The Dataiku platform unifies data work from analytics to Generative AI. It supports enterprise analytics with visual, cloud-based tooling for data preparation, visualization, and workflow automation.
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Saturn Cloud
Score 7.8 out of 10
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Saturn Cloud is an ML platform for individuals and teams, available on multiple clouds: AWS, Azure, GCP, and OCI. It provides access to computing resources with customizable amounts of memory and power, including GPUs and Dask distributed computing clusters, in a wholly hosted environment. Saturn Cloud is presented as flexible and straightforward for new data scientists while giving senior and experienced staff the
capabilities and configurability they need.…
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Community Pulse
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Chose Dataiku
Dataiku was selected for me, but I am happy about that. I like Dataiku for the user experience, it feels less code-y and I like to demo things to non technical stakeholders because they can still follow along. When you open some other notebooks, you can see that peoples eyes …
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 …
Open source availability is a critical factor given licensing cost of other platforms and budget reasons. Secondly, the available features in the community version covers most of the use cases, thus making it comparable or even outdo commercial versions of other software. …
Anaconda is mainly used by professional data scientists who have profound knowledge of Python coding, mainly used for building some new algorithm block or some optimization, then the module will be integrated into the Dataiku pipeline/workflow. While Dataiku can be used by …
Like said earlier the main keypoint being more compute time . This is the single most advantage Saturn Cloud has over others. This enables us to first try and then eventually move onto Saturn Cloud for the work making it a smooth experience and a rewarding one to say the least.
unlike google colab, the free part of saturn cloud is much more robust. I recently tried to train some BERT models with some data, and to be honest, colab was lagging behind in terms of processing and ram. also, the paid part is much more expensive compared to saturn cloud.
Runtimes are not automatically deleted, transparent free resources: 30hrs a month, persistent disk of 10 GB. The pricing on the premium plan is transparent. No credits that get deleted after 90 days. Possibility to run ipynb notebooks that were written in an IDE, no need to …
Saturn Cloud is an exceptional data science platform that offers a multitude of advantages to organizations. It excels in simplifying and optimizing data science workflows, providing scalable infrastructure resources, and promoting efficient collaboration among teams. With its …
We chose Saturn Cloud over other tools like Databricks and Google Colab because:1. Easy to Use: Saturn Cloud is simple and fun, just like working on a notebook.2. Sizing Help: Saturn Cloud lets us use bigger or smaller computers when we need them, saving money and time.3. Super …
I have used AWS EC2 , GCP Compute Instance, Paperspace Gradient etc. I have selected Saturn Cloud since it provides cost effective and also more reliable and available compute machines compared to the above list.
Saturn Cloud provides an R server, that's super important. Even you can write R on Colab with different settings, but it is inconvenient and slow. Saturn Cloud can give me a different IDE environment that I'm more used to, even if I'm using Python. Whereas Colab is more …
Saturn cloud has a niche market, being a reseller of cloud instances, it ofcourse has a higher cost than the giants but Saturn Cloud is well-suited for organizations that want a cloud-based data science platform that is easy to use and scalable. AWS, GCP, and Azure are more …
It has more free resources to use. Nowadays, all platforms are using the cloud, so I currently use it often. Previous platforms are being used in local host environments. Local host = not so much usage of resources. Cloud platforms = High usage of free resources and …
Saturn Cloud is way cheaper as compared to AWS Sage Maker, and also easy to use we get a notebook setup with the correct environment on the click of a single button. The UI is also a bit simpler and understandable which helps in explaining non-tech individuals and reduces the …
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.
1. Large-scale data processing: If your organization needs to process vast amounts of data, Saturn Cloud's parallel computing capabilities make it an ideal choice for handling these tasks efficiently and quickly.
2. Complex machine learning projects: Saturn Cloud is beneficial when working on machine learning projects requiring scalable resources and powerful computational capabilities, such as training deep learning models or running complex algorithms.
3. Collaborative data science work: Saturn Cloud provides an excellent environment for data scientists and engineers to collaborate on projects, share resources, and maintain version control, ensuring consistency and smooth teamwork.
Less appropriate scenarios for Saturn Cloud: Small-scale projects: For smaller projects with limited data and less demanding computational requirements, Saturn Cloud's advanced features might not be necessary.
While Saturn Cloud offers a range of pre-built templates and workflows, there is currently limited support for customization. For example, users may not be able to modify the pre-configured environments that come with the templates, or may find it difficult to integrate their own custom libraries and tools. Offering more flexibility in this area could help users tailor the platform to their specific needs and workflows.
While Saturn Cloud offers a variety of pre-built environments for data science and machine learning workloads, some users may prefer to use custom Docker images instead. However, the platform currently has limited support for Docker, which can be a limitation for users who need to work with specific dependencies or custom libraries. Adding more robust support for Docker could help to make the platform more versatile and adaptable to a wider range of use cases.
The user experience is very good. Everything feels intuitive and "flows" (sorry excuse the pun) so nicely, and the customization level is also appropriate to the tool. Even as a newer data scientist, it felt easy to use and the explanations/tutorials were very good. The documentation is also at a good level
This is user friendly , better than its counterparts. Anyone familiar working with other cloud solutions for GPU will agree on this. Hence the rating of 10 was given to this. I personally love the fact that I get so much compute time for being a free user which is very efficient in terms of budget
The amazing part of Dataiku DSS is their customer service. Based on urgency and technical level, you get a reply from the Dataiku engineer when you raise a query. So far, my queries have been pretty complex to solve, so I have received solutions even from the CTO of the company as well, which is why I would describe their customer support as very good.
Dataiku was selected for me, but I am happy about that. I like Dataiku for the user experience, it feels less code-y and I like to demo things to non technical stakeholders because they can still follow along. When you open some other notebooks, you can see that peoples eyes start to glaze over
Saturn Cloud is an exceptional data science platform that offers a multitude of advantages to organizations. It excels in simplifying and optimizing data science workflows, providing scalable infrastructure resources, and promoting efficient collaboration among teams. With its user-friendly interface and seamless integration with popular tools, Saturn Cloud enhances productivity and accelerates the development of data science models. The platform's automation capabilities streamline repetitive tasks, freeing up valuable time for experimentation and analysis. Additionally, Saturn Cloud's cost-effective approach, with on-demand cloud resources, ensures efficient resource utilization and budget optimization. Its features for version control, reproducibility, and deployment management further solidify Saturn Cloud's position as a superior choice for organizations seeking to leverage the power of data science effectively.
Faster experimentation and model iteration: Saturn Cloud's scalability and user-friendly interface can help organizations to reduce the time required to set up and run experiments, as well as to iterate on models more quickly. This can help to speed up the development cycle and get products to market more quickly.
Increased productivity and efficiency: Saturn Cloud's built-in tools and pre-built environments can help to streamline data science workflows and reduce the time required to set up and configure environments. This can help data scientists to focus on higher-value tasks and improve overall productivity.
