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ML Engineer

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April 29, 2024 5 minute read

Machine learning engineers are responsible for designing, developing, and deploying machine learning models. They work with data scientists and other engineers to turn machine learning models into real-world applications.

What Does a Machine Learning Engineer Do?

Machine learning engineers build and maintain machine learning models. They also work with data scientists and other engineers to turn machine learning models into real-world applications. Some of the specific tasks that machine learning engineers may perform include:

  • Collecting and cleaning data
  • Building and training machine learning models
  • Deploying and monitoring machine learning models
  • Working with data scientists and other engineers to turn machine learning models into real-world applications

Machine learning engineers typically have a strong background in computer science and mathematics. They also have experience with machine learning algorithms and techniques.

What is the Career Path to Becoming a Machine Learning Engineer?

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Salaries for ML Engineer

City
Median
New York
$209,000
San Francisco
$223,000
Seattle
$163,000
See all salaries
City
Median
New York
$209,000
San Francisco
$223,000
Seattle
$163,000
Austin
$203,000
Toronto
$147,000
London
£95,000
Paris
€55,000
Berlin
€79,000
Tel Aviv
₪381,000
Singapore
S$120,000
Beijing
¥718,000
Shanghai
¥203,000
Bengalaru
₹568,000
Delhi
₹2,419,000
Bars indicate relevance. All salaries presented are estimates. Completion of this course does not guarantee or imply job placement or career outcomes.

Reading list

We've selected some books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in ML Engineer.
Study guide for the AWS Certified Bedrock Developer Associate exam. It covers all the topics included in the exam and provides practice questions and mock exams. It valuable resource for anyone who wants to prepare for this certification.
Focuses on the use of deep learning techniques for video generation, exploring the latest advances and challenges in this area.
Explores the use of Amazon Bedrock for artificial intelligence (AI) development. It covers topics such as machine learning, deep learning, and natural language processing. It valuable resource for developers who want to build and deploy AI applications on Amazon Bedrock.
Examines the applications of video generation in the healthcare industry, discussing the creation of medical simulations, surgical planning tools, and personalized patient experiences.
Takes a practical approach to using Amazon Bedrock for cloud computing. It covers topics such as instance types, storage options, and networking. It valuable resource for developers and system administrators who want to learn how to use Amazon Bedrock effectively.
Explores the use of video generation for industrial applications, discussing the creation of virtual prototypes, assembly instructions, and safety training materials.
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