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Google Cloud Training
Neste curso, você vai aprender com engenheiros e instrutores de ML que trabalham com o desenvolvimento de última geração dos pipelines de ML aqui no Google Cloud. Nos primeiros módulos, vamos abordar o TensorFlow Extended (ou TFX), que é uma plataforma de machine learning do Google baseada no TensorFlow criada para gerenciar pipelines e metadados de ML. Você vai conhecer os componentes e a orquestração de um pipeline com o TFX. Também vamos abordar como é possível automatizar os pipelines usando a integração e a implantação contínuas e como gerenciar os metadados de ML. Depois disso, vamos mudar o foco para discutir como podemos automatizar e reutilizar os pipelines de ML em vários frameworks de machine learning, como tensorflow, pytorch, scikit-learn e xgboost. Você também vai aprender a usar outra ferramenta no Google Cloud, o Cloud Composer, para orquestrar seus pipelines de treinamento contínuo. Por fim, vamos mostrar como usar o MLflow para gerenciar o ciclo de vida completo do machine learning.
Register for this course and see more details by visiting:
OpenCourser.com/course/i0amm9/ml
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What's inside
Syllabus
Introdução
Neste módulo, abordamos o resumo do curso
Introdução aos Pipelines do TFX
Neste módulo, vamos apresentar o TensorFlow Extended (ou TFX) e abordar os conceitos e componentes dessa plataforma
Read more
Syllabus
Introdução
Neste módulo, abordamos o resumo do curso
Introdução aos Pipelines do TFX
Neste módulo, vamos apresentar o TensorFlow Extended (ou TFX) e abordar os conceitos e componentes dessa plataforma
Read more
Orquestração do pipeline com o TFX
Este módulo abrange o seguinte
Componentes personalizados e CI/CD para pipelines do TFX
Metadados com o TFX
Neste módulo, vamos falar sobre o uso dos metadados do TFX no gerenciamento de artefatos
Treinamento contínuo com vários SDKs, KubeFlow e AI Platform Pipelines
Neste módulo, abordamos o treinamento contínuo com vários SDKs, KubeFlow e AI Platform Pipelines
Treinamento contínuo com Cloud Composer
Neste módulo, abordamos o treinamento contínuo com o Cloud Composer
Pipelines de ML com o MLflow
Neste módulo, vamos apresentar o MLflow e os componentes dele
Resumo
Neste módulo, vamos fazer uma recapitulação do curso
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explora o TensorFlow Extended (TFX), uma plataforma para gerenciar pipelines de ML baseada no TensorFlow
Ensina conceitos e componentes do TFX, como orquestração, metadados e componentes personalizados
Aborda treinamento contínuo com vários SDKs, como TensorFlow, PyTorch e XGBoost
Integra o Cloud Composer para orquestrar pipelines de treinamento contínuo
Introduz o MLflow para gerenciar o ciclo de vida do aprendizado de máquina
Requer conhecimento prévio em pipelines de ML e conceitos relacionados
Save this course
Save ML Pipelines on Google Cloud - Português 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 ML Pipelines on Google Cloud - Português with these
activities:
Revise Concepts in TensorFlow
Show steps
Brush up on essential TensorFlow concepts to enhance your understanding of the course material.
Browse courses on
TensorFlow
Show steps
-
Review TensorFlow documentation
-
Complete introductory TensorFlow tutorials
-
Practice creating and training simple TensorFlow models
Work Through Custom Component and CI/CD Tutorials
Show steps
Reinforce your understanding of custom component creation and continuous integration/continuous delivery for TFX pipelines.
Browse courses on
Custom Components
Show steps
-
Follow official TFX tutorials on creating custom components
-
Set up CI/CD for a TFX pipeline
-
Run through sample scenarios to test your understanding
Attend ML Meetup or Conference
Show steps
Connect with professionals in the ML field at a meetup or conference to expand your knowledge and network.
Browse courses on
Community Engagement
Show steps
-
Identify relevant ML meetups or conferences
-
Register and attend the event
-
Participate in discussions and ask questions
-
Connect with other attendees
One other activity
Expand to see all activities and additional details
Show all four activities
Help Peers in Course Forums
Show steps
Strengthen your understanding of course concepts by helping others in the course forums.
Browse courses on
Peer Support
Show steps
-
Regularly check course forums
-
Identify questions or discussions where you can offer support
-
Provide clear and helpful responses
Revise Concepts in TensorFlow
Show steps
Brush up on essential TensorFlow concepts to enhance your understanding of the course material.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Review TensorFlow documentation
- Complete introductory TensorFlow tutorials
- Practice creating and training simple TensorFlow models
Work Through Custom Component and CI/CD Tutorials
Show steps
Reinforce your understanding of custom component creation and continuous integration/continuous delivery for TFX pipelines.
