Question 1

What is machine learning engineering for production? Why is it relevant?

Accepted Answer

Machine learning engineering for production refers to the tools, techniques, and practical experiences that transform theoretical ML knowledge into a production-ready skillset.

Effectively deploying machine learning models requires competencies more commonly found in technical fields such as software engineering and DevOps. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles.

Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. With machine learning engineering for production, you can turn your knowledge of machine learning into production-ready skills.

Question 2

What is the Machine Learning in Production Course about?

Accepted Answer

The Machine Learning in Production course covers how to conceptualize integrated systems that continuously operate in production as well as solve common challenges unique to the production environment. In striking contrast with standard machine learning modeling, production systems need to handle evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance.
In this Machine Learning in Production course, you will build intuition about designing a production ML system end-to-end: project scoping, data needs, modeling strategies, and deployment patterns and technologies. You will learn strategies for addressing common challenges in production like establishing a model baseline, addressing concept drift, and performing error analysis. You’ll follow a framework for developing, deploying, and continuously improving a productionized ML application.

Question 3

What will I be able to do after completing the Machine Learning in Production course?

Accepted Answer

By the end, you will be ready to:

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.

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

Establish data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas.

Apply techniques to manage modeling resources and best serve offline/online inference requests.

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

Deliver deployment pipelines for model serving that require different infrastructures.

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

Question 4

What background knowledge is necessary for the Machine Learning in Production course?

Accepted Answer

Learners should have a working knowledge of AI and deep learning.

Learners should have intermediate Python skills and experience with any deep learning framework (TensorFlow, Keras, or PyTorch).

We highly recommend that you complete the updated Deep Learning Specialization before starting this course.

Question 5

What will I learn in the Machine Learning in Production course?

Accepted Answer

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.

Question 6

Who is the Machine Learning in Production course for?

Accepted Answer

<text variant="body1">The Machine Learning in Production course is for early-career machine learning practitioners or software engineers looking to gain practical knowledge of how to formulate a reproducible, traceable, and verifiable machine learning project for production.

Question 7

How long does it take to complete the Machine Learning in Production course?

Accepted Answer

At the rate of 5 hours a week, it typically takes 3 weeks to complete this course.

Question 8

Who is the Machine Learning in Production course by?

Accepted Answer

The Machine Learning in Production course has been created by Andrew Ng. Andrew Ng is Founder of DeepLearning.AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University. As a pioneer in machine learning and online education, Dr. Ng has changed countless lives through his work in AI, authoring or co-authoring over 100 research papers in machine learning, robotics, and related fields. Previously, he was chief scientist at Baidu, the founding lead of the Google Brain team, and the co-founder of Coursera – the world’s largest MOOC platform.

Question 9

Is this a standalone course or a Specialization?

Accepted Answer

The course is a standalone course.

Question 10

How do I get a receipt to get this reimbursed by my employer?

Accepted Answer

Question 11

I want to purchase this course for my employees. How can I do that?

Accepted Answer

Question 12

Will I earn university credit for completing the Specialization?

Accepted Answer

No.

Question 13

Will I receive a certificate at the end of the Specialization?

Accepted Answer

<text variant="body1">You will receive a certificate at the end of the each course if you pay for the course and complete the assignments. There is a limit of 180 days of certificate eligibility, after which you must re-purchase the course to obtain a certificate. If you audit the course for free, you will not receive a certificate.

Question 14

When will I have access to the lectures and assignments?

Accepted Answer

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course.  You can try a Free Trial instead, or apply for Financial Aid. The course may offer &#x27;Full Course, No Certificate&#x27; instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

Question 15

What will I get if I subscribe to this Specialization?

Accepted Answer

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Question 16

Is financial aid available?

Accepted Answer

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.