Top Eclipse Deeplearning4j Alternatives and Competitors
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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.
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
7.9
Composite
Score
+99
Emotional
Footprint
10
Reviews
Best Alternatives and Competitors to Eclipse Deeplearning4j
Compare how Eclipse Deeplearning4j stacks up to the competition in the areas that matter most to real users to short list options that will best fit your business needs.
MathWorks
MathWorks MATLAB
8.7
Composite
Score
+92
Emotional
Footprint
62
Reviews
Reviews Say
Compared to Eclipse Deeplearning4j, MathWorks MATLAB is:
Harder to Customize
Less Caring
Less Inspiring
Less Efficient
Harder to Use
Harder to Implement
MATLAB is a high-level language and interactive environment for numerical computation, visualization, and programming. It is a programming and numeric computing platform used by millions of engineers and scientists to analyze data, develop algorithms, and create models.
Microsoft Corporation
Microsoft Azure Machine Learning
8.7
Composite
Score
+92
Emotional
Footprint
42
Reviews
Reviews Say
Compared to Eclipse Deeplearning4j, Microsoft Azure Machine Learning is:
Worse at Support
Less Transparent
Less Efficient
Harder to Customize
Harder to Use
Less Innovative
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.
Google Cloud Vertex AI
8.7
Composite
Score
+92
Emotional
Footprint
81
Reviews
Reviews Say
Compared to Eclipse Deeplearning4j, Google Cloud Vertex AI is:
Harder to Use
Harder to Customize
Worse at Support
Less Innovative
Harder to Implement
Less Transparent
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.
Amazon
AWS Machine Learning
8.4
Composite
Score
+88
Emotional
Footprint
46
Reviews
Reviews Say
Compared to Eclipse Deeplearning4j, AWS Machine Learning is:
Less Caring
Harder to Customize
Less Transparent
Harder to Use
Less Inspiring
Worse at Support
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.
TensorFlow
TensorFlow TFX
8.2
Composite
Score
+97
Emotional
Footprint
15
Reviews
Reviews Say
Compared to Eclipse Deeplearning4j, TensorFlow TFX is:
Less Transparent
Harder to Use
Harder to Implement
Worse at Support
Worse at Integrating
Harder to Customize
TFX is an end-to-end platform for deploying production ML pipelines. A TFX pipeline is a sequence of components that implement an ML pipeline which is specifically designed for scalable, high-performance machine learning tasks. Components are built using TFX libraries which can also be used individually. When you're ready to move your models from research to production, TFX can be used to create and manage a production pipeline.
Databricks
Databricks Data Intelligence Platform
8.1
Composite
Score
+96
Emotional
Footprint
34
Reviews
Reviews Say
Compared to Eclipse Deeplearning4j, Databricks Data Intelligence Platform is:
Harder to Customize
Worse at Support
Harder to Implement
Less Efficient
Harder to Use
Less Transparent
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. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals.
Microsoft Corporation
Microsoft Fabric
8.0
Composite
Score
+98
Emotional
Footprint
10
Reviews
Reviews Say
Compared to Eclipse Deeplearning4j, Microsoft Fabric is:
Better at Integrating
Better at Training
Harder to Customize
Worse at Support
Harder to Use
Harder to Implement
Microsoft Fabric is a platform that allows users to get, create, share, and visualize data using an array of tools. Give your data teams all the tools they need in a unified experience that helps reduce the cost and effort of data integration, governance, and security.
KNIME
KNIME Analytics Platform
7.8
Composite
Score
+87
Emotional
Footprint
32
Reviews
Reviews Say
Compared to Eclipse Deeplearning4j, KNIME Analytics Platform is:
Less Transparent
Worse at Support
Less Caring
Harder to Customize
Harder to Use
Less Respectful
KNIME offers a complete platform for end-to-end data science, from creating analytical models, to deploying them and sharing insights within the organization, through to data apps and services. The free and open source KNIME Analytics Platform allows users to easily build analyses with an intuitive, low-code/no-code interface. KNIME Business Hub enables users across different disciplines to collaborate and productionize analytical solutions created using KNIME Analytics Platform.
Dataiku
Dataiku
7.7
Composite
Score
+94
Emotional
Footprint
28
Reviews
Reviews Say
Compared to Eclipse Deeplearning4j, Dataiku is:
Better at Integrating
Harder to Customize
Harder to Implement
Harder to Use
Less Efficient
Less Caring
Dataiku is the platform democratizing access to data and enabling enterprises to build their own path to AI in a human-centric way. With Dataiku, everyone can get involved in data and AI projects on a single platform for design and production that delivers use cases in days, not months. No matter where they sit, they work in a safe and governed way that helps manage risk and create trust to drive high-quality outputs and value for your business.
DataRobot, Inc.
DataRobot AI Platform
7.7
Composite
Score
+82
Emotional
Footprint
35
Reviews
Reviews Say
Compared to Eclipse Deeplearning4j, DataRobot AI Platform is:
Less Transparent
Less Caring
Less Inspiring
Harder to Customize
Less Efficient
Worse at Support
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.
Alteryx
Alteryx
7.4
Composite
Score
+87
Emotional
Footprint
32
Reviews
Reviews Say
Compared to Eclipse Deeplearning4j, Alteryx is:
Less Innovative
Less Respectful
Worse at Support
Harder to Customize
Less Transparent
Less Inspiring
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. With Alteryx Machine Learning’s automated insight generation, you’ll quickly uncover hidden signals and key relationships in your data.
H2O.ai
H2O AI Cloud
7.3
Composite
Score
+87
Emotional
Footprint
14
Reviews
Reviews Say
Compared to Eclipse Deeplearning4j, H2O AI Cloud is:
Less Transparent
Less Caring
Worse at Support
Harder to Use
Less Reliable
Harder to Customize
H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models.
Altair Engineering, Inc.
Altair RapidMiner
7.2
Composite
Score
+80
Emotional
Footprint
27
Reviews
Reviews Say
Compared to Eclipse Deeplearning4j, Altair RapidMiner is:
Less Transparent
Less Caring
Harder to Customize
Less Innovative
Worse at Support
Less Efficient
Regardless of where your organization is on its data journey, Altair RapidMiner can help overcome the most challenging obstacles in your way. We offer a path to modernization for established data analytics teams as well as a path to automation for teams just getting started. We do this without requiring your organization to radically change your people, processes, computing environment, or existing data landscape, helping you achieve your data goals without changing who you are or what you have.
Altair Engineering, Inc.
Altair Data Analytics & AI
6.9
Composite
Score
+80
Emotional
Footprint
22
Reviews
Reviews Say
Compared to Eclipse Deeplearning4j, Altair Data Analytics & AI is:
Less Inspiring
Less Innovative
Harder to Use
Less Caring
Harder to Customize
Harder to Implement
Altair Data Analytics enables organizations worldwide to compete more effectively by operationalizing data analytics and AI with secure, governed, and scalable strategies. We deliver world-class, self-service analytics solutions for data preparation, predictive modeling, stream processing, visualization and more. Our solutions are designed for many different skill sets: from data scientists and engineers to MLOps specialists to business analysts to executives. With a no-code, cloud-ready interface, organizations can harness the full power of analytics and AI throughout its complete data lifecycle, driving next-level business results.
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