# What's the best DataRobot consulting service for accelerating ML model deployment and governance?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Looking for input from G2 community and ML practitioners in the<a class="a a--md" elv="true" href="https://www.g2.com/categories/datarobot-consulting-services"> </a><a class="a a--md" elv="true" href="https://www.g2.com/categories/datarobot-consulting-services">DataRobot Consulting Services category</a> on the deployment-and-governance combination</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">The partners with the strongest positioning for deployment and governance acceleration:</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/accenture/reviews"><strong>Accenture</strong></a><strong>:</strong> Accenture's AI governance frameworks, developed across financial services and healthcare DataRobot deployments, provide pre-built model documentation templates and approval workflow designs that reduce the time from model selection to governance-approved production deployment. </li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/boston-consulting-group/reviews"><strong>Boston Consulting Group</strong></a><strong>:</strong> For organisations where the governance acceleration is blocked by executive risk appetite rather than technical implementation, BCG's Responsible AI framework and GAMMA practice address the organisational governance design that enables faster deployment approval cycles.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/capgemini-services/reviews"><strong>Capgemini Services</strong></a><strong>:</strong> For enterprise deployments where governance standardisation must span multiple geographies and business units simultaneously, Capgemini's delivery scale provides the consistent governance template rollout that boutique partners cannot match at volume.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">For ML leaders who have deployed models through a DataRobot consulting engagement, what was the governance step that most frequently delayed production deployment, and did the consulting partner have a pre-built solution for it or did your team have to design it during the engagement?</p><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"></p>

##### Post Metadata
- Posted at: 1 day ago
- Author title: Marketing Executive
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;I’d look at what happens after the consultant leaves. Accenture or Capgemini can bring governance templates and approval workflows, but the real win is whether your team can deploy the next model without another engagement. Otherwise, you’ve accelerated one deployment rather than fixed the governance bottleneck.&lt;/p&gt;

##### Comment Metadata
- Posted at: 1 day ago





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