Machine Learning Platforms (ML) Platform

Machine Learning Platforms

What is Machine Learning Platforms Platform?

A comprehensive collection of tools, libraries and resources that allows data scientists and machine learning engineers to build and deploy Machine Learning (ML) powered business applications. ML tools automate the delivery life-cycle of predictive applications capable of processing big data using machine learning algorithms.

Common Features

  • Data Ingestion
  • Data Pre-Processing
  • Feature Engineering
  • Algorithm Diversity
  • Model Training
  • Model Tuning
  • Model Monitoring and Management
  • Performance and Scalability
  • Ensembling
  • Openness and Flexibility
  • Explainability
  • Data Exploration and Visualization
  • Pre-Packaged AI/ML Services
  • Data Labeling
  • Algorithm Recommendation

Top Machine Learning Platforms (ML) Platform

2024 Data Quadrant Awards

2023 Emotional Footprint Awards

At SoftwareReviews, we take pride in recognizing excellence. Each year, we present the Data Quadrant Awards to top-performing software products based solely on authentic user reviews, without any paid placements or analyst opinions. These awards highlight software products that excel in terms of features, vendor capabilities, and customer relationships, earning them the highest overall rankings.

At SoftwareReviews, we take pride in recognizing excellence. Each year, we present the Emotional Footprint Awards to top-performing software products based solely on authentic user reviews, without any paid placements or analyst opinions. These awards shine a spotlight on software vendors who excel in crafting and nurturing strong customer relationships.

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Products: 12
Next Award: Jun 2025

Top Machine Learning Platforms Platform 2024

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Composite Score
8.7 /10
CX Score
9.0 /10

MATLAB is a high-level language and interactive environment for numerical computation, visualization, and programming.

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Pros

  • Reliable
  • Client Friendly Policies
  • Unique Features
  • Respectful
Badge Winner
Badge Winner
Composite Score
8.7 /10
CX Score
8.9 /10

Machine Learning Studio is a powerfully simple browser-based, visual drag-and-drop authoring environment where no coding is necessary. Go from idea to deployment in a matter of clicks. Microsoft Azure Machine Learning Studio is a collaborative, drag-and-drop tool you can use to build, test, and deploy predictive analytics solutions on your data. Machine Learning Studio publishes models as web services that can easily be consumed by custom apps or BI tools such as Excel.

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Pros

  • Performance Enhancing
  • Respectful
  • Includes Product Enhancements
  • Reliable
Badge Winner
Badge Winner
Composite Score
8.6 /10
CX Score
8.8 /10

Cloud Machine Learning Engine is a managed service that lets developers and data scientists build and run superior machine learning models in production. Cloud ML Engine offers training and prediction services, which can be used together or individually. It has been used by enterprises to solve problems ranging from identifying clouds in satellite images, ensuring food safety, and responding four times faster to customer emails. The training and prediction services within ML Engine are now referred to as AI Platform Training and AI Platform Prediction.

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Pros

  • Inspires Innovation
  • Respectful
  • Reliable
  • Performance Enhancing
Badge Winner
Badge Winner
Composite Score
8.2 /10
CX Score
8.4 /10

Amazon Machine Learning is an Amazon Web Services product that allows a developer to discover patterns in end-user data through algorithms, construct mathematical models based on these patterns and then create and implement predictive applications.

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Pros

  • Reliable
  • Security Protects
  • Continually Improving Product
  • Respectful
Composite Score
8.2 /10
CX Score
8.5 /10

TFX is an end-to-end platform for deploying production ML pipelines

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Pros

  • Helps Innovate
  • Continually Improving Product
  • Reliable
  • Enables Productivity
Badge Winner
Composite Score
8.1 /10
CX Score
8.4 /10

The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals.

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Pros

  • Continually Improving Product
  • Effective Service
  • Fair
  • Client's Interest First
Badge Winner
Badge Winner
Dataiku

Dataiku

Composite Score
7.9 /10
CX Score
8.1 /10

Dataiku is the platform democratizing access to data and enabling enterprises to build their own path to AI in a human-centric way.

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Pros

  • Helps Innovate
  • Continually Improving Product
  • Reliable
  • Performance Enhancing
Badge Winner
Badge Winner
Eclipse Foundation Inc

Eclipse Deeplearning4j

Composite Score
7.8 /10
CX Score
8.1 /10

Eclipse Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Apache Spark, DL4J brings AI to business environments for use on distributed GPUs and CPUs.

Pros

  • Helps Innovate
  • Continually Improving Product
  • Reliable
  • Performance Enhancing
Alteryx

Alteryx

Composite Score
7.7 /10
CX Score
7.8 /10

Alteryx provides a single workflow for data blending, analytics, and reporting. This workflow allows the seamless blending of internal, third party and cloud-based data, and simple analysis using 60+ prebuilt tools for spatial and predictive analytics.

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Pros

  • Enables Productivity
  • Reliable
  • Saves Time
  • Acts with Integrity
Badge Winner
Composite Score
7.6 /10
CX Score
7.5 /10

The DataRobot Enterprise AI platform includes two independent but fully integrable machine learning model building products, and each can be deployed in multiple ways to match your business needs and IT requirements. All configurations feature a constantly expanding set of diverse, best-in-class algorithms from R, Python, H2O, Spark, and other sources, giving you the best set of tools for your machine learning and AI projects.

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Pros

  • Helps Innovate
  • Respectful
  • Reliable
  • Security Protects

Cons

  • Under Delivered