 51

How Do G2 Users Rate DataRobot?

  • Application: 5.0/10 (Category avg: 8.5/10)
  • Managed Service: 1.7/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 6.1/10 (Category avg: 8.3/10)
  • Ease of Admin: 7.7/10 (Category avg: 8.6/10)

Who Is the Company Behind DataRobot?

  • Seller: DataRobot
  • Year Founded: 2012
  • HQ Location: Boston, Massachusetts
  • Twitter: @DataRobot
    19,225 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    886 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 49% Small, 40% Medium

What Are Recent G2 Reviews of DataRobot?

Saturn Cloud

Saturn Cloud is the AI token factory platform for neoclouds, AI Factory operators, and enterprises. It turns GPU infrastructure into managed services for fine-tuning, model serving, and per-token billing, with standard inference endpoints, distributed training, and managed environments. Enterprise security and governance are built in, across public, private, and on-premises environments. Learn more at saturncloud.io.

Average Rating: 4.8/5.0

Total Reviews: 320

How Do G2 Users Rate Saturn Cloud?

  • Application: 9.1/10 (Category avg: 8.5/10)
  • Managed Service: 9.1/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 9.1/10 (Category avg: 8.3/10)
  • Ease of Admin: 9.2/10 (Category avg: 8.6/10)

Who Is the Company Behind Saturn Cloud?

  • Seller: Saturn Cloud
  • Year Founded: 2018
  • HQ Location: New York, US
  • Twitter: @saturn_cloud
    3,279 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    41 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Data Scientist, Student
  • Top Industries: Computer Software, Higher Education
  • Company Size: 82% Small, 12% Medium

What Do G2 Reviewers Say About Saturn Cloud?

AI-generated summary from verified user reviews

Pros
  • Users value the ease of use of Saturn Cloud, appreciating its intuitive setup and versatile notebook options.
  • Users appreciate the powerful GPU performance of Saturn Cloud, enabling faster simulations and efficient project development.
  • Users appreciate the powerful GPU resources of Saturn Cloud, enhancing their learning projects with ease and robustness.
  • Users enjoy the easy setup with Saturn Cloud, making it convenient to start working on projects quickly.
  • Users appreciate the easy integrations of Saturn Cloud, enabling seamless access to powerful resources for their projects.
Cons
  • Users find Saturn Cloud's pricing expensive compared to alternatives and suggest a more affordable plan for students.
  • Users feel the absence of custom image support and extensive LLM selection limits the platform's versatility and efficiency.
  • Users find the complexity issues of Saturn Cloud challenging, particularly with documentation and pricing confusion for beginners.
  • Users struggle with poor documentation that complicates the learning process and hinders effective use of Saturn Cloud.
  • Users find the difficult setup process challenging initially, but it improves with familiarity and updated documentation.

What Are Recent G2 Reviews of Saturn Cloud?

Azure Machine Learning

Azure Machine Learning is an enterprise-grade service that facilitates the end-to-end machine learning lifecycle, enabling data scientists and developers to build, train, and deploy models efficiently. Key Features and Functionality: - Data Preparation: Quickly iterate data preparation on Apache Spark clusters within Azure Machine Learning, interoperable with Microsoft Fabric. - Feature Store: Increase agility in shipping your models by making features discoverable and reusable across workspaces. - AI Infrastructure: Take advantage of purpose-built AI infrastructure uniquely designed to combine the latest GPUs and InfiniBand networking. - Automated Machine Learning: Rapidly create accurate machine learning models for tasks including classification, regression, vision, and natural language processing. - Responsible AI: Build responsible AI solutions with interpretability capabilities. Assess model fairness through disparity metrics and mitigate unfairness. - Model Catalog: Discover, fine-tune, and deploy foundation models from Microsoft, OpenAI, Hugging Face, Meta, Cohere, and more using the model catalog. - Prompt Flow: Design, construct, evaluate, and deploy language model workflows with prompt flow. - Managed Endpoints: Operationalize model deployment and scoring, log metrics, and perform safe model rollouts. Primary Value and Solutions Provided: Azure Machine Learning accelerates time to value by streamlining prompt engineering and machine learning model workflows, facilitating faster model development with powerful AI infrastructure. It streamlines operations by enabling reproducible end-to-end pipelines and automating workflows with continuous integration and continuous delivery (CI/CD). The platform ensures confidence in development through unified data and AI governance with built-in security and compliance, allowing compute to run anywhere for hybrid machine learning. Additionally, it promotes responsible AI by providing visibility into models, evaluating language model workflows, and mitigating fairness, biases, and harm with built-in safety systems.

Average Rating: 4.3/5.0

Total Reviews: 87

How Do G2 Users Rate Azure Machine Learning?

  • Application: 8.8/10 (Category avg: 8.5/10)
  • Managed Service: 8.9/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 8.7/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.3/10 (Category avg: 8.6/10)

Who Is the Company Behind Azure Machine Learning?

  • Seller: Microsoft
  • Year Founded: 1975
  • HQ Location: Redmond, Washington
  • Twitter: @microsoft
    13,091,739 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    232,750 employees on LinkedIn®
  • Ownership: MSFT

Who Uses This Product?

