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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 ingénieurs en machine learning utilisent des outils pour améliorer et évaluer en permanence les modèles déployés. Ils collaborent avec des data scientists (ou peuvent occuper ce poste) qui développent des modèles permettant de déployer de manière rapide et rigoureuse les solutions de machine learning les plus performantes.
Register for this course and see more details by visiting:
OpenCourser.com/course/ymljh9/mlops
What's inside
Syllabus
Bienvenue dans le module "Opérations de machine learning (MLOps) : premiers pas"
Ce module fournit une présentation du cours
Utiliser des opérations de machine learning
Read more
Syllabus
Bienvenue dans le module "Opérations de machine learning (MLOps) : premiers pas"
Ce module fournit une présentation du cours
Utiliser des opérations de machine learning
Read more
Les difficultés des professionnels du MLLe concept du DevOps en MLLes trois phases du cycle de vie du MLAutomatiser le processus de ML
Vertex AI et MLOps sur Vertex AI
Qu'est-ce que Vertex AI et pourquoi une plate-forme unifiée est-elle importante ?Présentation du MLOps sur Vertex AIEn quoi Vertex AI est-il utile dans le workflow de ML ? Partie 1En quoi Vertex AI est-il utile dans le workflow de ML ? Partie 2
Résumé
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
S'adresse aux ingénieurs en apprentissage automatique, aux data scientists et aux personnes intéressées par le déploiement, l'évaluation, la surveillance et l'exploitation des systèmes d'apprentissage automatique en production sur Google Cloud
Couvre les outils et les bonnes pratiques de MLOps, tels que Vertex AI
Save this course
Save Machine Learning Operations (MLOps): Getting Started - 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 Machine Learning Operations (MLOps): Getting Started - Français with these
activities:
Review MLOps terminology
Show steps
Reviewing the basics will revive understanding and aid during the course
Browse courses on
MLOps
Show steps
-
Define MLOps
-
List the phases in the MLOps lifecycle
-
Describe the role of DevOps in MLOps
Find an MLOps mentor
Show steps
Gain guidance and support from an experienced MLOps practitioner
Show steps
-
Identify potential mentors
-
Reach out to potential mentors and express interest
-
Build a relationship with your mentor
Build an ML pipeline with Vertex AI
Show steps
Gain hands-on experience with the tools and techniques used in the course
Browse courses on
Vertex AI
Show steps
-
Create a new Vertex AI project
-
Build a dataset in Vertex AI
-
Create a model in Vertex AI
-
Deploy the model to production
Five other activities
Expand to see all activities and additional details
Show all eight activities
Discuss the challenges of MLOps
Show steps
Share experiences and learn from others to gain a deeper understanding of the challenges and best practices in MLOps
Browse courses on
Machine Learning Operations
Show steps
-
Discuss the challenges of deploying ML models to production
-
Share best practices for monitoring and maintaining ML models in production
-
Discuss the role of automation in MLOps
Deploy a model using Vertex AI Pipelines
Show steps
Practice deploying models to production on a regular basis to reinforce understanding
Browse courses on
Vertex AI Pipelines
Show steps
-
Create a new Vertex AI Pipeline
-
Add a dataset to the pipeline
-
Add a model to the pipeline
-
Deploy the pipeline to production
Develop an MLOps plan for a real-world project
Show steps
Apply the knowledge and skills gained in the course to a practical problem
Browse courses on
Machine Learning Operations
Show steps
-
Define the project goals
-
Identify the data sources and types
-
Develop a model training plan
-
Create a plan for deploying and monitoring the model
Volunteer for an MLOps project
Show steps
Gain practical experience and contribute to the community by volunteering on an MLOps project
Show steps
-
Find an MLOps project to volunteer for
-
Contact the project leader and express interest
-
Contribute to the project in a meaningful way
Contribute to an open-source MLOps project
Show steps
Gain experience and contribute to the community by contributing to an open-source MLOps project
Show steps
-
Find an open-source MLOps project to contribute to
-
Contact the project leader and express interest
-
Contribute to the project in a meaningful way
Review MLOps terminology
