Managing Machine Learning Production SystemsDeployment PipelinesModel PipelinesData PipelinesMachine Learning Engineering for ProductionHuman-level Performance (HLP)Concept DriftModel baselineProject Scoping and DesignML Deployment ChallengesML MetadataConvolutional Neural Network
Специализация Machine Learning Engineering for Production (MLOps)
Become a Machine Learning expert. Productionize your machine learning knowledge and expand your production engineering capabilities.
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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
Специализация: общие сведения
Недавно просмотрено: 105 655
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: 4 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: 4 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
Introduction to Machine Learning in Production
Оценки: 594
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.
Machine Learning Data Lifecycle in Production
Оценки: 128
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.
Machine Learning Modeling Pipelines in Production
Оценки: 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.
Deploying Machine Learning Models in Production
In the fourth course of Machine Learning Engineering for Production Specialization, you will deliver deployment pipelines by productionizing, scaling, and monitoring model serving that require different infrastructure; establish procedures to minimize model decay and performance drops; and apply progressive delivery techniques to maintain and monitor a continuously operating production system.
от партнера
deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Часто задаваемые вопросы
Получу ли я зачеты в университете за прохождение специализации?
Can I just enroll in a single course?
Можно ли зарегистрироваться только на один курс?
Can I take the course for free?
Могу ли я пройти курс бесплатно?
Действительно ли это полностью дистанционный курс? Нужно ли мне посещать какие-либо занятия лично?
What is the Machine Learning Engineering for Production (MLOps) Specialization about?
What will I be able to do after completing the Machine Learning Engineering in Production (MLOps) Specialization?
What background knowledge is necessary for the Machine Learning Engineering for Production (MLOps) Specialization?
Who is the Machine Learning Engineering for Production (MLOps) Specialization for?
How long does it take to complete the Machine Learning Engineering for Production (MLOps) Specialization?
Who is the Machine Learning Engineering for Production (MLOps) Specialization by?
Is this a standalone course or a Specialization?
Do I need to take the courses in a specific order?
Can I apply for financial aid?
Can I audit the Machine Learning Engineering for Production (MLOps) Specialization?
How do I get a receipt to get this reimbursed by my employer?
I want to purchase this Specialization for my employees. How can I do that?
Will I earn university credit for completing the Specialization?
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