Within the ever-evolving world of machine studying and DevOps, pace, reliability, and observability are now not elective — they’re necessary. Having spent over 14 years crafting scalable and clever techniques throughout enterprises, I developed InfraSentience, a forward-thinking simulation of recent ML infrastructure tailor-made for MLOps pipelines.
This open-source venture isn’t just code — it’s a mindset. It’s the end result of trade finest practices embedded right into a blueprint that each startups and enterprises can emulate. From containerized FastAPI companies to Triton Inference Server stubs, from Prometheus metrics to Grafana dashboards, InfraSentience mimics a production-ready surroundings.
- Finish-to-Finish Observability: Prometheus metrics and Grafana visualizations allow you to monitor mannequin latency, throughput, and request counts in real-time.
- Triton-Impressed Serving Simulation: Replicates the conduct of NVIDIA Triton for life like inference workflows.
- CI/CD Pipelines: Built-in GitHub Actions simulate seamless deployment pipelines for ML companies.
- Docker-First Structure: Totally containerized for real-world deployment simulation, utilizing Docker Compose.
InfraSentience isn’t only a venture. It’s a apply surroundings, a proof of architectural maturity, and a profession alternative ready to be found. Whether or not you’re an HR supervisor, a CTO, or a technical recruiter, this venture showcases the caliber of real-world experience and future-ready considering any top-tier engineering crew wants.
InfraSentience is public for now, providing an unmissable likelihood to evaluate earlier than it turns into non-public. When you’re on the lookout for somebody to guide your MLOps transformation, let this venture converse for itself.
InfraSentience was constructed utilizing a strong and production-oriented stack:
- FastAPI: Excessive-performance internet framework for real-time inference APIs
- from fastapi import FastAPI, Request
app = FastAPI() - @app.put up(“/simulate”)
async def simulate(request: Request):
knowledge = await request.json()
return {“outcome”: “simulated inference”, “enter”: knowledge}
Docker & Docker Compose: For seamless native deployment and container orchestration
# Dockerfile
FROM python:3.9
WORKDIR /app
COPY necessities.txt ./
RUN pip set up -r necessities.txt
COPY . .
CMD [“uvicorn”, “src.main:app”, “ — host”, “0.0.0.0”, “ — port”, “8000”]
GitHub Actions: Automated CI/CD pipelines for steady supply simulation
# .github/workflows/deploy.yml
identify: Deploy
on: [push]
jobs:
construct:
runs-on: ubuntu-latest
steps:
— makes use of: actions/checkout@v2
— identify: Arrange Python
makes use of: actions/setup-python@v2
with:
python-version: ‘3.9’
— run: pip set up -r necessities.txt
— run: pytest.
- Triton Inference Server (Simulated): Structure modeled on NVIDIA’s serving platform
- Python: Core scripting and simulation logic.
For interviews, collaborations, or consulting alternatives, attain out instantly at [email protected] or view my full GitHub portfolio.
Your future MLOps lead architect may be only one click on away.
Creator: Santhosh Kumar V S
Senior MLOps Engineer | 14+ Years in DevOps & ML
Contact: [email protected]
GitHub: InfraSentience Repository
