DeepLearning.AI
Introduction to Machine Learning in Production
DeepLearning.AI

Introduction to Machine Learning in Production

Andrew Ng
Cristian Bartolomé Arámburu

Instructors: Andrew Ng

Top Instructor

76,336 already enrolled

Course

Gain insight into a topic and learn the fundamentals

4.8

(2,337 reviews)

Advanced level

Recommended experience

11 hours (approximately)
Flexible schedule
Learn at your own pace

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

Details to know

Shareable Certificate

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Quizzes and assessments

1 quiz, 5 assessments

English
Subtitles: English, French

Course

Gain insight into a topic and learn the fundamentals

4.8

(2,337 reviews)

Advanced level

Recommended experience

11 hours (approximately)
Flexible schedule
Learn at your own pace

Build your subject-matter expertise

This course is part of the Machine Learning Engineering for Production (MLOps) Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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There are 3 modules in this course

This week covers a quick introduction to machine learning production systems focusing on their requirements and challenges. Next, the week focuses on deploying production systems and what is needed to do so robustly while facing constantly changing data.

What's included

9 videos2 readings

This week is about model strategies and key challenges in model development. It covers error analysis and strategies to work with different data types. It also addresses how to cope with class imbalance and highly skewed data sets.

What's included

16 videos1 reading

This week is all about working with different data types and ensuring label consistency for classification problems. This leads to establishing a performance baseline for your model and discussing strategies to improve it given your time and resources constraints.

What's included

16 videos3 readings1 quiz

Instructors

Instructor ratings
5.0 (807 ratings)
Andrew Ng

Top Instructor

DeepLearning.AI
38 Courses6,297,979 learners

Offered by

DeepLearning.AI
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4.8

2,337 reviews

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5

Reviewed on Dec 23, 2022

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Reviewed on Jan 7, 2023

Recommended if you're interested in Data Science

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