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Google Cloud Training
Neste curso, os participantes vão conhecer as ferramentas de MLOps e as práticas recomendadas para a implantação, a avaliação, o monitoramento e a operação de sistemas de ML de produção no Google Cloud. MLOps é uma disciplina com foco na implantação, no teste, no monitoramento e na automação de sistemas de ML em produção. Profissionais de engenharia de machine learning usam ferramentas para fazer melhorias contínuas e avaliações de modelos implantados. São profissionais que trabalham com ciências de dados e desenvolvem modelos para garantir a velocidade e o rigor na implantação de modelos com melhor desempenho.
Register for this course and see more details by visiting:
OpenCourser.com/course/m27j53/mlops
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What's inside
Syllabus
Conheça as operações de machine learning (MLOps): como começar
Neste módulo, apresentamos uma visão geral do curso
Como empregar operações de machine learning
Read more
Syllabus
Conheça as operações de machine learning (MLOps): como começar
Neste módulo, apresentamos uma visão geral do curso
Como empregar operações de machine learning
Read more
Dificuldades dos profissionais de machine learning O conceito de DevOps em ML As três fases do ciclo de vida de ML Automatização de processos de ML
Vertex AI e MLOps na Vertex AI
O que é Vertex AI e qual a importância de ter uma plataforma unificada? Introdução a MLOps na Vertex AI
Como a Vertex AI ajuda no fluxo de trabalho de MLOps? Parte 1
Como a Vertex AI ajuda no fluxo de trabalho de MLOps? Parte 2
Resumo
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores the intersection of machine learning and DevOps (ML and DevOps) to enhance production ML
Focuses on providing a structured and efficient workflow for deploying, evaluating, monitoring, and operating ML systems
Suitable for professionals seeking to implement effective MLOps practices and improve the performance of their ML models
Led by Google Cloud Training, a reputable provider with expertise in cloud-based MLOps solutions
Offers hands-on labs, which enhance the practicality and applicability of the concepts taught
Emphasizes the importance of continuous improvement and evaluation in the ML lifecycle
Save this course
Save Machine Learning Operations (MLOps): Getting Started - Português Brasileiro to your list so you can find it easily later:
Save
Save
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 - Português Brasileiro with these
activities:
Review the fundamentals of machine learning
Show steps
Review the basics of machine learning, including supervised and unsupervised learning, as well as reinforcement learning, to strengthen your foundation and better prepare for the course.
Browse courses on
Machine Learning Fundamentals
Show steps
-
Enroll in an online course or tutorial on machine learning fundamentals.
-
Read textbooks or articles on machine learning.
-
Complete practice problems and exercises.
Create a curated list of resources on MLOps
Show steps
Gather and organize a comprehensive list of resources on MLOps, including articles, tutorials, and tools, to enhance your knowledge and stay up-to-date with the latest advancements.
Browse courses on
Machine Learning Operations
Show steps
-
Search for articles, tutorials, and other resources on MLOps.
-
Organize the resources into a structured format, such as a spreadsheet or online document.
Mentor a junior machine learning engineer on MLOps
Show steps
Share your knowledge and experience by mentoring a junior machine learning engineer on MLOps, reinforcing your understanding and consolidating your learning.
Browse courses on
Mentoring
Show steps
-
Identify a junior machine learning engineer who is interested in learning about MLOps.
-
Set up regular meetings to discuss MLOps concepts and practices.
-
Provide guidance and support on MLOps projects.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete a Coursera course on MLOps
Show steps
Enroll in a guided Coursera course on MLOps to gain a deeper understanding of the practices and tools used in deploying, evaluating, and monitoring machine learning models in production.
Browse courses on
MLOps
Show steps
-
Choose a Coursera course on MLOps that aligns with your learning goals.
-
Complete the course at your own pace, following the provided lectures, tutorials, and assignments.
Attend a workshop on MLOps best practices
Show steps
Participate in a hands-on workshop to enhance your understanding of MLOps best practices and gain insights from experts in the field.
Browse courses on
Machine Learning Operations
Show steps
-
Research and identify MLOps workshops that align with your learning goals.
