We are fixing the hardest entry point in ML: setup
Deepline is building the entry point to production ML by removing setup complexity in pipelines. Today we focus on simplifying Airflow + MLflow setup; later we will expand into monitoring, retraining, and portability.
The Thesis
The Problem
Pipeline setup is slow, brittle, and inconsistent. Teams waste weeks on configuration before they can focus on the actual machine learning work that drives value.
Why Existing Tools Fail
Current solutions create vendor lock-in, require extensive manual configurations, and introduce too many moving parts. This complexity compounds over time, creating operational debt before teams even reach production.
Our Contrarian View
We believe you should start with setup, build credibility with immediate value, and then layer in monitoring and retraining capabilities. Most platforms try to solve everything at once - we focus on the sharpest pain point first.
What We Are Building
Today: A setup simplification layer for Airflow and MLflow that reduces weeks of configuration to a single YAML file and two commands.
Tomorrow: Our roadmap expands into three key phases - Observe (monitoring and drift detection), Adapt (safe retraining workflows), and Port (cloud-agnostic deployment).
Roadmap
Team
Taimoor, Founder & CEO
Data Scientist and AI Engineer, leading Deepline to eliminate ML pipeline setup complexity.
Abdul, Chief of Engineering
Systems design engineer, leading technical architecture and execution.
Operating Principles
- Solve the sharpest pain first
- Truth over status
- Quantify or delete
- Default to automation
- Portability first