Machine Learning Engineering for Production
(MLOps) Specialization

  • 4 courses
  • >
    Advanced
  • >
    4 months (5 hours/week)
  • >
    Andrew Ng, Robert Crowe, Laurence Moroney, Cristian Bartolomé Arámburu

What you will 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.

Skills you will gain

  • Data Pipelines
  • Model Pipelines
  • Deploy Pipelines
  • Managing Machine Learning Production systems
  • ML Deployment Challenges
  • Project Scoping and Design
  • Concept Drift
  • Model Baseline
  • Human-level Performance (HLP)
  • TensorFlow Extended (TFX)
  • ML Metadata
  • Data transformation
  • Data augmentation
  • Data validation
  • AutoML
  • Precomputing predictions
  • Fairness Indicators
  • Explainable AI
  • Model Performance Analysis
  • TensorFlow Serving
  • Model Monitoring
  • General Data Protection Regulation (GDPR)
  • Model Registries
  • MLOps
  • Machine Learning Engineering for Production

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

The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently.

In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems.  

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. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills.

By the end of this program, you will be ready to: 

  1. Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements.
  2. Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application.
  3. Build data pipelines by gathering, cleaning, and validating datasets.
  4. Implement feature engineering, transformation, and selection with TensorFlow Extended.
  5. Establish data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas.
  6. Apply techniques to manage modeling resources and best serve offline/online inference requests.
  7. Use analytics to address model fairness, explainability issues, and mitigate bottlenecks.
  8. Deliver deployment pipelines for model serving that require different infrastructures.
  9. Apply best practices and progressive delivery techniques to maintain a continuously operating production system.

Syllabus

Course 1: Introduction to Machine Learning in Production

In this course, you will:

– Identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements.

– Establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.

Enroll

Week 1: Overview of the ML Lifecycle and Deployment

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

Week 2: Selecting and Training a Model

  • Identify the key challenges in model development and understand how performance on a small set of disproportionately important examples may be more crucial than performance on the majority of examples

Week 3: Data Definition and Baseline

  • Compare the various types of problems to be solved for structured vs. unstructured data and big vs. small data and understand why label consistency is essential and how you can improve it

Course 2: Machine Learning Data Lifecycle in Production

In this course, you will:

– Build data pipelines by gathering, cleaning, and validating datasets and assessing data quality.

– Implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data.

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

Enroll

Week 1: Collecting, Labeling, and Validating data

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

Week 2: Feature Engineering, Transformation, and Selection

  • Implement feature engineering, transformation, and selection with TensorFlow Extended by encoding structured and unstructured data types and addressing class imbalances

Week 3: Data Journey and Data Storage

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

Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types

  • Combine labeled and unlabeled data to improve ML accuracy and augment data to diversify your training set

Course 3: Machine Learning Modeling Pipelines in Production

This is the third course in the Machine Learning Engineering for Production Specialization.

Enroll

In this course, you will:

  • Build models for different serving environments.
  • Implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests.
  • Use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks.

Course 4: Deploying Machine Learning Models in Production

This is the fourth course in the Machine Learning Engineering for Production Specialization.

Enroll

In this course you will:

  • Deliver deployment pipelines by productionizing, scaling, and monitoring model serving that require different infrastructure.
  • Establish procedures to mitigate model decay and performance drops.
  • Establish best practices and apply progressive delivery techniques to maintain and monitor a continuously operating production system.

Program Instructors

Andrew Ng Instructor

Founder, DeepLearning.AI; Co-founder, Coursera

Robert Crowe Instructor

TensorFlow Developer Engineer, Google

Laurence Moroney Instructor

Lead AI Advocate, Google

Cristian Bartolomé Arámburu Curriculum Developer

Founding Engineer, Pulsar

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    Frequently Asked Questions

    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.

    The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently.

     

    In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems.  

    By the end, you will be ready to:

     

    1. Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements.
    2. Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application.
    3. Build data pipelines by gathering, cleaning, and validating datasets.
    4. Implement feature engineering, transformation, and selection with TensorFlow Extended.
    5. Establish data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas.
    6. Apply techniques to manage modeling resources and best serve offline/online inference requests.
    7. Use analytics to address model fairness, explainability issues, and mitigate bottlenecks.
    8. Deliver deployment pipelines for model serving that require different infrastructures.
    9. Apply best practices and progressive delivery techniques to maintain a continuously operating production system.

    • 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).
    • Learners should be proficient in basic calculus, linear algebra, and statistics.
    • We highly recommend that you complete the updated Deep Learning Specialization before starting this Specialization. 

    1. Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements.
    2. Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application.
    3. Build data pipelines by gathering, cleaning, and validating datasets. Establish data lifecycle by using data lineage and provenance metadata tools.
    4. Apply best practices and progressive delivery techniques to maintain and monitor a continuously operating production system.

    The Machine Learning Engineering for Production (MLOps) Specialization 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. 

    This Specialization consists of four courses. At the rate of 5 hours a week, it typically takes 3 weeks to complete the first course, 4 weeks to complete the second, 6 weeks to complete the third, and 4 weeks to complete the fourth. It typically takes about 4 months to complete the entire Specialization. 

    The Machine Learning Engineering for Production Specialization has been created by Andrew Ng, Robert Crowe, and Laurence Moroney.

     

    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. 

     

    A data scientist and TensorFlow addict, Robert Crowe has a passion for helping developers quickly learn what they need to be productive. Since the very early days, he’s used TensorFlow and is excited about how rapidly it’s evolving to become even better. Before moving to data science, Robert led software engineering teams for large and small companies, focusing on providing clean, elegant solutions for well-defined needs.

     

    Laurence Moroney leads AI Advocacy at Google, with a vision to make AI easy for developers and to widen access to ML careers for everyone. He’s written dozens of programming books, the most recent being ‘AI and ML for Coders’ at O’Reilly. Laurence believes that MOOCs are one of the greatest ways to learn, and is excited to create TensorFlow Specializations with DeepLearning.AI on Coursera. When not working with technology, he’s an active member of the Science Fiction Writers of America, and has authored several sci-fi novels, and comics books and a produced screenplay. Laurence is based in Washington state, where he drinks way too much coffee.

    The Machine Learning Engineering for Production (MLOps) Specialization is made up of 4 courses. 

    You can enroll in the Machine Learning Engineering for Production (MLOps) Specialization on Coursera. You will watch videos and complete assignments on Coursera as well.

    We recommend taking the courses in the prescribed order for a logical and thorough learning experience.

    A Coursera subscription costs $49 / month.

    Yes, Coursera provides financial aid to learners who cannot afford the fee. 

    You can audit the courses in the Machine Learning Engineering for Production (MLOps) Specialization for free.  Note that you will not receive a certificate at the end of the course if you choose to audit it for free instead of purchasing it.

    You will receive a certificate at the end of each course if you pay for the courses and complete the programming 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.

    If you complete all 4 courses in the Specialization, you will also receive an additional certificate showing that you completed the entire Specialization.

    Visit coursera.org/business for more information, to pick up a plan, and to contact Coursera. For each plan, you decide the number of courses every member can enroll in and the collection of courses they can choose from. 

    1. Go to your Coursera account. 
    2. Click on My Purchases and find the relevant course or Specialization.
    3. Click Email Receipt and wait up to 24 hours to receive the receipt. 
    4. You can read more about it here.