-
Register for the workshop and attend all sessions.
Develop a plan for implementing MLOps in your organization
Show steps
Create a comprehensive plan for implementing MLOps in your organization to apply your learning to a real-world scenario and demonstrate your understanding of MLOps principles.
Browse courses on
Machine Learning Operations
Show steps
-
Identify the specific goals and objectives for implementing MLOps.
-
Research and evaluate different MLOps tools and platforms.
-
Develop a roadmap for implementing MLOps, including timelines and milestones.
-
Identify the roles and responsibilities of different stakeholders in the MLOps process.
Practice deploying and monitoring machine learning models using Vertex AI
Show steps
Gain hands-on experience by deploying and monitoring machine learning models on Vertex AI to reinforce your understanding of the MLOps lifecycle.
Browse courses on
Vertex AI
Show steps
-
Create a Vertex AI project.
-
Deploy a machine learning model using Vertex AI.
-
Monitor the performance of your deployed model using Vertex AI.
Contribute to an open-source MLOps project
Show steps
Contribute to an open-source MLOps project to gain practical experience and enhance your understanding of real-world MLOps practices.
Browse courses on
Open Source
Show steps
-
Find an open-source MLOps project that aligns with your interests.
-
Read the project documentation and contribute to discussions.
-
Submit a bug report or feature request.
-
Propose and implement a code change.
Review the fundamentals of machine learning
Show steps
Review the basics of machine learning, including supervised and unsupervised learning, as well as reinforcement learning, to strengthen your foundation and better prepare for the course.
Browse courses on
Machine Learning Fundamentals
Show steps
Show steps
Show steps
- Enroll in an online course or tutorial on machine learning fundamentals.
- Read textbooks or articles on machine learning.
- Complete practice problems and exercises.
Create a curated list of resources on MLOps
Show steps
Gather and organize a comprehensive list of resources on MLOps, including articles, tutorials, and tools, to enhance your knowledge and stay up-to-date with the latest advancements.
Browse courses on
Machine Learning Operations
Show steps
Show steps
Show steps
- Search for articles, tutorials, and other resources on MLOps.
- Organize the resources into a structured format, such as a spreadsheet or online document.
Mentor a junior machine learning engineer on MLOps
Show steps
Share your knowledge and experience by mentoring a junior machine learning engineer on MLOps, reinforcing your understanding and consolidating your learning.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify a junior machine learning engineer who is interested in learning about MLOps.
- Set up regular meetings to discuss MLOps concepts and practices.
- Provide guidance and support on MLOps projects.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete a Coursera course on MLOps
Show steps
Enroll in a guided Coursera course on MLOps to gain a deeper understanding of the practices and tools used in deploying, evaluating, and monitoring machine learning models in production.
Browse courses on
MLOps
Show steps
Show steps
Show steps
- Choose a Coursera course on MLOps that aligns with your learning goals.
- Complete the course at your own pace, following the provided lectures, tutorials, and assignments.
Attend a workshop on MLOps best practices
Show steps
Participate in a hands-on workshop to enhance your understanding of MLOps best practices and gain insights from experts in the field.
Browse courses on
Machine Learning Operations
Show steps
Show steps
Show steps
- Research and identify MLOps workshops that align with your learning goals.
- Register for the workshop and attend all sessions.
Develop a plan for implementing MLOps in your organization
Show steps
Create a comprehensive plan for implementing MLOps in your organization to apply your learning to a real-world scenario and demonstrate your understanding of MLOps principles.
Browse courses on
Machine Learning Operations
Show steps
Show steps
Show steps
- Identify the specific goals and objectives for implementing MLOps.
- Research and evaluate different MLOps tools and platforms.
- Develop a roadmap for implementing MLOps, including timelines and milestones.
- Identify the roles and responsibilities of different stakeholders in the MLOps process.
Practice deploying and monitoring machine learning models using Vertex AI
Show steps
Gain hands-on experience by deploying and monitoring machine learning models on Vertex AI to reinforce your understanding of the MLOps lifecycle.
