Managed MLOps

Keep the gears of innovation turning by offloading operations to our dedicated experts

Managed MLOps Diagram

What is Managed MLOps?

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.

What can you do with help from phData’s experienced machine learning engineers?

What does a typical Managed MLOps package look like?

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:

What is typically included?
What is typically considered out of scope?

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Got questions about Managed MLOps? We've got answers.

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.

How phData Managed MLOps can enhance your ML program

Our enterprise ready MLOps framework and operations teams have a proven record of stability and performance.

  • Proactive monitoring

    Identify issues arising in infrastructure and applications before they create problems for downstream consumers.

  • On-call support

    Whether you only need it during business hours or you need 24x7x365 availability, relieve your teams of on-call duties by leveraging phData’s world-class support.

  • Incident management

    Restore services quickly and ensure issues don’t recur with phData’s expertise in root cause analysis.

  • Automation

    Gain access to phData’s cloud automation, such as containerized ML workflows on Apache Airflow, TRAM for Snowflake onboarding, and SQLMorph for SQL translation.

  • Clear communication

    Stay on top of performance and costs with status reporting, weekly technical reviews, and quarterly business reviews.

Want to learn more about Managed MLOps? We've got you covered.

The Ultimate MLOps Guide: How to Deploy Models to Production

Want to know how MLOps can help you build a repeatable process for deploying robust models into production? Check out our Ultimate MLOPs Guide.

When Should You Retrain Machine Learning Models?

Curious about issues that can affect ML models in production, such as data drift and model retraining? Check out our guide on when to retrain ML models.

What is the Cost to Deploy and Maintain a ML Model?

Wondering if upfront MLOps investments reduce long term costs? Check out our deep dive into the cost of deploying and maintaining ML models.

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Make sure your team is working on what really matters and leave the operational details to us.

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