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

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Специализация Machine Learning Engineering for Production (MLOps)deeplearning.ai

Чему вы научитесь

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.

Приобретаемые навыки

Managing Machine Learning Production Systems
Deployment Pipelines
Model Pipelines
Data Pipelines
Machine Learning Engineering for Production
Human-level Performance (HLP)
Concept Drift
Model baseline
Project Scoping and Design
ML Deployment Challenges
ML Metadata
Convolutional Neural Network

Специализация: общие сведения

Недавно просмотрено: 96 251

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. 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. 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.

Проект прикладного обучения

By the end, you'll 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

Сертификат, ссылками на который можно делиться с другими людьми

Получите сертификат по завершении

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.

Гибкий график

Установите гибкие сроки сдачи заданий.

Продвинутый уровень

• Some knowledge of AI / deep learning • Intermediate skills in Python • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)

Ориентировочное время на прохождение: 4 месяца

Рекомендуемый темп: 6 ч/неделю

Английский

Субтитры: Английский

Сертификат, ссылками на который можно делиться с другими людьми

Получите сертификат по завершении

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.

Гибкий график

Установите гибкие сроки сдачи заданий.

Продвинутый уровень

• Some knowledge of AI / deep learning • Intermediate skills in Python • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)

Ориентировочное время на прохождение: 4 месяца

Рекомендуемый темп: 6 ч/неделю

Английский

Субтитры: Английский

О специализации

Пройти курсы

Специализация Coursera — это серия курсов, помогающих в совершенстве овладеть определенным навыком. Можно сразу записаться на специализацию или просмотреть курсы, из которых она состоит и выбрать тот, с которого вы хотите начать. Подписываясь на курс, который входит в специализацию, вы автоматически подписываетесь на всю специализацию. Можно завершить всего один курс, а потом сделать паузу в обучении или в любой момент отменить подписку. Отслеживайте свои курсы и прогресс на панели управления учащегося.

Практический проект

В каждой специализации есть практический проект, который нужно успешно выполнить, чтобы завершить специализацию и получить сертификат. Если для практического проекта в специализации предусмотрен отдельный курс, прежде чем начать его, необходимо завершить все остальные курсы.

Получите сертификат

Когда вы пройдете все курсы и завершите практический проект, вы получите сертификат, которым можно поделиться с потенциальными работодателями и коллегами.

Специализация включает несколько курсов: 4

Курс1

Курс 1

Introduction to Machine Learning in Production

4.8

звезд

Оценки: 1,265

•

Рецензии: 225

In the first course of Machine Learning Engineering for Production Specialization, 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; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.

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. Week 1: Overview of the ML Lifecycle and Deployment Week 2: Selecting and Training a Model Week 3: Data Definition and Baseline

Курс2

Курс 2

Machine Learning Data Lifecycle in Production

4.4

звезд

Оценки: 329

•

Рецензии: 63

In the second course of Machine Learning Engineering for Production Specialization, 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; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas.

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. Week 1: Collecting, Labeling, and Validating data Week 2: Feature Engineering, Transformation, and Selection Week 3: Data Journey and Data Storage Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types

Курс3

Курс 3

Machine Learning Modeling Pipelines in Production

4.6

звезд

Оценки: 164

•

Рецензии: 27

In the third course of Machine Learning Engineering for Production Specialization, 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; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks.

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. Week 1: Neural Architecture Search Week 2: Model Resource Management Techniques Week 3: High-Performance Modeling Week 4: Model Analysis Week 5: Interpretability

Курс4

Курс 4

Deploying Machine Learning Models in Production

4.7

звезд

Оценки: 102

•

Рецензии: 15

In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ML models and make them available to end-users. You will build scalable and reliable hardware infrastructure to deliver inference requests both in real-time and batch depending on the use case. You will also implement workflow automation and progressive delivery that complies with current MLOps practices to keep your production system running. Additionally, you will continuously monitor your system to detect model decay, remediate performance drops, and avoid system failures so it can continuously operate at all times.

