← All drillsLearn Fast: The Deep Learning Project Life Cycle
What you'll be able to do- ✓Scope a deep-learning project end to end
- ✓Plan the data, modeling, and deployment phases
- ✓Monitor models once they are in production
Plan and run a deep-learning project from scoping to monitoring — the end-to-end lifecycle every production ML team follows, so projects ship instead of stalling in notebooks.
⌁ The MLOps lifecycle behind production ML at Google (Rules of ML, Vertex AI MLOps) and AWS SageMaker pipelines, and the "Software 2.0" shift popularized at Tesla.
Start this internshipCreate an account to unlock the 4 sections, the workbench, and AskThili.
BeginSections
1. The Deep Learning Project Life Cycle
🔒 locked2. Stakeholders and Software 2.0
🔒 locked3. Scoping and Data Collection
🔒 locked4. Model Development, Deployment and Monitoring
🔒 lockedDig deeper
🔗Software 2.0 (Andrej Karpathy, 2017)
article📄Hidden Technical Debt in Machine Learning Systems (Sculley et al., 2015)
paper🔗Rules of Machine Learning — Google
articlePart of these learning paths
I want to become an ML engineer who ships and operates models in production
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