Machine Learning Development built for Cybersecurity companies
What is Machine Learning Development?
We build ML systems engineered to stay accurate after deployment, not just in the notebook. For Cybersecurity companies, this means navigating SOC 2, ISO 27001 while shipping fast.
Who needs Machine Learning Development for Cybersecurity?
- Detecting threats in real time at scale
- False positive rates undermining analyst trust
- Integrating across fragmented security tool stacks
- Models performing well in testing, poorly in production
- No retraining pipeline as data drifts over time
Best Machine Learning Development company
Security products are held to a higher engineering bar than almost any other software category.
We specialize in Machine Learning Development for Cybersecurity, with a growing track record across industries that move fast.
Get a Machine Learning Development proposal tailored to your Cybersecurity goals
Book a free 30-minute discovery callCommon Machine Learning Development mistakes we see across Cybersecurity teams
- Detecting threats in real time at scale
- False positive rates undermining analyst trust
- Integrating across fragmented security tool stacks
- Meeting compliance certifications for enterprise sales
- Keeping pace with evolving attack techniques
How GarudLabs approaches Machine Learning Development for Cybersecurity companies
ML model architecture and training pipeline
Feature engineering and data pipeline
Model deployment and serving infrastructure
Drift detection and retraining automation
Experiment tracking and model versioning
Core Technology Stack
PythonPyTorchscikit-learnMLflowKubeflowAWS SageMaker
Timeline, investment & compliance
Typical timeline
8-16 weeks
Typical investment
$25,000 - $170,000
Machine Learning Development engagements in this combination typically need to account for:
- SOC 2
- ISO 27001
- NIST frameworks
- GDPR
Our Approach
Cybersecurity software fails quietly if it isn't built for detecting threats in real time at scale from the start. We scope for SOC 2 and ISO 27001 requirements up front, and keep the review cadence tight enough to catch drift before it ships.
See our case studiesGet a Machine Learning Development proposal tailored to your Cybersecurity goals
Get a free project cost estimateFrequently Asked Questions
Most Machine Learning Development engagements take 8-16 weeks, depending on scope and how clear the requirements are upfront. We'll give you a firm timeline estimate after a short discovery call, not a generic range.
Machine Learning Development projects with us typically range from $25,000 - $170,000, scaled to the complexity of your requirements. We provide a detailed, itemized estimate before any work begins so there are no surprises mid-project.
For machine learning development, we typically work with Python, PyTorch, scikit-learn, MLflow, chosen based on your specific scalability and integration requirements. We're not tied to a single stack and will recommend the right tools for your situation rather than our default.
A typical machine learning development engagement includes ml model architecture and training pipeline, along with the other deliverables outlined in our proposal, such as documentation and a post-launch support window. The exact scope is tailored to your project during discovery.
Most clients come to us for machine learning development because of models performing well in testing, poorly in production, among other related challenges. We start every engagement by mapping your specific pain points before writing a single line of code.
We've delivered multiple cybersecurity projects addressing challenges like detecting threats in real time at scale. We helped a cybersecurity startup reduce false positive alert rates by 37% with an improved detection pipeline.
For cybersecurity clients, we pay close attention to SOC 2, ISO 27001, building these requirements into the architecture from day one rather than retrofitting them later. We'll confirm which specific regulations apply to your business during discovery.
The most common challenges we solve for cybersecurity clients include detecting threats in real time at scale and false positive rates undermining analyst trust. We tailor our discovery process to surface the specific version of these problems your business faces.