# What low-code machine learning platforms work well for a data team that needs full ML workflow capabilities in one place without stitching together separate data prep and deployment tools?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">We've been researching how data teams avoid the "stitched-together stack" problem, where data prep lives in one tool, training in another, and deployment somewhere else. The goal we keep hearing is a single platform that carries the full ML workflow, so this is a look at what the<a class="a a--md" elv="true" href="https://www.g2.com/categories/low-code-machine-learning-platforms"> </a><a class="a a--md" elv="true" href="https://www.g2.com/categories/low-code-machine-learning-platforms">low-code machine learning platforms</a> reviews suggest teams are hoping to find:</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p><ul>
<li>One place for data prep, training, deployment, and monitoring</li>
<li>Low-code access for non-engineers without cutting off code when it's needed</li>
<li>Native connections to the data sources the team already uses</li>
<li>Less setup overhead than assembling separate services</li>
</ul><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 platforms reviewers pointed to for that all-in-one workflow:</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/gemini-enterprise-agent-platform"><strong>Gemini Enterprise Agent Platform</strong></a> — Reviewers on GCP describe the full lifecycle in one place, from data prep through monitoring, with tight links to BigQuery and Cloud Storage that cut setup effort for teams already there.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/amazon-sagemaker"><strong>Amazon SageMaker</strong></a> — Described as a wide-ranging, end-to-end ecosystem for getting models into production without building much infrastructure, though reviewers say the starting complexity is real.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/ibm-watsonx-ai"><strong>IBM watsonx.ai</strong></a> — Comes up for broad enterprise ML capability, with a caveat from smaller-team reviewers that it can feel heavily enterprise-oriented.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/dataiku"><strong>Dataiku</strong></a> — Repeatedly called the single hub where analysts and data scientists collaborate across visual workflows and code in the same project.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">For teams that consolidated onto one platform, did it actually reduce the number of tools you run, or did you end up bolting things back on? And for the cloud-native options, how much did being on a specific cloud decide it for you?</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: 2 months ago
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;The cloud you already run on tends to decide this more than any feature list. A team already in BigQuery can consolidate cleanly into Vertex because the data is right there, and the reverse holds on AWS. Once your warehouse sits outside that cloud, the all-in-one claim starts leaking, and tools creep back in.&lt;/p&gt;

##### Comment Metadata
- Posted at: about 2 months ago
- Author title: Marketer





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