ML Ops
Scale your AI systems with ML Ops
What is ML Ops?

ML Ops (Machine Learning Operations) combines machine learning with DevOps to streamline model deployment and management in production environments. By leveraging ML Ops, businesses can streamline ML processes, reduce costs, and accelerate time-to-market for their ML applications.

MLOps 101: A Primer on Machine Learning Operations

Get a comprehensive intro to MLOps, including practical recommendations for companies looking to implement MLOps practices and build a successful MLOps team.

Recent projects


Built ML ops platform for public medical device company

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Optimized deep learning pipelines for healthcare startup

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Automated pricing strategy for a PE-owned insurance MGA

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Optimized compute resources for public drug company

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How Tribe can help

ML infrastructure

MLOps can help companies set up robust ML infrastructure that is scalable and can support large-scale machine learning operations. This involves selecting appropriate hardware, recommending the use of TPUs, GPUs, or CPUs, and selecting the right cloud provider.

Model monitoring and management

MLOps helps companies monitor and manage machine learning models in production environments by tracking model and data versions, monitoring for data drift and uptime, and enabling rollback capability. It provides insights for diagnosing and debugging issues, and facilitates deployment into both cloud and IoT environments. By leveraging MLOps, companies can ensure that their models perform as expected, and make timely adjustments when issues arise.

Reproducibility & scalability

MLOps ensures scalable and reproducible machine learning models by implementing version control, logging, and testing processes, and setting up data and model versioning and data lineage. By leveraging MLOps, companies can develop and deploy machine learning models efficiently, while ensuring quality and consistency of outputs.

Security & compliance

MLOps ensures compliance with industry regulations and best practices for security by implementing appropriate data encryption, access controls, and testing procedures. By adhering to best practices like access control, encryption, and data anonymization, MLOps helps ensure compliance with data management regulations such as GDPR, HIPAA, etc. Companies can mitigate risks and develop and deploy machine learning models responsibly by leveraging MLOps for best practices in security and compliance.
Work with the best talent in AI

CTO at Togal
ML at Adblock

Deep learning, Neural networks, ML engineer
Principal ML Engineer

Director of Data Science at Appen,

Researcher at Toyota

Infrastructure, Data Science, ML Ops
ML Engineer

Engineer at Cityblock Health

Sr Engineer Flatiron Health

Product, Dimensional modeling, Analytics
ML Engineer

Led ML at ClearBrain , Amplitude

Recommendations, ML infra, AWS
ML Scientist

PhD from Cambridge University

Worked on NLP at Amazon Alexa

NLP, Chatbots, NLU
After Tribe joined, we went from 0 to 100 in terms of time to market. This work was a jet propulsion for our team.
Tribe came in and systematically benchmarked and tested our infrastructure, eventually implementing a number of improvements and fixes that led to massive cost savings.
This is one of the most dynamic consulting engagements we’ve ever had. The value Tribe has already provided is exceptional.
We got so much more out of this project than we thought we would. And that’s in large part to the quality of the people Tribe brought in.
I think the benefit with Tribe is they’re so experienced in the field, they can come in and very quickly assess what needs to be done and start making progress.

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