The Appen platform combines human intelligence from over one million people all over the world with models to create training data for ML projects. Appen users can upload data to the Appen platform, and they provide the annotations, judgments, and labels needed to help create ground truth for models.
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Dataiku
Score7.5 out of 10
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The Dataiku platform unifies all data work, from analytics to Generative AI. It can modernize enterprise analytics and accelerate time to insights with visual, cloud-based tooling for data preparation, visualization, and workflow automation.
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Pricing
Appen
Dataiku
Editions & Modules
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Offerings
Pricing Offerings
Appen
Dataiku
Free Trial
No
Yes
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
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More Pricing Information
Features
Appen
Dataiku
Platform Connectivity
Comparison of Platform Connectivity features of Appen and Dataiku
Feature
Appen
-
Ratings
Dataiku
9.1
4 Ratings
8% above category average
Connect to Multiple Data Sources
00 Ratings
10.04 Ratings
Extend Existing Data Sources
00 Ratings
10.04 Ratings
Automatic Data Format Detection
00 Ratings
10.04 Ratings
MDM Integration
00 Ratings
6.52 Ratings
Data Exploration
Comparison of Data Exploration features of Appen and Dataiku
Feature
Appen
-
Ratings
Dataiku
10.0
4 Ratings
18% above category average
Visualization
00 Ratings
9.94 Ratings
Interactive Data Analysis
00 Ratings
10.04 Ratings
Data Preparation
Comparison of Data Preparation features of Appen and Dataiku
Feature
Appen
-
Ratings
Dataiku
10.0
4 Ratings
20% above category average
Interactive Data Cleaning and Enrichment
00 Ratings
10.04 Ratings
Data Transformations
00 Ratings
10.04 Ratings
Data Encryption
00 Ratings
10.04 Ratings
Built-in Processors
00 Ratings
10.04 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Appen and Dataiku
Feature
Appen
-
Ratings
Dataiku
8.7
4 Ratings
4% above category average
Multiple Model Development Languages and Tools
00 Ratings
5.14 Ratings
Automated Machine Learning
00 Ratings
10.04 Ratings
Single platform for multiple model development
00 Ratings
10.04 Ratings
Self-Service Model Delivery
00 Ratings
10.04 Ratings
Model Deployment
Comparison of Model Deployment features of Appen and Dataiku
It is well suited for the users and potential employee who are free of any job perspective and need their free time to be utilized. Users can use their free time to be used for submission of interesting tasks.
Whereas the number of tasks are very less and processing time is also very extensive and recruitment takes time more.
Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Appen offers projects mostly related to my native language and also according to my expertise . It offers very interesting projects to be completed , which requires not very expertise and less time to be completed for each task. It is also very convenient to use after selection for the task and also well rewarding against the time consumed for the task completion.
Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info