Browse courses on
Vertex AI
Show steps
Show steps
Show steps
- Create a Vertex AI project.
- Deploy a machine learning model using Vertex AI.
- Monitor the performance of your deployed model using Vertex AI.
Contribute to an open-source MLOps project
Show steps
Contribute to an open-source MLOps project to gain practical experience and enhance your understanding of real-world MLOps practices.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find an open-source MLOps project that aligns with your interests.
- Read the project documentation and contribute to discussions.
- Submit a bug report or feature request.
- Propose and implement a code change.
Career center
Learners who complete Machine Learning Operations (MLOps): Getting Started - Português Brasileiro will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use data to build models that can predict outcomes or make decisions. They may also be responsible for deploying and monitoring these models in production. This course can help Data Scientists by providing them with the tools and practices they need to successfully deploy, evaluate, monitor, and operate machine learning systems in production. Data Scientists who take this course may gain a competitive edge in the job market, as they will be able to demonstrate their proficiency in MLOps best practices.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use machine learning algorithms to build models that can predict outcomes or make decisions. They may also be responsible for deploying and monitoring these models in production. This course can help Machine Learning Engineers by providing them with the tools and practices they need to successfully deploy, evaluate, monitor, and operate machine learning systems in production. Machine Learning Engineers who take this course may gain a competitive edge in the job market, as they will be able to demonstrate their proficiency in MLOps best practices.
Data Engineer
Data Engineer
Data Engineers build and maintain the infrastructure that supports data-driven applications. They may also be responsible for deploying and monitoring machine learning models in production. This course can help Data Engineers by providing them with the tools and practices they need to successfully deploy, evaluate, monitor, and operate machine learning systems in production. Data Engineers who take this course may gain a competitive edge in the job market, as they will be able to demonstrate their proficiency in MLOps best practices.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They may also be responsible for deploying and monitoring these systems in production. This course can help Software Engineers by providing them with the tools and practices they need to successfully deploy, evaluate, monitor, and operate machine learning systems in production. Software Engineers who take this course may gain a competitive edge in the job market, as they will be able to demonstrate their proficiency in MLOps best practices.
DevOps Engineer
DevOps Engineer
DevOps Engineers are responsible for bridging the gap between development and operations teams. They may also be responsible for deploying and monitoring machine learning models in production. This course can help DevOps Engineers by providing them with the tools and practices they need to successfully deploy, evaluate, monitor, and operate machine learning systems in production. DevOps Engineers who take this course may gain a competitive edge in the job market, as they will be able to demonstrate their proficiency in MLOps best practices.
Cloud Architect
Cloud Architect
Cloud Architects design and build cloud-based solutions. They may also be responsible for deploying and monitoring machine learning models in production. This course can help Cloud Architects by providing them with the tools and practices they need to successfully deploy, evaluate, monitor, and operate machine learning systems in production. Cloud Architects who take this course may gain a competitive edge in the job market, as they will be able to demonstrate their proficiency in MLOps best practices.
Machine Learning Operations Engineer
Machine Learning Operations Engineer
Machine Learning Operations Engineers are responsible for deploying, evaluating, monitoring, and operating machine learning systems in production. This course can help Machine Learning Operations Engineers by providing them with the tools and practices they need to be successful in their roles. Machine Learning Operations Engineers who take this course may gain a competitive edge in the job market, as they will be able to demonstrate their proficiency in MLOps best practices.
Data Analyst
Data Analyst
Data Analysts use data to identify trends and patterns. They may also be responsible for deploying and monitoring machine learning models in production. This course can help Data Analysts by providing them with the tools and practices they need to successfully deploy, evaluate, monitor, and operate machine learning systems in production. Data Analysts who take this course may gain a competitive edge in the job market, as they will be able to demonstrate their proficiency in MLOps best practices.
Business Analyst
Business Analyst
Business Analysts use data to understand business needs and opportunities. They may also be responsible for deploying and monitoring machine learning models in production. This course can help Business Analysts by providing them with the tools and practices they need to successfully deploy, evaluate, monitor, and operate machine learning systems in production. Business Analysts who take this course may gain a competitive edge in the job market, as they will be able to demonstrate their proficiency in MLOps best practices.