  • Who Uses This: Software Engineer
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 40% Large, 33% Small

What Do G2 Reviewers Say About Azure Machine Learning?

AI-generated summary from verified user reviews

Pros
  • Users find Azure Machine Learning's ease of use beneficial for implementing and managing machine learning projects effectively.
  • Users appreciate the scalability and integration of Azure Machine Learning, enhancing deployment and management of models seamlessly.
  • Users value the excellent customer support of Azure Machine Learning, appreciating the comprehensive documentation and community assistance.
  • Users appreciate the ease of use and robust data management features that help in organizing and analyzing data effectively.
  • Users value the efficient environment of Azure Machine Learning for launching and monitoring machine learning jobs seamlessly.
Cons
  • Users report a challenging learning curve with Azure Machine Learning, requiring time to master its tools and interface.
  • Users find Azure Machine Learning's difficult navigation frustrating, often struggling to locate options and understand workflows.
  • Users find the disordered user interface of Azure Machine Learning frustrating, complicating their navigation and task completion.
  • Users find the complex interface of Azure Machine Learning challenging, particularly due to non-intuitive navigation and missing features.
  • Users find the difficult learning aspect challenging, especially those new to Azure or machine learning concepts.

What Are Recent G2 Reviews of Azure Machine Learning?

What Are G2 Users Discussing About Azure Machine Learning?

Red Hat OpenShift Data Science

Red Hat® OpenShift® AI is a flexible, scalable artificial intelligence (AI) and machine learning (ML) platform that enables enterprises to create and deliver AI-enabled applications at scale across hybrid cloud environments. Built using open source technologies, OpenShift AI provides trusted, operationally consistent capabilities for teams to experiment, serve models, and deliver innovative apps.

Average Rating: 4.5/5.0

Total Reviews: 41

How Do G2 Users Rate Red Hat OpenShift Data Science?

  • Application: 8.7/10 (Category avg: 8.5/10)
  • Managed Service: 8.8/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 8.7/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.1/10 (Category avg: 8.6/10)

Who Is the Company Behind Red Hat OpenShift Data Science?

  • Seller: Red Hat
  • Year Founded: 1993
  • HQ Location: Raleigh, NC
  • Twitter: @RedHat
    300,769 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    19,487 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Market Research, Computer Software
  • Company Size: 40% Large, 38% Medium

What Are Recent G2 Reviews of Red Hat OpenShift Data Science?

What Are G2 Users Discussing About Red Hat OpenShift Data Science?

Palantir Foundry

Foundry is a transformative data platform built to help solve the modern enterprise’s most critical problems by creating a central operating system for an organization’s data, while securely integrating siloed data sources into a common analytics and operations picture. Palantir works with commercial companies and government organizations alike to close the operational loop, feeding real-time data into your data science models and updating source systems. With a breadth of industry-leading capabilities, Palantir can help enterprises traverse and operationalize data to enable and scale decision-making, alongside best-in-class security, data protection, and governance. Foundry was named by Forrester as a leader in the The Forrester Wave™: AI/ML Platforms, Q3 2022. Scoring the highest marks possible in product vision, performance, market approach, and applications criteria. As a Dresner-Award winning platform, Foundry is the overall leader in the BI and Analytics market and rated a perfect 5/5 by its customer base.

Average Rating: 4.2/5.0

Total Reviews: 31

How Do G2 Users Rate Palantir Foundry?

  • Application: 7.1/10 (Category avg: 8.5/10)
  • Managed Service: 7.5/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 9.0/10 (Category avg: 8.3/10)
  • Ease of Admin: 6.7/10 (Category avg: 8.6/10)

Who Is the Company Behind Palantir Foundry?

  • Seller: Palantir
  • HQ Location: Denver, US
  • Twitter: @PalantirTech
    425,595 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    6,022 employees on LinkedIn®
  • Ownership: PLTR (NYSE)

Who Uses This Product?

  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 52% Large, 26% Small

What Do G2 Reviewers Say About Palantir Foundry?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the integration of AI workflows in Palantir Foundry, enhancing their tech stack and data ecosystem.
  • Users value the AI workflow integration of Palantir Foundry, enhancing their tech stack and data ecosystem effectively.
  • Users appreciate the seamless AI integration in Palantir Foundry, enhancing their tech and data workflows significantly.
  • Users value the integration of AI workflows in Palantir Foundry, enhancing their tech stack and data ecosystem.
  • Users benefit from the efficient analysis capabilities of Palantir Foundry, streamlining their data processes and insights.
Cons
  • Users note that Palantir Foundry is not cheap, but they appreciate the value received for the cost.
  • Users find the limited customization options in Palantir Foundry restrictive compared to other open-source alternatives.
  • Users find the limited customization options of Palantir Foundry restrictive compared to other open-source platforms.
  • Users find the limited customization options of Palantir Foundry restrict their ability to tailor the platform to their needs.

What Are Recent G2 Reviews of Palantir Foundry?

Bijou Barry
BB
Researched and written by Bijou Barry
Updated April 22, 2026