Dataiku vs. Pytorch

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Dataiku
Score 8.3 out of 10
N/A
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.N/A
Pytorch
Score 9.3 out of 10
N/A
Pytorch is an open source machine learning (ML) framework boasting a rich ecosystem of tools and libraries that extend PyTorch and support development in computer vision, NLP and or that supports other ML goals.N/A
Pricing
DataikuPytorch
Editions & Modules
Discover
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Business
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Enterprise
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Offerings
Pricing Offerings
DataikuPytorch
Free Trial
YesNo
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
DataikuPytorch
Top Pros

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Top Cons

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Features
DataikuPytorch
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Dataiku
9.1
4 Ratings
9% above category average
Pytorch
-
Ratings
Connect to Multiple Data Sources10.04 Ratings00 Ratings
Extend Existing Data Sources10.04 Ratings00 Ratings
Automatic Data Format Detection10.04 Ratings00 Ratings
MDM Integration6.52 Ratings00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Dataiku
10.0
4 Ratings
18% above category average
Pytorch
-
Ratings
Visualization9.94 Ratings00 Ratings
Interactive Data Analysis10.04 Ratings00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Dataiku
10.0
4 Ratings
20% above category average
Pytorch
-
Ratings
Interactive Data Cleaning and Enrichment10.04 Ratings00 Ratings
Data Transformations10.04 Ratings00 Ratings
Data Encryption10.04 Ratings00 Ratings
Built-in Processors10.04 Ratings00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Dataiku
8.7
4 Ratings
3% above category average
Pytorch
-
Ratings
Multiple Model Development Languages and Tools5.14 Ratings00 Ratings
Automated Machine Learning10.04 Ratings00 Ratings
Single platform for multiple model development10.04 Ratings00 Ratings
Self-Service Model Delivery10.04 Ratings00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Dataiku
9.0
4 Ratings
5% above category average
Pytorch
-
Ratings
Flexible Model Publishing Options9.04 Ratings00 Ratings
Security, Governance, and Cost Controls9.04 Ratings00 Ratings
Best Alternatives
DataikuPytorch
Small Businesses
Jupyter Notebook
Jupyter Notebook
Score 9.2 out of 10
Jupyter Notebook
Jupyter Notebook
Score 9.2 out of 10
Medium-sized Companies
Posit
Posit
Score 9.8 out of 10
Posit
Posit
Score 9.8 out of 10
Enterprises
Posit
Posit
Score 9.8 out of 10
Posit
Posit
Score 9.8 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
DataikuPytorch
Likelihood to Recommend
10.0
(4 ratings)
9.0
(6 ratings)
Usability
10.0
(1 ratings)
10.0
(1 ratings)
Support Rating
9.4
(3 ratings)
-
(0 ratings)
User Testimonials
DataikuPytorch
Likelihood to Recommend
Dataiku
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.
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Open Source
They have created Pytorch Lightening on top of Pytorch to make the life of Data Scientists easy so that they can use complex models they need with just a few lines of code, so it's becoming popular. As compared to TensorFlow(Keras), where we can create custom neural networks by just adding layers, it's slightly complicated in Pytorch.
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Pros
Dataiku
  • The intuitiveness of this tool is very good.
  • Click or Code - If you are a coder, you can code. If you are a manager, you can wrangle with data with visuals
  • The way you can control things, the set of APIs gives a lot of flexibility to a developer.
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Open Source
  • flexibility
  • Clean code, close to the algorithm.
  • Fast
  • Handles GPUs, multiple GPUs on a single machine, CPUs, and Mac.
  • Versatile, can work efficiently on text/audio/image/tabular datasets.
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Cons
Dataiku
  • End product deployment.
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Open Source
  • Since pythonic if developing an app with pytorch as backend the response can be substantially slow and support is less compares to Tensorflow
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Usability
Dataiku
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.
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Open Source
The big advantage of PyTorch is how close it is to the algorithm. Oftentimes, it is easier to read Pytorch code than a given paper directly. I particularly like the object-oriented approach in model definition; it makes things very clean and easy to teach to software engineers.
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Support Rating
Dataiku
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.
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Open Source
No answers on this topic
Alternatives Considered
Dataiku
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.
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Open Source
Pytorch is very, very simple compared to TensorFlow. Simple to install, less dependency issues, and very small learning curve. TensorFlow is very much optimised for robust deployment but very complicated to train simple models and play around with the loss functions. It needs a lot of juggling around with the documentation. The research community also prefers PyTorch, so it becomes easy to find solutions to most of the problems. Keras is very simple and good for learning ML / DL. But when going deep into research or building some product that requires a lot of tweaks and experimentation, Keras is not suitable for that. May be good for proving some hypotheses but not good for rigorous experimentation with complex models.
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Return on Investment
Dataiku
  • Given its open source status, only cost is the learning curve, which is minimal compared to time savings for data exploration.
  • Platform also ease tracking of data processing workflow, unlike Excel.
  • Build-in data visualizations covers many use cases with minimal customization; time saver.
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Open Source
  • The ability to make models as never before
  • Being able to control the bias of models was not done before the arrival of Pytorch in our company
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