Show steps
Reviewing the basics will revive understanding and aid during the course
Browse courses on
MLOps
Show steps
Show steps
Show steps
- Define MLOps
- List the phases in the MLOps lifecycle
- Describe the role of DevOps in MLOps
Find an MLOps mentor
Show steps
Gain guidance and support from an experienced MLOps practitioner
Show steps
Show steps
Show steps
- Identify potential mentors
- Reach out to potential mentors and express interest
- Build a relationship with your mentor
Build an ML pipeline with Vertex AI
Show steps
Gain hands-on experience with the tools and techniques used in the course
Browse courses on
Vertex AI
Show steps
Show steps
Show steps
- Create a new Vertex AI project
- Build a dataset in Vertex AI
- Create a model in Vertex AI
- Deploy the model to production
Five other activities
Expand to see all activities and additional details
Show all eight activities
Discuss the challenges of MLOps
Show steps
Share experiences and learn from others to gain a deeper understanding of the challenges and best practices in MLOps
Browse courses on
Machine Learning Operations
Show steps
Show steps
Show steps
- Discuss the challenges of deploying ML models to production
- Share best practices for monitoring and maintaining ML models in production
- Discuss the role of automation in MLOps
Deploy a model using Vertex AI Pipelines
Show steps
Practice deploying models to production on a regular basis to reinforce understanding
Browse courses on
Vertex AI Pipelines
Show steps
Show steps
Show steps
- Create a new Vertex AI Pipeline
- Add a dataset to the pipeline
- Add a model to the pipeline
- Deploy the pipeline to production
Develop an MLOps plan for a real-world project
Show steps
Apply the knowledge and skills gained in the course to a practical problem
Browse courses on
Machine Learning Operations
Show steps
Show steps
Show steps
- Define the project goals
- Identify the data sources and types
- Develop a model training plan
- Create a plan for deploying and monitoring the model
Volunteer for an MLOps project
Show steps
Gain practical experience and contribute to the community by volunteering on an MLOps project
Show steps
Show steps
Show steps
- Find an MLOps project to volunteer for
- Contact the project leader and express interest
- Contribute to the project in a meaningful way
Contribute to an open-source MLOps project
Show steps
Gain experience and contribute to the community by contributing to an open-source MLOps project
Show steps
Show steps
Show steps
- Find an open-source MLOps project to contribute to
- Contact the project leader and express interest
- Contribute to the project in a meaningful way
Career center
Learners who complete Machine Learning Operations (MLOps): Getting Started - Français will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers can utilize their knowledge of MLOps concepts from this course to automate the deployment, monitoring, and maintenance of ML models in production. Vertex AI, which is covered in the course, offers a unified platform for these tasks, enabling Machine Learning Engineers to efficiently manage the ML lifecycle. By understanding MLOps best practices and using Vertex AI, they can ensure the reliability and performance of ML systems in real-world applications.
Data Scientist
Data Scientist
A Data Scientist can use the concepts and skills learned in Machine Learning Operations (MLOps): Getting Started - Français to integrate ML models into production systems and environments. The course can provide a foundation in Continuous Integration/Continuous Delivery using Vertex AI, which is a platform for deploying and managing ML models in the cloud. As Data Scientists generally work with large datasets and complex models, understanding MLOps best practices is crucial for their successful deployment and improvement.
DevOps Engineer
DevOps Engineer
DevOps Engineers working on ML projects will find Machine Learning Operations (MLOps): Getting Started - Français to be a valuable resource. The course offers a comprehensive overview of MLOps concepts and best practices, including topics such as continuous integration, continuous delivery, and monitoring. DevOps Engineers can use this knowledge to optimize the development and deployment of ML models in production environments. By understanding the MLOps lifecycle and leveraging tools like Vertex AI, DevOps Engineers can ensure the reliability, scalability, and performance of ML systems.