Browse courses on
Custom Components
Show steps
Show steps
Show steps
- Follow official TFX tutorials on creating custom components
- Set up CI/CD for a TFX pipeline
- Run through sample scenarios to test your understanding
Attend ML Meetup or Conference
Show steps
Connect with professionals in the ML field at a meetup or conference to expand your knowledge and network.
Browse courses on
Community Engagement
Show steps
Show steps
Show steps
- Identify relevant ML meetups or conferences
- Register and attend the event
- Participate in discussions and ask questions
- Connect with other attendees
One other activity
Expand to see all activities and additional details
Show all four activities
Help Peers in Course Forums
Show steps
Strengthen your understanding of course concepts by helping others in the course forums.
Browse courses on
Peer Support
Show steps
Show steps
Show steps
- Regularly check course forums
- Identify questions or discussions where you can offer support
- Provide clear and helpful responses
Career center
Learners who complete ML Pipelines on Google Cloud - Português will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are data science professionals specializing in building, training, and deploying machine learning systems. They work to improve processes and optimize efficiency by applying machine learning models in various domains. The ML Pipelines on Google Cloud - Português course can be a valuable asset for aspiring and current Machine Learning Engineers as it provides a comprehensive overview of the TensorFlow Extended (TFX) platform. By learning about TFX's components and pipeline orchestration, course takers can gain practical skills essential for success in this role.
Data Scientist
Data Scientist
Data Scientists are responsible for extracting insights from data to solve real-world problems. They play a crucial role in transforming raw data into valuable information and developing predictive models. Enrolling in the ML Pipelines on Google Cloud - Português course may benefit individuals pursuing a career in Data Science. The course covers TFX in detail, enhancing the understanding of managing pipelines and ML metadata. This knowledge is in high demand within the Data Science domain.
Machine Learning Operations Engineer
Machine Learning Operations Engineer
Machine Learning Operations Engineers are responsible for deploying, monitoring, and maintaining machine learning models in production. They collaborate with data scientists and software engineers to ensure that models are deployed smoothly and operate efficiently. Individuals interested in a career as a Machine Learning Operations Engineer may find the ML Pipelines on Google Cloud - Português course valuable. The course's emphasis on CI/CD and the automation of ML pipelines can provide them with the skills needed to successfully manage and monitor ML systems in production.
Software Engineer
Software Engineer
Software Engineers are professionals specializing in designing, developing, and maintaining software systems. They use their expertise in programming languages, software design principles, and software development tools to create efficient and reliable software solutions. The ML Pipelines on Google Cloud - Português course can be a valuable resource for Software Engineers interested in working with machine learning pipelines. The course's emphasis on TFX, CI/CD, and the automation of pipelines can enhance their understanding of managing and deploying ML systems.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers are responsible for designing, developing, and deploying artificial intelligence solutions. They possess expertise in machine learning, deep learning, and other AI technologies. Enrolling in the ML Pipelines on Google Cloud - Português course can be a valuable step for individuals pursuing a career as an Artificial Intelligence Engineer. The course's focus on TFX and its components, including model evaluation and serving, can provide them with the foundation needed to successfully manage and deploy AI systems.
Cloud Architect
Cloud Architect
Cloud Architects are responsible for designing, building, and maintaining cloud computing solutions. They possess expertise in cloud technologies, infrastructure management, and software development. Pursuing the ML Pipelines on Google Cloud - Português course can be a strategic move for aspiring or current Cloud Architects. The course's focus on TFX and ML pipelines can provide them with the necessary knowledge and skills to effectively manage machine learning workloads in the cloud environment.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to provide insights for decision-making. They work closely with data scientists, business analysts, and other stakeholders to identify trends, patterns, and anomalies in data. The ML Pipelines on Google Cloud - Português course can be a valuable resource for Data Analysts who want to expand their knowledge in managing and analyzing machine learning data. The course's coverage of TFX and its components, including data validation and transformation, can provide them with the skills needed to effectively prepare data for ML modeling.
Data Architect
Data Architect
Data Architects are specialists in designing and managing data architectures. They work closely with data engineers and data analysts to ensure that data is structured, organized, and accessible to meet the needs of an organization. Taking the ML Pipelines on Google Cloud - Português course can be a beneficial step for Data Architects seeking to enhance their knowledge in managing machine learning data pipelines. The course's coverage of TFX and its components, including metadata management, can provide valuable insights for optimizing data architectures for ML workloads.
