Delivering impact with machine learning safely and at scale in the enterprise requires best-practice Machine Learning Operations (MLOps).

Max Kelsen has deep experience and expertise in delivering production-level machine learning solutions. We help you to identify the required skill sets, robust processes and scalable technologies to ensure your machine learning operations meet your organisation's needs.

Max Kelsen works with enterprises across APAC and the USA to design, implement and scale MLOps capabilities across people, processes and technologies.

We work with teams to establish operating models tailored to their organisation's needs, building out required roles and skills, and implementing best-practice MLOps platform capabilities on Google Cloud and Amazon Web Services.

Use Cases

Deployment
Deployment
MLOps allows streamlined deployment of machine learning models to production environments across a range of languages, frameworks and tools.
Monitoring
Monitoring
Machine learning models are dynamic and sensitive to changes in data sets or the real world. MLOps enables the monitoring of model performance so that issues can be caught and addressed before they escalate.
Lifecycle Management
Lifecycle Management
Machine learning models need to be updated over time to prevent performance degradation. MLOps allows for models to be tested and updated in production environments without impacting up-time or availability in business critical systems.
Governance
Governance
Machine learning models in production environments require oversight to ensure they meet all performance and compliance needs. MLOps provides measures including access control, auditing, and logging to meet all regulatory requirements.

Success Stories

See how this capability has been used in practice with real world case studies.

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Transforming Gut Health with Google Cloud and Microba
Microba

Transforming Gut Health with Google Cloud and Microba

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