Forget becoming a Data Scientist, become a Machine Learning Engineer instead
summary
Argues that becoming a machine learning engineer means taking models into production, not just exploring algorithms; recommends starting with Python and practicing ML algorithms, then adding cloud (AWS/GCP/Azure), big data (Spark), and DevOps (Docker, Kubernetes/Kubeflow). Describes an end‑to‑end workflow: prepare data, train at scale, package models as web services, register containers, deploy, auto‑scale, monitor and version — and suggests hands‑on projects that train, evaluate and successfully deploy models to the cloud.
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chapters (15)
- 0:00Introduction and welcome
- 3:12What is machine learning
- 7:03Model building vs production
- 10:41Skills for ML engineers
- 11:24Machine learning fundamentals
- 14:54Cloud computing essentials
- 17:19Big data and distributed computing
- 19:46Deploying models in production
- 21:30DevOps for ML engineers
- 23:32Learning path for ML engineers
- 27:44Resources and next steps
- 33:23Projects and hands-on learning
- 35:07Data engineers transitioning to ML
- 37:13AutoML and future trends
- 40:03Q&A and closing remarks
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