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Google Cloud Training
Neste curso, vamos conhecer os componentes e as práticas recomendadas para criar sistemas de ML com alto desempenho em ambientes de produção. Vamos abordar algumas considerações comuns relacionadas à criação desses sistemas, como treinamento estático e dinâmico, inferência estática e dinâmica, TensorFlow distribuído e TPUs. O objetivo deste curso é conhecer as características de um sistema de ML eficiente, que vão muito além da capacidade de fazer boas previsões.
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What's inside
Syllabus
Introdução ao machine learning avançado no Google Cloud
Neste módulo, mostramos os temas que serão abordados no curso e ensinamos a usar o Qwiklabs para fazer todos os laboratórios no Google Cloud.
Read more
Syllabus
Introdução ao machine learning avançado no Google Cloud
Neste módulo, mostramos os temas que serão abordados no curso e ensinamos a usar o Qwiklabs para fazer todos os laboratórios no Google Cloud.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Atua com ferramentas e técnicas recomendadas pelo mercado de trabalho
Ensina conceitos que podem ser aplicados em sistemas de produção de aprendizado de máquina
Desenvolve habilidades para criar sistemas de machine learning que não só fazem boas previsões, como também funciona bem em produção
Tem o Google Cloud Training como instrutor, que é referência na área
Explora conceitos fundamentais, como treinamento dinâmico estático, inferência e TPUs
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Create your own learning path.
Save this course to your list so you can find it easily later.
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Reviews summary
Sistemas de ml em produção de alto nível
De acordo com os alunos, este curso é excelente para profissionais que buscam aprofundar seus conhecimentos em Machine Learning em ambientes de produção. A maioria dos estudantes elogia o conteúdo prático e os laboratórios úteis, que permitem a aplicação imediata dos conceitos. Os tópicos abordados, como TensorFlow distribuído e TPUs, são considerados relevantes para as necessidades do mercado. No entanto, alguns revisores alertam que o curso é bastante denso e exige um conhecimento prévio sólido em Machine Learning e Google Cloud, podendo ser desafiador para iniciantes na área ou em GCP.
Destinado a profissionais com base sólida em ML e GCP.
"Muito bom conteúdo, mas exige um conhecimento prévio sólido em machine learning e Google Cloud."
"Achei o curso muito avançado para o meu nível. Não tenho muita experiência com Google Cloud e os exemplos já partiam do pressuposto que eu sabia usar as ferramentas."
"Acredito que seja ideal para quem já tem experiência e quer formalizar ou aprofundar conhecimentos em ML de produção."
"Recomendo para quem já tem uma base em ML e quer ir para o próximo nível."
Os labs permitem aplicar o conhecimento e entender conceitos.
"Os laboratórios são muito úteis e me deram uma visão prática de como implementar modelos em grande escala."
"Os exemplos práticos compensam a densidade teórica."
"Os labs são muito bem elaborados e me permitiram aplicar o conhecimento imediatamente."
Oferece uma visão prática e aprofundada de ML em ambientes reais.
"Excelente curso! Aborda os desafios reais de ML em produção."
"Um dos melhores cursos que fiz sobre MLOps. A profundidade dos tópicos de arquitetura de sistemas e desempenho é impressionante. Realmente focado em produção, não apenas em modelos."
"Realmente um divisor de águas para quem quer ir além da teoria e colocar ML em produção."
"Totalmente alinhado com as necessidades do mercado. Aborda desde a arquitetura até a otimização de performance."
O conteúdo é técnico e pode ser rápido para alguns alunos.
"Às vezes, senti que o ritmo era um pouco rápido para quem não está 100% familiarizado com todos os termos."
"O curso é denso e aborda tópicos importantes, mas achei que faltaram mais explicações sobre os conceitos básicos de alguns serviços do Google Cloud."
"Conteúdo de alta qualidade, porém a linguagem é bastante técnica. Esperava talvez um pouco mais de introdução para alguns tópicos."
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 Production Machine Learning Systems - Português Brasileiro with these
activities:
Revise Tensorflow
Show steps
TensorFlow e TPU são os principais componentes para a construção de modelos de aprendizado de máquina no Google Cloud. Revisar o TensorFlow ajudará a construir uma base sólida para o curso.
Browse courses on
TensorFlow
Show steps
-
Revise os conceitos básicos do TensorFlow, como tensores, operações e gráficos de fluxo de dados.
-
Pratique a criação e o treinamento de modelos simples do TensorFlow.
Engage in peer discussions on ML best practices
Show steps
Enhance understanding by exchanging ideas and discussing machine learning best practices with peers.
Show steps
-
Join or create peer study groups or online forums.
-
Actively participate in discussions, sharing insights and experiences.
-
Seek feedback and constructive criticism on your own approaches.
