# 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">I’m comparing <a class="a a--md" elv="true" href="https://www.g2.com/categories/data-science-and-machine-learning-platforms?utm_source=chatgpt.com"><strong>best data science service for cloud-based apps</strong></a> because teams shipping cloud apps usually need ML that fits into production patterns (deployments, monitoring, access controls), not just offline notebooks.</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 a few I’m considering: </p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/amazon-sagemaker/reviews?utm_source=chatgpt.com"><strong>Amazon SageMaker</strong></a>: Managed service for building, training, and deploying ML models on AWS. Useful when your application stack and infra are already centered on AWS.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/google-vertex-ai/reviews?utm_source=chatgpt.com"><strong>Google Vertex AI</strong></a>: Managed ML platform for end-to-end workflows in Google Cloud. Often used for production ML pipelines and model deployment/operations.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/microsoft-azure-machine-learning/reviews?utm_source=chatgpt.com"><strong>Microsoft Azure Machine Learning</strong></a>: Managed ML service for training, deployment, and governance in Azure. Good option when you want ML tied to Azure security, networking, and MLOps patterns.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/domino-enterprise-ai-platform/reviews?utm_source=chatgpt.com"><strong>Domino Enterprise AI Platform</strong></a>: Enterprise data science platform focused on collaboration and model lifecycle management. Often used when teams need reproducibility and controls across cloud environments.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"><strong>For cloud-based apps, what’s been the most reliable production setup for ML—and what part was hardest (deployment, monitoring, or governance)?</strong></p>

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- Posted at: 1 day ago
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