One of the valuable features of Dataiku is the workflow capability. It allows us to organize a workflow efficiently. The platform has a visual interface, making it much easier for educated professionals to organize their work. This feature is useful because it simplifies tasks and eliminates the need for a data scientist. If you are knowledgeable about AI, you can directly write using primitive tools like Pantera flow, PyTorch, and Scikit-learn. However, Dataiku makes this process much easier.

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Traceability and collaboration are essential for me. I have eight or nine engineers working together. Integration with machine learning is also crucial for us. 

Additionally, traceability is vital since I manage many cohorts, and collaboration is key as I have multiple engineers substituting for one another.

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Dataiku is highly regarded as it is a leader in the Gartner ranking. It offers most of the capabilities required for data science, MLOps, and LLMOps. Integration with public cloud and multiple other platforms is excellent. The product is easy to install and can be maintained by a single expert. It supports good functionalities that are essential in data visualization and responsible AI. View full review »

The most valuable feature of Dataiku, in my opinion, is the possibility to use Spark, which is a distributed compute engine. This is a feature that is usually appreciated by our customers. 

Additionally, the automation features have been impactful, particularly in the deployment phase, as we use what Dataiku calls deployer nodes. Dataiku primarily enhances the speed at which our customers can develop or train their machine learning models since it is a drag-and-drop platform. Our clients can easily drag and drop components and use them on the spot.

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The advantage is that you can focus on machine learning while having access to what they call 'recipes.' These recipes allow me to preprocess and prepare data without writing any code. This saves a lot of time because I can quickly handle all the data preparation tasks and concentrate on building my machine learning algorithms.

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It's got good feature selection and creation of feature stores, and it also gives different aspects of modeling approaches. There are a lot of similarities with DataRobot. 

So feature selection, different modeling, and financial metrics are good aspects. 

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Data Science Studio's data science model is very useful.

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The best feature is the user interface. It allows us to see the visual flows. 

The intuitiveness of the software itself is very good. 

It has both click or code flexibility, which is quite useful. I already have coding knowledge, so I go for coding as my go-to method of interaction. However, for a normal person that may not have a background in coding, the visual lists are really great. It makes it easy for non-IT individuals to navigate everything.

The pricing is very good.

The solution is quite stable.

You can scale the solution well.

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The most valuable feature is the set of visual data preparation tools. 

The solution supports code from different languages including Python and R. Whatever code you want to use, it works well.

This solution allows us to store and retrieve data directly into BigQuery.

The documentation and tutorials are quite good.

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I like the interface, which is probably my favorite part of the solution. It is really user-friendly. I might think that it is user-friendly because I'm in IT and it seems familiar to me. It is colorful and I think it is really beautiful and well-designed.

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The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction. 

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• Process scheduler (called Scenario).
• Cloud-based process run, which helps in not keeping the systems on while processes are running.

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