Practice exercises on TensorFlow and TPUs
Show steps
Deepen your understanding of TensorFlow and TPUs through hands-on exercises.
Browse courses on
TensorFlow
Show steps
-
Complete coding exercises and tutorials on TensorFlow and TPUs.
-
Build and train machine learning models using TensorFlow and TPUs.
-
Troubleshoot and optimize models for improved performance.
One other activity
Expand to see all activities and additional details
Show all four activities
Follow tutorials on distributed machine learning
Show steps
Expand knowledge by exploring tutorials on distributed machine learning systems.
Show steps
-
Locate reputable tutorials and resources on distributed machine learning.
-
Follow the tutorials, implementing concepts and techniques in your own projects.
-
Experiment with different distributed machine learning frameworks and tools.
Revise Tensorflow
Show steps
TensorFlow e TPU são os principais componentes para a construção de modelos de aprendizado de máquina no Google Cloud. Revisar o TensorFlow ajudará a construir uma base sólida para o curso.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Revise os conceitos básicos do TensorFlow, como tensores, operações e gráficos de fluxo de dados.
- Pratique a criação e o treinamento de modelos simples do TensorFlow.
Engage in peer discussions on ML best practices
Show steps
Enhance understanding by exchanging ideas and discussing machine learning best practices with peers.
Show steps
Show steps
Show steps
- Join or create peer study groups or online forums.
- Actively participate in discussions, sharing insights and experiences.
- Seek feedback and constructive criticism on your own approaches.
Practice exercises on TensorFlow and TPUs
Show steps
Deepen your understanding of TensorFlow and TPUs through hands-on exercises.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Complete coding exercises and tutorials on TensorFlow and TPUs.
- Build and train machine learning models using TensorFlow and TPUs.
- Troubleshoot and optimize models for improved performance.
One other activity
Expand to see all activities and additional details
Show all four activities
Follow tutorials on distributed machine learning
Show steps
Expand knowledge by exploring tutorials on distributed machine learning systems.
Show steps
Show steps
Show steps
- Locate reputable tutorials and resources on distributed machine learning.
- Follow the tutorials, implementing concepts and techniques in your own projects.
- Experiment with different distributed machine learning frameworks and tools.
Career center
Learners who complete Production Machine Learning Systems - Português Brasileiro will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is responsible for designing, developing, and deploying machine learning models. This course can help you build a foundation in the principles and practices of machine learning, and gain experience with the tools and technologies used to develop and deploy machine learning models. The course covers a wide range of topics, including:
1. Introduction to machine learning
2. Machine learning algorithms
3. Model training and evaluation
4. Model deployment
5. Best practices for machine learning development.
Data Scientist
Data Scientist
A Data Scientist is responsible for collecting, analyzing, and interpreting data to extract insights and make predictions. This course can help you build a foundation in the principles and practices of data science, and gain experience with the tools and technologies used to collect, analyze, and interpret data. The course covers a wide range of topics, including:
1. Data collection and preprocessing
2. Data analysis and visualization
3. Machine learning algorithms
4. Model training and evaluation
5. Best practices for data science.
Software Engineer
Software Engineer
A Software Engineer is responsible for designing, developing, and maintaining software systems. This course can help you build a foundation in the principles and practices of software engineering, and gain experience with the tools and technologies used to design, develop, and maintain software systems. The course covers a wide range of topics, including:
1. Software design and architecture
2. Software development methodologies
3. Software testing and debugging
4. Best practices for software engineering.
Product Manager
Product Manager
A Product Manager is responsible for defining, developing, and launching new products. This course can help you build a foundation in the principles and practices of product management, and gain experience with the tools and technologies used to define, develop, and launch new products. The course covers a wide range of topics, including:
1. Product definition and strategy
2. Product development
3. Product marketing
4. Best practices for product management
Business Analyst
Business Analyst
A Business Analyst is responsible for analyzing business processes and identifying opportunities for improvement. This course can help you build a foundation in the principles and practices of business analysis, and gain experience with the tools and technologies used to analyze business processes and identify opportunities for improvement. The course covers a wide range of topics, including:
1. Business process analysis
2. Data analysis and visualization
3. Requirements gathering and analysis
4. Best practices for business analysis.
Data Analyst
Data Analyst
A Data Analyst is responsible for collecting, analyzing, and interpreting data to extract insights and make predictions. This course can help you build a foundation in the principles and practices of data analysis, and gain experience with the tools and technologies used to collect, analyze, and interpret data. The course covers a wide range of topics, including:
