MLOps for Security and Defense

Machine learning and computer vision are major breakthroughs for companies trying to improve physical safety. While developing ML models has never been easier but developing real-world ML solutions for mission-critical problems such as physical security remains difficult.

Continuous delivery for machine learning can still be difficult.

Here’s a few key MLOps challenges we identified with our partners in the security space.

Big Data

Data sets are GIGANTIC.

Whether you are training models with CCTV-footage or high-resolution images, you’ll quickly need powerful GPU or TPU machines. Your data science team should have easy access to compute resources without having to bother system administrators or DevOps engineers.

Data Security

Data has to be secure.

If your training data is sensitive and subject to regulatory requirements, you’ll need to consider private cloud or on-premise solutions which can incur significant cost. You’ll need to keep full control of where and how your data is stored.


Governance is important.

When a machine learning system makes or aids in mission-critical decisions, it becomes paramount to ensure proper governance for your models. You’ll want to always be able to trace back what data, code and parameters were used to train a model and set in place safeguards against unapproved models getting into production use.

Embedded ML

Deployment is often embedded.

Applying machine learning to physical security often requires models to be deployed on edge devices. Machine learning for IoT devices is still a nascent field and you’ll want to make sure your MLOps infrastructure can support all the purpose-built frameworks such as MXNet, ELL and TensorFlow Lite.

Introducing Valohai

The MLOps platform for deep learning and unstructured data sets.

Valohai is an MLOps platform for teams that work with the most demanding machine learning problems. The platform makes it easy to build pipelines that automate everything from data extraction to model deployment.

Valohai manages cloud infrastructure so a data scientist can run any kind of cloud instance with a single click. Our platform can also be installed on any private cloud or on-premise setup.

Valohai is technology agnostic to ensure that data science teams can integrate pre-processing, training, evaluation and deployment steps together into a cohesive pipeline rather than running steps separately.

Valohai offers powerful capabilities to build pipelines that cover many training runs with different data sets. Pipelines can be dynamic to evaluate models f.ex. against previously trained models.

Valohai automatically versions every model but also what code, data and parameters were used to train them to ensure full reproducibility.

Learn more about MLOps best practices and Valohai from our experts.

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Real world example of deep learning: Sexual abuse material detection

Two Hat Security builds and sells a system for automatically detecting sexual abuse material in video material in darknet or other hard to reach parts of the internet.
Read full case study ➜


Custom models for automating image and document processing

colabel enables companies to automate workflows specific to their business, from recognizing objects in microscopic images to automatically categorizing incoming documents for different internal workflows.
Read full case study ➜
David Wang
Valohai is a super stable environment for using computing resources and thanks to it none of us need to compete about resources internally anymore. Everything is in isolation, so I can even do some rapid testing and Valohai just shuts down the cloud instance when my test ends.
David Wang – Data Scientist, Two Hat Security
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