Azure Data Science Virtual Machines (DSVM) vs. Dataiku

Azure DSVM is useful in [a] Machine Learning environment where GPU-based processing is [required]. [The] most relevant [users] for the Azure DSVM is in ML/AI for model training and processing [high-end] CPU tasks with GPU compatibility. Azure DSVM is built for [a] startup to low medium IT environments where the ML/AI-based projects are [carried] out.

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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• Leveraging data.
• Computer vision.
• Data science.

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• 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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• Azure DSVM pricing must be reduced so that an AI-based start-up can use the Azure DSVM.
• Azure must create an environment to use Azure DSVM offline as well.
• Lack of frameworks

• End product deployment.

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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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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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It's within the Azure environment and it's easy to manage.

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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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• Azure DSVM is little costly with long term support for ML based environments.
• Azure DSVM is very good for short tasking and costs us [a] little low than the on-prem server.
• [Scaling] option is very convenient.

• 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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