# What is the best data science service for cloud-based apps?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Cloud-native teams need data science tools that can scale with their infrastructure, support modern workflows, and integrate well with other cloud services. Whether the goal is building predictive models, running analytics, or deploying machine learning in production, selecting the right platform is critical.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Here are some top-rated <a class="a a--md" elv="true" href="https://www.g2.com/categories/data-science-and-machine-learning-platforms">data science services</a> commonly used to support cloud-based applications:</p><ol>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/google-vertex-ai/reviews">Vertex AI</a>: A fully managed ML platform on Google Cloud with support for AutoML and custom model deployment. Strong fit for teams already building on GCP.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/databricks-data-intelligence-platform/reviews">Databricks Data Intelligence Platform</a>: Combines cloud-scale data processing with collaborative ML development. Often used in cloud environments for unified data and AI workflows.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/deepnote/reviews">Deepnote</a>: Web-based collaborative notebooks that integrate with cloud storage and data sources. Ideal for teams iterating quickly on analytics and experimentation.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/dataiku/reviews">Dataiku</a>: Offers a flexible interface for both code and no-code workflows, with deployment options across multiple cloud providers. Supports collaboration between technical and business users.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/saturn-cloud-saturn-cloud/reviews">Saturn Cloud</a>: Jupyter-based environment with scalable compute resources. Built for Python users managing data-heavy cloud applications with minimal DevOps burden.</li>
</ol><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Which platforms have proven most effective for supporting ML and analytics in cloud-based systems? Feedback from teams deploying in production or integrating across cloud ecosystems would be especially helpful.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Let me know if you'd like a version focused more on deployment, infrastructure, or DevOps use cases.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p>

##### Post Metadata
- Posted at: 11 months ago
- Author title: BBCOR Tester
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;I heard Vertex AI and Databricks Data Intelligence Platform are both strong for data science in cloud-based apps. Which one provides a better workflow for model development and deployment?&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;

##### Comment Metadata
- Posted at: 11 months ago
- Author title: BBCOR Tester





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