Business Analyst
Business Analyst
Business Analysts are responsible for understanding the business needs of an organization and translating them into technical requirements. They work closely with stakeholders to define project scope, gather requirements, and develop solutions. The ML Pipelines on Google Cloud - Português course can be a valuable resource for Business Analysts who want to gain a deeper understanding of how machine learning pipelines can be used to solve business problems. The course's coverage of TFX and its components can provide them with the knowledge needed to effectively collaborate with data scientists and machine learning engineers.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. They work closely with stakeholders to define project scope, create project plans, and track progress. The ML Pipelines on Google Cloud - Português course may be of interest to Project Managers who want to gain a better understanding of how machine learning pipelines can be used to achieve project objectives. The course's coverage of TFX and its components can provide them with the knowledge needed to effectively collaborate with data scientists and machine learning engineers, and to manage machine learning projects successfully.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products. They work closely with engineers, designers, and other stakeholders to define product vision, prioritize features, and ensure that products meet user needs. The ML Pipelines on Google Cloud - Português course may be of interest to Product Managers who want to gain a better understanding of how machine learning can be used to improve products. The course's coverage of TFX and its components can provide them with the knowledge needed to effectively collaborate with data scientists and machine learning engineers, and to make informed decisions about the use of machine learning in products.
Data Engineer
Data Engineer
Data Engineers are responsible for designing, building, and maintaining data infrastructure. They work closely with data scientists, data analysts, and other stakeholders to ensure that data is available, reliable, and secure. The ML Pipelines on Google Cloud - Português course may be of interest to Data Engineers who want to gain a better understanding of how machine learning pipelines can be used to process and transform data. The course's coverage of TFX and its components can provide them with the knowledge needed to effectively collaborate with data scientists and machine learning engineers, and to build and maintain data pipelines that support machine learning workloads.
Software Developer
Software Developer
Software Developers are responsible for designing, developing, and testing software applications. They work closely with other engineers, designers, and product managers to create software that meets user needs. The ML Pipelines on Google Cloud - Português course may be of interest to Software Developers who want to gain a better understanding of how machine learning can be used to improve software applications. The course's coverage of TFX and its components can provide them with the knowledge needed to effectively collaborate with data scientists and machine learning engineers, and to integrate machine learning into software applications.
IT Manager
IT Manager
IT Managers are responsible for planning, implementing, and managing IT systems. They work closely with other IT professionals to ensure that IT systems are aligned with business objectives. The ML Pipelines on Google Cloud - Português course may be of interest to IT Managers who want to gain a better understanding of how machine learning can be used to improve IT systems. The course's coverage of TFX and its components can provide them with the knowledge needed to effectively collaborate with data scientists and machine learning engineers, and to make informed decisions about the use of machine learning in IT systems.
Database Administrator
Database Administrator
Database Administrators are responsible for managing and maintaining databases. They work closely with other IT professionals to ensure that databases are available, reliable, and secure. The ML Pipelines on Google Cloud - Português course may be of interest to Database Administrators who want to gain a better understanding of how machine learning can be used to improve database management. The course's coverage of TFX and its components can provide them with the knowledge needed to effectively collaborate with data scientists and machine learning engineers, and to build and maintain databases that support machine learning workloads.
For more career information including salaries, visit:
OpenCourser.com/course/i0amm9/ml
Reading list
We've selected eight 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
ML Pipelines on Google Cloud - Português.
This classic textbook provides a comprehensive introduction to statistical learning methods. It covers a wide range of topics, including supervised and unsupervised learning, model selection, and regularization. It serves as a valuable reference for understanding the theoretical foundations of machine learning.
Offers a comprehensive introduction to deep learning with Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It provides a solid foundation for understanding the fundamentals of deep learning, which is relevant to many ML pipelines.
Offers a practical guide to making machine learning models more interpretable. It covers techniques for understanding model predictions, identifying important features, and communicating results effectively.
Save
Offers a comprehensive overview of designing data-intensive applications, covering topics such as data modeling, storage, and processing. It provides a strong foundation for understanding the underlying principles of ML pipelines.
Comprehensive guide to using Scikit-Learn, Keras, and TensorFlow to build and deploy ML models. It valuable resource for ML practitioners of all levels.
Comprehensive guide to using Python to build and deploy ML models. It valuable resource for ML practitioners of all levels.
Comprehensive guide to ML for beginners. It covers the fundamentals of ML, including topics such as data preparation, model selection, and model evaluation.
Save
Classic textbook on ML. It covers the fundamentals of ML, including topics such as data preparation, model selection, and model evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/i0amm9/ml
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Effort
3 weeks of study, 6-8 hours/week
Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
Português
Transcript
English
Good to know
Explora o TensorFlow Extended (TFX), uma plataforma para gerenciar pipelines de ML baseada no TensorFlow
Ensina conceitos e componentes do TFX, como orquestração, metadados e componentes personalizados
Aborda treinamento contínuo com vários SDKs, como TensorFlow, PyTorch e XGBoost
Integra o Cloud Composer para orquestrar pipelines de treinamento contínuo
Introduz o MLflow para gerenciar o ciclo de vida do aprendizado de máquina
Requer conhecimento prévio em pipelines de ML e conceitos relacionados
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