Data Engineer
Data Engineer
Data Engineers who specialize in ML or work on ML projects can apply the concepts and knowledge gained from Machine Learning Operations (MLOps): Getting Started - Français to enhance their skills. The course covers MLOps best practices for managing the ML lifecycle, including data preparation, feature engineering, model training, and deployment. By understanding these principles and leveraging tools like Vertex AI, Data Engineers can contribute to the development and maintenance of high-quality ML systems that deliver value to end users.
Data Analyst
Data Analyst
Data Analysts working on ML projects can enhance their skills by taking the Machine Learning Operations (MLOps): Getting Started - Français course. The course covers foundational MLOps concepts such as continuous integration and delivery, monitoring, and performance optimization. Data Analysts can leverage this knowledge to understand how ML models are deployed and maintained in production environments. By collaborating with MLOps engineers, Data Analysts can ensure the quality and accuracy of ML models throughout their lifecycle, leading to better decision-making and business outcomes.
Systems Engineer
Systems Engineer
Systems Engineers working on ML infrastructure can benefit from the insights provided in Machine Learning Operations (MLOps): Getting Started - Français. The course covers MLOps best practices for deploying and managing ML models in production environments, including topics such as continuous integration, continuous delivery, and monitoring. This knowledge enables Systems Engineers to design and implement reliable and scalable ML systems that meet the performance and availability requirements of end users. By understanding MLOps principles and leveraging tools like Vertex AI, Systems Engineers can contribute to the successful deployment and maintenance of ML solutions.
Software Engineer
Software Engineer
Software Engineers who work on ML-related projects can benefit greatly from the concepts and skills taught in Machine Learning Operations (MLOps): Getting Started - Français. The course provides a foundation in MLOps best practices, including continuous integration, continuous delivery, and monitoring. These principles are essential for building and maintaining robust ML systems that can be deployed and scaled in production environments. Software Engineers can apply these concepts to improve the quality and efficiency of their ML-based software projects.
Cloud Architect
Cloud Architect
Cloud Architects who focus on designing and managing cloud-based ML solutions can benefit from the insights provided in Machine Learning Operations (MLOps): Getting Started - Français. The course covers MLOps best practices for deploying and managing ML models in production environments, including topics such as continuous integration, continuous delivery, and monitoring. This knowledge enables Cloud Architects to design and implement scalable and reliable ML systems that meet the performance and availability requirements of end users.
Solutions Architect
Solutions Architect
Solutions Architects specializing in ML can gain valuable knowledge from Machine Learning Operations (MLOps): Getting Started - Français. The course provides a comprehensive overview of MLOps concepts and best practices, including topics such as continuous integration, continuous delivery, and performance optimization. By understanding these principles, Solutions Architects can design and implement scalable and reliable ML solutions that meet the specific needs of their clients. The course also emphasizes the importance of collaboration between technical and non-technical teams, enabling Solutions Architects to effectively communicate the value and benefits of ML solutions to stakeholders at all levels.
IT Manager
IT Manager
IT Managers responsible for overseeing ML projects can benefit from the knowledge gained in Machine Learning Operations (MLOps): Getting Started - Français. The course provides a comprehensive overview of MLOps concepts and best practices, including topics such as continuous integration, continuous delivery, and monitoring. By understanding these principles, IT Managers can make informed decisions regarding the infrastructure and resources required to support ML projects. Additionally, the course highlights the importance of collaboration between technical and non-technical teams, enabling IT Managers to foster a positive and productive work environment that drives the successful implementation of ML solutions.
Business Analyst
Business Analyst
Business Analysts who wish to gain a deeper understanding of ML operations can benefit from Machine Learning Operations (MLOps): Getting Started - Français. The course provides a comprehensive overview of MLOps concepts and best practices, including topics such as continuous integration, continuous delivery, and monitoring. This knowledge enables Business Analysts to effectively collaborate with technical teams and make informed decisions regarding the deployment and management of ML models in production environments. By understanding MLOps principles, Business Analysts can contribute to the success of ML-driven initiatives within their organizations.
