How to build an end-to-end ML project in 8 weeks (even if you’ve never deployed anything).
Most data scientists stop at the notebook. Model works. Metrics look good. Then it sits there.
The gap between “𝐦𝐨𝐝𝐞𝐥 𝐝𝐨𝐧𝐞” and “𝐦𝐨𝐝𝐞𝐥 𝐝𝐞𝐩𝐥𝐨𝐲𝐞𝐝” is where careers get stuck.
Here’s what building a real end-to-end ML system actually looks like:
𝐖𝐞𝐞𝐤𝐬 𝟏-𝟐: 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐌𝐋
→ Estimate $ business impact from messy data
→ Build production-ready models (not just notebook experiments)
→ MLflow tracking, Optuna tuning, SHAP explainability
𝐖𝐞𝐞𝐤𝐬 𝟑-𝟕: 𝐄𝐧𝐝-𝐭𝐨-𝐄𝐧𝐝 𝐌𝐋𝐎𝐩𝐬
→ Build ML pipelines with Kedro
→ Dockerize and deploy to the cloud
→ CI/CD with GitHub Actions
→ Data drift monitoring and automated retraining
→ Build a full ML platform (not a Streamlit dashboard)
𝐖𝐞𝐞𝐤 𝟖: 𝐂𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐏𝐫𝐨𝐟𝐢𝐥𝐞 𝐔𝐩𝐠𝐫𝐚𝐝𝐞
→ CV that actually gets responses
→ LinkedIn that works for you 24/7
→ Personal website with your deployed project
→ Personal brand strategy that builds authority
This is exactly what my friend Timur is teaching in his 8-week End-to-End ML, MLOps & Career Accelerator.
Timur is a Principal Data Scientist with 8+ years experience. Built 10+ end-to-end ML systems. Delivered $100M+ in revenue with ML. TOP 3 LinkedIn brand in Data Science.
24 𝐋𝐈𝐕𝐄 sessions. Full code access. Lifetime access to all materials.
Only 100 seats available. The next cohort isn’t for another 6 months.
The program starts May 4, 2026.
𝐉𝐨𝐢𝐧 𝐡𝐞𝐫𝐞: https://lnkd.in/dBzcRcQH
Use code 𝐒𝐀𝐈𝟏𝟓𝟎 for $150 off.
♻️ Repost if you know someone stuck between notebook and production