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Google Cloud Training
Ce cours présente les outils et les bonnes pratiques MLOps pour déployer, évaluer, surveiller et exploiter des systèmes de ML en production sur Google Cloud. Le MLOps est une discipline axée sur le déploiement, le test, la surveillance et l'automatisation des systèmes de ML en production.
Les participants s'entraîneront à utiliser l'ingestion en flux continu de Vertex AI Feature Store au niveau du SDK.
Register for this course and see more details by visiting:
OpenCourser.com/course/1mgtjf/mlops
What's inside
Syllabus
Bienvenue dans le cours "Machine Learning Operations (MLOps) with Vertex AI: Manage Features"
Présentation du cours.
Présentation de Vertex AI Feature Store
Read more
Syllabus
Bienvenue dans le cours "Machine Learning Operations (MLOps) with Vertex AI: Manage Features"
Présentation du cours.
Présentation de Vertex AI Feature Store
Read more
Vertex AI et ses fonctionnalités MLOps Principales difficultés liées aux données et solutions potentielles pour les atténuer.
"Machine Learning Operations (MLOps) with Vertex AI: Manage Features" en détail
Principales fonctionnalités de Vertex AI Feature Store.
Résumé
Résumé du cours.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Enseigne les meilleures pratiques MLOps, qui sont standard dans l'industrie
Développe des fonctionnalités et des outils essentiels pour le MLOps
Enseigné par Google Cloud Training, reconnu pour son expertise en MLOps
Approfondit les problèmes liés aux données et propose des solutions pour les atténuer
Inclus des exercices pratiques sur l'ingestion en continu des fonctionnalités de Vertex AI
Requiert une expérience préalable en MLOps pour tirer pleinement parti du cours
Save this course
Save MLOps with Vertex AI: Manage Features - Français to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in MLOps with Vertex AI: Manage Features - Français with these
activities:
Suivez un tutoriel sur les meilleures pratiques MLOps
Show steps
Suivez un tutoriel complet sur les meilleures pratiques MLOps pour approfondir votre compréhension des techniques de déploiement, d'évaluation et de surveillance des systèmes de ML.
Browse courses on
MLOps
Show steps
-
Trouvez un tutoriel réputé sur les meilleures pratiques MLOps.
-
Suivez attentivement le tutoriel et prenez des notes.
-
Appliquez les techniques apprises dans vos projets MLOps personnels.
Show all one activities
Suivez un tutoriel sur les meilleures pratiques MLOps
Show steps
Suivez un tutoriel complet sur les meilleures pratiques MLOps pour approfondir votre compréhension des techniques de déploiement, d'évaluation et de surveillance des systèmes de ML.
Browse courses on
MLOps
Show steps
Show steps
Show steps
- Trouvez un tutoriel réputé sur les meilleures pratiques MLOps.
- Suivez attentivement le tutoriel et prenez des notes.
- Appliquez les techniques apprises dans vos projets MLOps personnels.
Show all one activities
Career center
Learners who complete MLOps with Vertex AI: Manage Features - Français will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use statistical methods and machine learning to analyze data. They develop predictive models and algorithms from ML to interpret data and extract meaningful information. The course's focus on managing features, a core component of predictive modeling, can help Data Scientists enhance their models and improve accuracy.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and maintain ML systems. They work closely with Data Scientists to bring ML models into production. This course aligns with the role of Machine Learning Engineers as it delves into the practical aspects of deploying and managing ML systems, including feature engineering and monitoring.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to provide insights and make data-driven decisions. The course's focus on feature management and data quality can help Data Analysts improve the accuracy and reliability of their analysis and decision-making process.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines and infrastructure. They ensure the availability, reliability, and security of data. This course may assist Data Engineers in understanding the principles of feature engineering and data quality management, which are essential for building robust data pipelines.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. The course's focus on deploying and managing ML systems may provide Software Engineers with a foundational understanding of ML operations, which can be beneficial for developing ML-based applications.
Product Manager
Product Manager
Product Managers lead the development and management of products. They work closely with engineers and other stakeholders to define product requirements and ensure customer satisfaction. This course may provide Product Managers with an understanding of the challenges and best practices in deploying and managing ML systems, enabling them to make informed decisions.