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. Week 1: Model Serving Introduction Week 2: Model Serving Patterns and Infrastructures Week 3: Model Management and Delivery Week 4: Model Monitoring and Logging

Преподаватели

от партнера

deeplearning.ai

DeepLearning.AI is an education technology company that develops a global community of AI talent.

Часто задаваемые вопросы

Какие правила возврата средств?
Когда вы оформите подписку, начнется семидневный бесплатный пробный период, в течение которого подписку можно отменить без штрафа. По истечении этого срока вы не сможете вернуть средства, но сможете отменить подписку в любой момент. Ознакомьтесь с нашей политикой возврата средств.
Можно ли зарегистрироваться только на один курс?
Да! Чтобы начать, нажмите карточку интересующего вас курса и зарегистрируйтесь. Зарегистрировавшись, вы можете пройти курс и получить сертификат, ссылкой на который можно делиться с другими людьми. Просто ознакомиться с содержанием курса можно бесплатно. При подписке на курс, входящий в специализацию, вы автоматически подписываетесь на всю специализацию. Ход учебы можно отслеживать в панели управления учащегося.
Можно ли получить финансовую помощь?
Да, Coursera предоставляет финансовую помощь учащимся, которые не могут оплатить обучение. Чтобы подать заявление, перейдите по ссылке "Финансовая помощь" слева под кнопкой "Зарегистрироваться". Заполните форму заявления. Если его примут, вы получите уведомление. Обратите внимание: этот шаг необходимо выполнить для каждого курса специализации, в том числе для дипломного проекта. Подробнее
Могу ли я пройти курс бесплатно?
Когда вы регистрируетесь на курс, то можете получить доступ ко всем курсам в специализации. Кроме того, вы получаете сертификат о прохождении курса.Просто ознакомиться с содержанием курса можно бесплатно. Если стоимость обучения для вас слишком велика, вы можете подать заявку на финансовую помощь.
Действительно ли это полностью дистанционный курс? Нужно ли мне посещать какие-либо занятия лично?
Это полностью дистанционный курс, потому вам не нужно ничего посещать. Все лекции, материалы для самостоятельного изучения и задания доступны всегда и везде по Интернету и с мобильных устройств.
What is machine learning engineering for production? Why is it relevant?
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.
What is the Machine Learning Engineering for Production (MLOps) Specialization about?
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.
What will I be able to do after completing the Machine Learning Engineering in Production (MLOps) Specialization?
By the end of the Machine Learning Engineering for Production (MLOps) Specialization, 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 by productionizing model serving with different infrastructures.
Apply best practices and progressive delivery techniques to maintain a continuously operating production system.
What background knowledge is necessary for the Machine Learning Engineering for Production (MLOps) Specialization?
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.
Who is the Machine Learning Engineering for Production (MLOps) Specialization for?
The Machine Learning Engineering for Production 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.
How long does it take to complete the Machine Learning Engineering for Production (MLOps) Specialization?
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.
Who is the Machine Learning Engineering for Production (MLOps) Specialization by?
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.
Is this a standalone course or a Specialization?
The Machine Learning Engineering for Production (MLOps) Specialization is made up of 4 courses.
Do I need to take the courses in a specific order?
We recommend taking the courses in the prescribed order for a logical and thorough learning experience.
Can I apply for financial aid?
Yes, Coursera provides financial aid to learners who cannot afford the fee.
Can I audit the Machine Learning Engineering for Production (MLOps) Specialization?
You can audit the courses in the Machine Learning Engineering for Production 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.
How do I get a receipt to get this reimbursed by my employer?
Go to your Coursera account.
Click on My Purchases and find the relevant course or Specialization.
Click Email Receipt and wait up to 24 hours to receive the receipt.
You can read more about it here.
I want to purchase this Specialization for my employees. How can I do that?
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.
Получу ли я зачеты в университете за прохождение специализации?
No.

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