A next-generation ML program requires ongoing support to keep systems running. Some of those systems support data science teams in their development of new models. In turn, those models will be deployed and require operational support and maintenance. Managed MLOps supports vital services to make certain your talented teams can pursue innovation without interruption.
Many data scientists and ML engineers are constantly putting out fires and bogged down by technical details.
With Managed MLOps, your teams can focus on new projects that generate value rather than getting bogged down in technical details.
With new ML tools and platforms being released and updated constantly, no two companies are alike. Here are some examples of services that are typically included in a Managed MLOps Framework:
Not all organizations have an ML program at a scale that requires dedicated operations support, but we’ve got the answers to help determine what you need.
ML applications are like traditional software in many ways, but they involve data, which can be messy. For example, a common issue is data drift, where new business data starts to differ from that which was used to train the model. These issues require operations experts with the ability to interpret statistical information and make quantitative decisions. In a similar vein, data science platforms may look like SaaS or PaaS on the surface, but the computational complexity of ML brings heavy infrastructure and scaling requirements. Servicing this infrastructure while also keeping a handle on costs requires careful calculations and guardrails.
Some ML applications are lightweight and easy to scale with container platforms, while others have more significant demands for compute power or memory. Costs can be controlled by selecting the right size for virtual machines and clusters. For more details on costs, check out our article on the cost of deploying ML applications.
Every organization is different when it comes to the scale of their ML program. Some teams will have just a few data scientists and ML applications, while others are servicing hundreds of models and deploying new ones weekly. The cost of Managed MLOps varies significantly based on the platforms, infrastructure, and applications under support, but our services start in the ballpark of $150k per year, which includes prepaid credits that can be used for things like maintenance, upgrades, and end-user support.
Our enterprise ready MLOps framework and operations teams have a proven record of stability and performance.
Make sure your team is working on what really matters and leave the operational details to us.