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

4.7

étoiles

987 évaluations

Andrew Ng +3 enseignants de plus
Enseignants de premier plan

18 451 déjà inscrits

Offert par

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

Ce que vous allez apprendre

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.

Compétences que vous acquerrez

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

À propos de ce Spécialisation

93 493 consultations récentes

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.

Projet d'apprentissage appliqué

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

Certificat partageable

Obtenez un Certificat lorsque vous terminez

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Planning flexible

Définissez et respectez des dates limites flexibles.

Niveau avancé

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

Approximativement 4 mois pour terminer

Rythme recommandé de 6 heures/semaine

Anglais

Sous-titres : Anglais

Certificat partageable

Obtenez un Certificat lorsque vous terminez

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Planning flexible

Définissez et respectez des dates limites flexibles.

Niveau avancé

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

Approximativement 4 mois pour terminer

Rythme recommandé de 6 heures/semaine

Anglais

Sous-titres : Anglais

Comment fonctionne la Spécialisation

Suivez les cours

Une Spécialisation Coursera est une série de cours axés sur la maîtrise d'une compétence. Pour commencer, inscrivez-vous directement à la Spécialisation ou passez en revue ses cours et choisissez celui par lequel vous souhaitez commencer. Lorsque vous vous abonnez à un cours faisant partie d'une Spécialisation, vous êtes automatiquement abonné(e) à la Spécialisation complète. Il est possible de terminer seulement un cours : vous pouvez suspendre votre formation ou résilier votre abonnement à tout moment. Rendez-vous sur votre tableau de bord d'étudiant pour suivre vos inscriptions aux cours et vos progrès.

Projet pratique

Chaque Spécialisation inclut un projet pratique. Vous devez réussir le(s) projet(s) pour terminer la Spécialisation et obtenir votre Certificat. Si la Spécialisation inclut un cours dédié au projet pratique, vous devrez terminer tous les autres cours avant de pouvoir le commencer.

Obtenir un Certificat

Lorsque vous aurez terminé tous les cours et le projet pratique, vous obtiendrez un Certificat que vous pourrez partager avec des employeurs éventuels et votre réseau professionnel.

Cette Spécialisation compte 4 cours

Cours1

Cours 1

Introduction to Machine Learning in Production

4.8

étoiles

875 évaluations

•

172 avis

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

Cours2

Cours 2

Machine Learning Data Lifecycle in Production

4.5

étoiles

220 évaluations

•

37 avis

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

Cours3

Cours 3

Machine Learning Modeling Pipelines in Production

4.6

étoiles

91 évaluations

•

18 avis

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

Cours4

Cours 4

Deploying Machine Learning Models in Production

4.8

étoiles

41 évaluations

•

11 avis

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

Enseignants

Offert par

deeplearning.ai

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

Foire Aux Questions

Quelle est la politique de remboursement ?
Si vous vous abonnez, vous bénéficiez d'une période d'essai gratuite de 7 jours, durant laquelle vous pouvez annuler votre abonnement sans pénalité. Ensuite, nous n'accordons plus de remboursements, mais vous pouvez annuler votre abonnement à tout instant. Consultez notre politique de remboursement complète.
Puis-je m'inscrire à un seul cours ?
Oui ! Pour commencer, cliquez sur la carte du cours qui vous intéresse et inscrivez-vous. Vous pouvez vous inscrire et terminer le cours pour obtenir un Certificat partageable, ou vous pouvez accéder au cours en auditeur libre afin d'en visualiser gratuitement le contenu. Si vous vous abonnez à un cours faisant partie d'une Spécialisation, vous êtes automatiquement abonné(e) à la Spécialisation complète. Visitez votre tableau de bord d'étudiant(e) pour suivre vos progrès.
Une aide financière est-elle possible ?
Oui, Coursera offre une Aide Financière aux étudiants qui n'ont pas les moyens d'acquitter les frais. Demandez-la en cliquant sur le lien Aide Financière sous le bouton S'inscrire situé à gauche. Vous devrez remplir un formulaire de demande et vous serez averti(e) si elle est acceptée. Vous devrez répéter cette procédure pour chaque cours de la Spécialisation, y compris pour le Projet Final. En savoir plus.
Puis-je suivre le cours gratuitement ?
Si vous vous inscrivez au cours, vous pouvez accéder à tous les cours de la Spécialisation et obtenir un Certificat lorsque vous terminez le travail. Si vous souhaitez seulement lire et examiner le contenu du cours, vous pouvez accéder gratuitement au cours en auditeur libre. Si vous n'avez pas les moyens d'acquitter les frais, vous pouvez faire une demande d'Aide Financière.
Ce cours est-il vraiment accessible en ligne à 100 % ? Dois-je assister à certaines activités en personne ?
Ce cours est entièrement en ligne : vous n'avez donc pas besoin de vous présenter physiquement dans une salle de classe. Vous pouvez accéder à vos vidéos de cours, lectures et devoirs en tout temps et en tout lieu, par l'intermédiaire du Web ou de votre appareil mobile.
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.
Puis-je obtenir des crédits universitaires si je réussis la Spécialisation ?
No.

D'autres questions ? Visitez le Centre d'Aide pour les Etudiants.