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

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

Was Sie lernen werden

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.

Kompetenzen, die Sie erwerben

Über dieses Spezialisierung

56.277 kürzliche Aufrufe

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.

Praktisches Lernprojekt

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

Zertifikat zur Vorlage

Erhalten Sie nach Abschluss ein Zertifikat

Kurse, die komplett online stattfinden

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Coursera-Labore

Enthält praktische Lernprojekte.

Hier finden Sie weitere Informationen zu Coursera-Laboren

Flexibler Zeitplan

Festlegen und Einhalten flexibler Termine.

Stufe „Fortgeschritten“

Etwa 4 Monate bis zum Abschluss

Empfohlenes Lerntempo: 5 Stunden/Woche

Englisch

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Zertifikat zur Vorlage

Erhalten Sie nach Abschluss ein Zertifikat

Kurse, die komplett online stattfinden

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Coursera-Labore

Enthält praktische Lernprojekte.

Hier finden Sie weitere Informationen zu Coursera-Laboren

Flexibler Zeitplan

Festlegen und Einhalten flexibler Termine.

Stufe „Fortgeschritten“

Etwa 4 Monate bis zum Abschluss

Empfohlenes Lerntempo: 5 Stunden/Woche

Englisch

Könnte Ihr Unternehmen von Mitarbeiterweiterbildungen für gefragte Kompetenzen profitieren?

Probieren Sie Coursera for Business aus

Erfahren Sie, wie Mitarbeiter führender Unternehmen gefragte Kompetenzen erwerben.

Weitere Informationen zu Coursera für Unternehmen

So funktioniert die Spezialisierung

Kurse absolvieren

Eine Coursera-Spezialisierung ist eine Reihe von Kursen, in denen Sie eine Kompetenz erwerben. Um zu beginnen, melden Sie sich direkt für die Spezialisierung an oder überprüfen Sie deren Kurse und wählen Sie denjenigen Kurs aus, mit dem Sie beginnen möchten. Wenn Sie einen Kurs abonnieren, der Bestandteil einer Spezialisierung ist, abonnieren Sie automatisch die gesamte Spezialisierung Es ist in Ordnung, wenn Sie nur einen Kurs absolvieren möchten — Sie können Ihren Lernprozess jederzeit unterbrechen oder Ihr Abonnement kündigen. Gehen Sie zu Ihrem Lernender-Dashboard, um Ihre Kursanmeldungen und Ihren Fortschritt zu verfolgen.

Praxisprojekt

Jede Spezialisierung umfasst ein Praxisprojekt. Sie müssen das Projekt/die Projekte erfolgreich abschließen, um die Spezialisierung abzuschließen und Ihr Zertifikat zu erwerben. Wenn die Spezialisierung einen separaten Kurs für das Praxisprojekt umfasst, müssen Sie zunächst alle anderen Kurse abschließen, bevor Sie damit beginnen können.

Zertifikat erwerben

Wenn Sie alle Kurse und das Praxisprojekt abgeschlossen haben, erhalten Sie ein Zertifikat, dass Sie für potenzielle Arbeitgeber und Ihr berufliches Netzwerk freigeben können.

Es gibt 4 Kurse in dieser Spezialisierung

Kurs1

Kurs 1

Introduction to Machine Learning in Production

4.8

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2,329 Bewertungen

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

Kurs2

Kurs 2

Machine Learning Data Lifecycle in Production

4.3

Sterne

618 Bewertungen

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

Kurs3

Kurs 3

Machine Learning Modeling Pipelines in Production

4.4

Sterne

315 Bewertungen

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

Kurs4

Kurs 4

Deploying Machine Learning Models in Production

4.6

Sterne

242 Bewertungen

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

Dozenten

von

deeplearning.ai

Häufig gestellte Fragen

Wie erfolgen Rückerstattungen?
Wenn Sie ein Abonnement abgeschlossen haben, erhalten Sie eine 7-tägige, kostenlose Testversion, die Sie gebührenfrei wieder kündigen können. Danach gewähren wir keine Rückerstattungen mehr, aber Sie können Ihr Abonnement jederzeit kündigen. Lesen Sie unsere vollständige Rückerstattungsrichtlinie.
Kann ich mich auch nur für einen Kurs anmelden?
Ja! Um loszulegen, klicken Sie auf die Kurskarte, die Sie interessiert, und melden Sie sich an. Sie können sich anmelden und den Kurs absolvieren, um ein teilbares Zertifikat zu erwerben, oder Sie können als Gast teilnehmen, um die Kursmaterialien gratis einzusehen. Wenn Sie einen Kurs abonnieren, der Teil einer Spezialisierung ist, abonnieren Sie automatisch die gesamte Spezialisierung. Auf Ihrem Kursteilnehmer-Dashboard können Sie Ihren Fortschritt verfolgen.
Ist finanzielle Unterstützung möglich?
Ja. Für ausgewählte Lernprogramme können Sie finanzielle Unterstützung oder ein Stipendium beantragen, wenn Sie sich die Anmeldegebühr nicht leisten können. Wenn für das von Ihnen gewählte Lernprogramm finanzielle Unterstützung oder ein Stipendium verfügbar ist, wird auf der Beschreibungsseite ein Link zu einem solchen Antrag angezeigt.
Kann ich kostenlos an diesem Kurs teilnehmen?
Wenn Sie sich für den Kurs anmelden, erhalten Sie Zugriff auf alle Kurse in der Spezialisierung und Sie erwerben ein Zertifikat, wenn Sie die Arbeit abschließen. Wenn Sie lediglich den Kursinhalt lesen und anzeigen möchten, können Sie kostenlos als Gast an dem Kurs teilnehmen. Wenn Sie die Gebühr nicht bezahlen können, haben Sie die Möglichkeit, finanzielle Hilfe zu beantragen.
Findet dieser Kurs wirklich ausschließlich online statt? Muss ich zu irgendwelchen Sitzungen persönlich erscheinen?
Dieser Kurs findet ausschließlich online statt, Sie müssen also zu keiner Sitzung persönlich erscheinen. Sie können jederzeit und überall über das Netz oder Ihr Mobilgerät auf Ihre Vorträge, Lektüren und Aufgaben zugreifen.
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.
Erhalte ich akademische Leistungspunkte für den Abschluss der Spezialisierung?
No.

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Spezialisierung Machine Learning Engineering for Production (MLOps)

Was Sie lernen werden

Kompetenzen, die Sie erwerben

Über dieses Spezialisierung

Praktisches Lernprojekt