1. Data collection and preprocessing
2. Data analysis and visualization
3. Statistical methods
4. Best practices for data analysis.
Statistician
Statistician
A Statistician is responsible for collecting, analyzing, and interpreting data to draw conclusions about a population. This course can help you build a foundation in the principles and practices of statistics, and gain experience with the tools and technologies used to collect, analyze, and interpret data. The course covers a wide range of topics, including:
1. Statistical methods
2. Data analysis and visualization
3. Probability theory
4. Best practices for statistics.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst is responsible for using mathematical and analytical methods to solve business problems. This course can help you build a foundation in the principles and practices of operations research, and gain experience with the tools and technologies used to solve business problems. The course covers a wide range of topics, including:
1. Mathematical modeling
2. Optimization techniques
3. Simulation
4. Best practices for operations research.
Financial Analyst
Financial Analyst
A Financial Analyst is responsible for analyzing financial data and making recommendations on investments. This course may help you build a foundation in the principles and practices of financial analysis, and gain experience with the tools and technologies used to analyze financial data and make recommendations on investments. The course covers a wide range of topics, including:
1. Financial statement analysis
2. Valuation techniques
3. Investment analysis
4. Best practices for financial analysis.
Market Research Analyst
Market Research Analyst
A Market Research Analyst is responsible for conducting research to understand market trends and consumer behavior. This course may help you build a foundation in the principles and practices of market research, and gain experience with the tools and technologies used to conduct research to understand market trends and consumer behavior. The course covers a wide range of topics, including:
1. Market research methods
2. Data analysis and visualization
3. Marketing strategy
4. Best practices for market research.
User Experience Researcher
User Experience Researcher
A User Experience Researcher is responsible for conducting research to understand how users interact with products and services. This course may help you build a foundation in the principles and practices of user experience research, and gain experience with the tools and technologies used to conduct research to understand how users interact with products and services. The course covers a wide range of topics, including:
1. User research methods
2. Data analysis and visualization
3. Human-computer interaction
4. Best practices for user experience research.
Technical Writer
Technical Writer
A Technical Writer is responsible for writing and editing technical documentation. This course may help you build a foundation in the principles and practices of technical writing, and gain experience with the tools and technologies used to write and edit technical documentation. The course covers a wide range of topics, including:
1. Technical writing principles
2. Editing and proofreading
3. Document management
4. Best practices for technical writing.
Instructional Designer
Instructional Designer
An Instructional Designer is responsible for designing and developing educational materials. This course may help you build a foundation in the principles and practices of instructional design, and gain experience with the tools and technologies used to design and develop educational materials. The course covers a wide range of topics, including:
1. Learning theory
2. Instructional design models
3. Multimedia development
4. Best practices for instructional design.
Librarian
Librarian
A Librarian is responsible for managing and providing access to information. This course may help you build a foundation in the principles and practices of library science, and gain experience with the tools and technologies used to manage and provide access to information. The course covers a wide range of topics, including:
1. Library organization and management
2. Information retrieval
3. Reference services
4. Best practices for library science.
Archivist
Archivist
An Archivist is responsible for preserving and providing access to historical records. This course may help you build a foundation in the principles and practices of archival science, and gain experience with the tools and technologies used to preserve and provide access to historical records. The course covers a wide range of topics, including:
1. Archival theory and practice
2. Preservation and conservation
3. Access and outreach
4. Best practices for archival science.
For more career information including salaries, visit:
OpenCourser.com/course/i2qzt0/production
Reading list
We've selected 12 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
Production Machine Learning Systems - Português Brasileiro .
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, from the basics of neural networks to the latest advances in the field.
Provides a comprehensive introduction to deep learning with Python. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks.
Provides a gentle introduction to deep learning. It great resource for learners who are new to machine learning or for those who want to learn more about deep learning.
Provides a comprehensive introduction to statistical learning with sparsity. It covers a wide range of topics, including the LASSO, the elastic net, and group LASSO.
Este livro fornece uma introdução ao aprendizado por reforço. Ele cobre uma ampla gama de tópicos, desde os fundamentos do aprendizado por reforço até os algoritmos mais recentes.
Provides a practical guide to machine learning using Python. It covers a wide range of topics, from data preprocessing to model evaluation.
Provides a comprehensive introduction to machine learning with R. It covers a wide range of topics, including data preprocessing, model selection, and model evaluation.
Provides a comprehensive introduction to machine learning with Python. It covers a wide range of topics, including data preprocessing, model selection, and model evaluation.
Provides a comprehensive introduction to probabilistic graphical models. It covers a wide range of topics, including Bayesian networks, Markov random fields, and latent Dirichlet allocation.
Provides a comprehensive introduction to information theory, inference, and learning algorithms. It covers a wide range of topics, including entropy, mutual information, and Bayesian inference.
Provides a comprehensive introduction to convex optimization. It covers a wide range of topics, including linear programming, semidefinite programming, and conic programming.
Provides a practical guide to machine learning for non-experts. It covers a wide range of topics, from data collection to model deployment.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/i2qzt0/production
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Effort
5 - 7 hours per week
Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
Português
Transcript
English
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