Business Analyst
Business Analyst
Business Analysts analyze business needs and develop solutions to improve efficiency and performance. This course may provide Business Analysts with an understanding of the role of ML in solving business problems, enabling them to effectively collaborate with technical teams and evaluate ML solutions.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They use statistical methods to solve problems and make informed decisions. This course may provide Statisticians with a foundational understanding of ML techniques, particularly in feature engineering and data quality management, which can enhance their statistical analysis and modeling capabilities.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new ML algorithms and techniques. They work at the forefront of ML research to push the boundaries of what is possible. This course may provide Machine Learning Researchers with insights into the practical aspects of deploying and managing ML systems, which can help them evaluate and improve the real-world applicability of their research.
Data Architect
Data Architect
Data Architects design and manage data architectures. They ensure the data is accessible, reliable, and meets the needs of the organization. This course may provide Data Architects with an understanding of feature engineering principles and data quality management practices, which are essential for designing and implementing scalable and efficient data architectures.
Data Science Manager
Data Science Manager
Data Science Managers lead and manage teams of data scientists and engineers. They set the strategic direction for data science initiatives and ensure the effective use of data science resources. This course may provide Data Science Managers with an understanding of the operational aspects of ML systems, enabling them to make informed decisions and effectively manage ML projects.
Cloud Architect
Cloud Architect
Cloud Architects design and manage cloud computing solutions. They ensure that cloud solutions are scalable, reliable, and cost-effective. This course may provide Cloud Architects with an understanding of the principles and practices of deploying and managing ML systems in the cloud, enabling them to design and implement robust and efficient cloud-based ML solutions.
IT Manager
IT Manager
IT Managers plan, implement, and manage IT systems and services. They ensure that IT systems meet the needs of the organization and align with business objectives. This course may provide IT Managers with an understanding of the role of ML in IT operations, enabling them to make informed decisions about adopting and managing ML solutions.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. They ensure that projects are completed on time, within budget, and meet the desired quality standards. This course may provide Project Managers with an understanding of the challenges and best practices in managing ML projects, enabling them to effectively lead and deliver successful ML initiatives.
Data Governance Specialist
Data Governance Specialist
Data Governance Specialists develop and implement policies and procedures to ensure the proper use of data. They protect data privacy, ensure data quality, and promote ethical data practices. This course may provide Data Governance Specialists with an understanding of the role of ML in data governance, enabling them to develop and implement effective data governance strategies for ML initiatives.
For more career information including salaries, visit:
OpenCourser.com/course/1mgtjf/mlops
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
MLOps with Vertex AI: Manage Features - Français.
Provides a deep dive into feature engineering, which is essential for building effective machine learning models. It valuable resource for practitioners who want to improve the performance of their models.
Provides a hands-on introduction to machine learning using R, a popular programming language for data science. It valuable resource for practitioners who want to get started with machine learning.
Provides a gentle introduction to machine learning for beginners. It valuable resource for practitioners who want to understand the basics of machine learning.
Provides a deep dive into the statistical foundations of machine learning. It valuable resource for practitioners who want to understand the mathematical underpinnings of machine learning.
Save
Provides a comprehensive overview of data mining, including machine learning. It valuable resource for practitioners who want to learn about the different techniques used in data mining.
Provides a concise overview of machine learning. It valuable resource for practitioners who want to get started with machine learning quickly.
Provides a comprehensive overview of machine learning using Python, a popular programming language for data science. It valuable resource for practitioners who want to learn about the different machine learning algorithms and how to implement them in Python.
Provides a deep dive into the different machine learning algorithms. It valuable resource for practitioners who want to understand the mathematical foundations of machine learning algorithms.
Provides a gentle introduction to machine learning for beginners. It valuable resource for practitioners who want to understand the basics of machine learning.
Provides a hands-on introduction to machine learning using Scikit-Learn, Keras, and TensorFlow, three popular open-source machine learning libraries. It valuable resource for practitioners who want to get started with machine learning quickly.
Provides a concise overview of machine learning. It valuable resource for practitioners who want to get started with machine learning quickly.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1mgtjf/mlops
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Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
Français
Good to know
Enseigne les meilleures pratiques MLOps, qui sont standard dans l'industrie
Développe des fonctionnalités et des outils essentiels pour le MLOps
Enseigné par Google Cloud Training, reconnu pour son expertise en MLOps
Approfondit les problèmes liés aux données et propose des solutions pour les atténuer
Inclus des exercices pratiques sur l'ingestion en continu des fonctionnalités de Vertex AI
Requiert une expérience préalable en MLOps pour tirer pleinement parti du cours
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