Deploying AI/ML Models to Production: A Practical Guide
Learn how to deploy machine learning models to production with Docker, GPU orchestration, model versioning, A/B testing, and monitoring. Real-world MLOps...
The MLOps Gap
Training a model in Jupyter notebook is the easy part. Getting it into production — with versioning, monitoring, rollback, and scaling — is where 85% of ML projects fail.
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At TechSaaS, we've built ML infrastructure for companies deploying everything from NLP models to computer vision pipelines. Here's what we've learned.
The Production ML Stack
1. Containerization
Every model gets its own Docker container with pinned dependencies:
FROM python:3.12-slim
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY model/ /app/model/
COPY serve.py /app/
CMD ["python", "/app/serve.py"]2. Model Versioning
Store model artifacts with version tags:
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3. GPU Orchestration
For inference workloads, NVIDIA Container Toolkit enables GPU access in Docker:
services:
ml-model:
image: my-model:v1.0
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]4. A/B Testing
Route a percentage of traffic to the new model and compare metrics:
5. Monitoring
Track model-specific metrics beyond standard HTTP monitoring:
The NexusAI Approach
Our NexusAI platform handles all of this automatically:
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Self-Hosted ML Infrastructure
Self-hosted GPU servers are dramatically cheaper than cloud GPU instances:
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For inference workloads that run 24/7, self-hosted GPUs pay for themselves in 1-2 months.
TechSaaS designs and manages ML infrastructure from training to production. We handle GPU orchestration, model serving, and monitoring so your data science team focuses on model quality. Contact [email protected].
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