DeepLearning.AI
Machine Learning Engineering for Production (MLOps) Specialization
DeepLearning.AI

Machine Learning Engineering for Production (MLOps) Specialization

Become a Machine Learning expert. Productionize your machine learning knowledge and expand your production engineering capabilities.

Taught in English

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Andrew Ng
Cristian Bartolomé Arámburu
Laurence Moroney

Instructors: Andrew Ng

Top Instructor

68,677 already enrolled

Specialization - 4 course series

Get in-depth knowledge of a subject

4.6

(3,375 reviews)

Advanced level

Recommended experience

2 months at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements.

  • Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application.

  • Build data pipelines by gathering, cleaning, and validating datasets. Establish data lifecycle by using data lineage and provenance metadata tools.

  • Apply best practices and progressive delivery techniques to maintain and monitor a continuously operating production system.

Details to know

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Specialization - 4 course series

Get in-depth knowledge of a subject

4.6

(3,375 reviews)

Advanced level

Recommended experience

2 months at 10 hours a week
Flexible schedule
Learn at your own pace

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Specialization - 4 course series

Introduction to Machine Learning in Production

Course 111 hours4.8 (2,813 ratings)

What you'll learn

  • Identify the key components of the ML lifecycle and pipeline and compare the ML modeling iterative cycle with the ML product deployment cycle.

  • Understand how performance on a small set of disproportionately important examples may be more crucial than performance on the majority of examples.

  • Solve problems for structured, unstructured, small, and big data. Understand why label consistency is essential and how you can improve it.

Skills you'll gain

Category: Concept Drift
Category: ML Deployment Challenges
Category: Human-level Performance (HLP)
Category: Project Scoping and Design
Category: Model baseline

Machine Learning Data Lifecycle in Production

Course 221 hours4.3 (814 ratings)

What you'll learn

  •  Identify responsible data collection for building a fair ML production system.

  • Implement feature engineering, transformation, and selection with TensorFlow Extended

  • Understand the data journey over a production system’s lifecycle and leverage ML metadata and enterprise schemas to address quickly evolving data.

Skills you'll gain

Category: Convolutional Neural Network
Category: Data Validation
Category: ML Metadata
Category: Data transformation
Category: TensorFlow Extended (TFX)

Machine Learning Modeling Pipelines in Production

Course 323 hours4.4 (410 ratings)

What you'll learn

  • Apply techniques to manage modeling resources and best serve batch and real-time inference requests.

  • Use analytics to address model fairness, explainability issues, and mitigate bottlenecks.

Skills you'll gain

Category: Model Performance Analysis
Category: Precomputing Predictions
Category: Fairness Indicators
Category: Explainable AI
Category: automl

Deploying Machine Learning Models in Production

Course 432 hours4.5 (321 ratings)

What you'll learn

Skills you'll gain

Category: Model Registries
Category: TensorFlow Serving
Category: Generate Data Protection Regulation (GDPR)
Category: Model Monitoring
Category: Machine Learning Operations (MLOps)

Instructors

Laurence Moroney
DeepLearning.AI
15 Courses485,207 learners

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DeepLearning.AI

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