Project Manager
Project Manager
Project Managers are responsible for planning and executing projects. They may also be responsible for deploying and monitoring machine learning models in production. This course can help Project Managers by providing them with the tools and practices they need to successfully deploy, evaluate, monitor, and operate machine learning systems in production. Project Managers who take this course may gain a competitive edge in the job market, as they will be able to demonstrate their proficiency in MLOps best practices.
Product Manager
Product Manager
Product Managers are responsible for managing the development and launch of new products and features. They may also be responsible for deploying and monitoring machine learning models in production. This course can help Product Managers by providing them with the tools and practices they need to successfully deploy, evaluate, monitor, and operate machine learning systems in production. Product Managers who take this course may gain a competitive edge in the job market, as they will be able to demonstrate their proficiency in MLOps best practices.
Technical Writer
Technical Writer
Technical Writers create documentation for software and hardware products. They may also be responsible for deploying and monitoring machine learning models in production. This course can help Technical Writers by providing them with the tools and practices they need to successfully deploy, evaluate, monitor, and operate machine learning systems in production. Technical Writers who take this course may gain a competitive edge in the job market, as they will be able to demonstrate their proficiency in MLOps best practices.
Systems Analyst
Systems Analyst
Systems Analysts analyze and design computer systems. They may also be responsible for deploying and monitoring machine learning models in production. This course can help Systems Analysts by providing them with the tools and practices they need to successfully deploy, evaluate, monitor, and operate machine learning systems in production. Systems Analysts who take this course may gain a competitive edge in the job market, as they will be able to demonstrate their proficiency in MLOps best practices.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts test software and hardware products to ensure that they meet quality standards. They may also be responsible for deploying and monitoring machine learning models in production. This course can help Quality Assurance Analysts by providing them with the tools and practices they need to successfully deploy, evaluate, monitor, and operate machine learning systems in production. Quality Assurance Analysts who take this course may gain a competitive edge in the job market, as they will be able to demonstrate their proficiency in MLOps best practices.
For more career information including salaries, visit:
OpenCourser.com/course/m27j53/mlops
Reading list
We've selected ten 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 - Português Brasileiro.
Save
Provides a comprehensive guide to deep learning. The book covers topics such as the history of deep learning, the different types of deep learning, and the applications of deep learning.
Provides a practical guide to deploying and managing ML models in production. The book covers topics such as model monitoring, data drift detection, and A/B testing.
Covers topics such as MLOps principles, data management, feature engineering, model training, evaluation, monitoring, and deployment in a production environment. The book is written for engineers who are new to MLOps.
Provides a comprehensive guide to the theory and practice of machine learning. The book covers topics such as supervised learning, unsupervised learning, and deep learning.
Save
Provides a comprehensive guide to writing clean code. The book covers topics such as naming conventions, refactoring, and testing.
Provides a hands-on guide to machine learning. The book covers topics such as data preprocessing, model training, and evaluation.
Provides a comprehensive guide to software development. The book covers topics such as design patterns, testing, and refactoring.
Provides a business-oriented guide to machine learning. The book covers topics such as the benefits of ML, how to use ML to solve business problems, and the challenges of implementing ML.
Provides a comprehensive guide to Python programming. The book covers topics such as data types, control flow, and functions.
Provides a comprehensive overview of artificial intelligence. The book covers topics such as the history of AI, the different types of AI, and the applications of AI.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/m27j53/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
English
Transcript
Português
Good to know
Explores the intersection of machine learning and DevOps (ML and DevOps) to enhance production ML
Focuses on providing a structured and efficient workflow for deploying, evaluating, monitoring, and operating ML systems
Suitable for professionals seeking to implement effective MLOps practices and improve the performance of their ML models
Led by Google Cloud Training, a reputable provider with expertise in cloud-based MLOps solutions
Offers hands-on labs, which enhance the practicality and applicability of the concepts taught
Emphasizes the importance of continuous improvement and evaluation in the ML lifecycle
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