Sales Engineer
Sales Engineer
Sales Engineers specializing in ML products can enhance their knowledge and skills by taking Machine Learning Operations (MLOps): Getting Started - Français. The course provides a comprehensive overview of MLOps concepts and best practices, including continuous integration, continuous delivery, monitoring, and performance optimization. By understanding the challenges and opportunities associated with deploying and managing ML models in production environments, Sales Engineers can effectively demonstrate the value and benefits of their ML solutions to potential customers. The course also emphasizes clear and concise communication, enabling Sales Engineers to effectively engage with technical and non-technical stakeholders alike.
Product Manager
Product Manager
Product Managers responsible for ML-driven products can gain valuable insights from Machine Learning Operations (MLOps): Getting Started - Français. The course provides a comprehensive overview of MLOps concepts and best practices, including topics such as continuous integration, continuous delivery, and monitoring. This knowledge enables Product Managers to understand the challenges and opportunities associated with deploying and managing ML models in production environments. By leveraging MLOps principles, Product Managers can ensure the successful launch and ongoing improvement of ML-powered products.
Technical Writer
Technical Writer
Technical Writers specializing in documentation for ML systems can enhance their knowledge by taking Machine Learning Operations (MLOps): Getting Started - Français. The course provides a comprehensive overview of MLOps concepts and best practices, including continuous integration, continuous delivery, and monitoring. By understanding these principles, Technical Writers can create accurate and informative documentation that guides users through the deployment and management of ML models in production environments. The course also emphasizes the importance of clear and concise communication, enabling Technical Writers to effectively convey complex technical information to a wide range of audiences.
Project Manager
Project Manager
Project Managers leading ML projects can enhance their knowledge and skills by taking Machine Learning Operations (MLOps): Getting Started - Français. The course covers key MLOps concepts such as continuous integration and delivery, monitoring, and performance optimization. By understanding these principles, Project Managers can effectively plan, execute, and manage ML projects, ensuring timely and successful delivery of high-quality ML solutions. The course also provides insights into the collaboration between technical and non-technical team members, enabling Project Managers to foster a productive and efficient work environment.
For more career information including salaries, visit:
OpenCourser.com/course/ymljh9/mlops
Reading list
We've selected six 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
Machine Learning Operations (MLOps): Getting Started - Français.
Provides a detailed overview of machine learning system design. It covers topics such as data architecture, model training, and deployment. It valuable resource for anyone looking to build and operate scalable and reliable ML systems.
Provides a comprehensive overview of machine learning with Python. It covers a wide range of topics, from data preparation and model training to deployment and evaluation. It great resource for anyone looking to learn more about machine learning in Python.
Save
Provides a comprehensive overview of data mining. It covers a wide range of topics, from data preprocessing and data mining techniques to data warehousing and data visualization. It great resource for anyone looking to learn more about data mining.
Provides a comprehensive introduction to machine learning. It covers a wide range of topics, from data preparation and model training to deployment and evaluation. It great resource for anyone looking to learn more about machine learning.
Provides a practical introduction to machine learning. It covers a wide range of topics, from data preparation and model training to deployment and evaluation. It great resource for anyone looking to get started with machine learning.
Provides a practical introduction to machine learning with Python. It covers a wide range of topics, from data preparation and model training to deployment and evaluation. It great resource for anyone looking to get started with machine learning in Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ymljh9/mlops
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Effort
3 weeks of study, 6-8 hours/week
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
Français
Good to know
S'adresse aux ingénieurs en apprentissage automatique, aux data scientists et aux personnes intéressées par le déploiement, l'évaluation, la surveillance et l'exploitation des systèmes d'apprentissage automatique en production sur Google Cloud
Couvre les outils et les bonnes pratiques de MLOps, tels que Vertex AI
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