Production Machine Learning Systems - Português Brasileiro from Coursera

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.

Como arquitetar sistemas de ML de produção

Neste módulo, você vai entender o que um sistema de ML de produção precisa fazer e como atender a essas necessidades. Você também vai aprender a tomar decisões de design importantes e de alto nível relacionadas a treinamentos e disponibilização de modelos para conseguir o perfil de desempenho certo para seu modelo.

Como projetar sistemas de ML adaptáveis

Neste módulo, você vai aprender a reconhecer as formas como o modelo fica dependente dos dados, tomar decisões de engenharia com foco nos custos, saber quando reverter os modelos para versões anteriores, depurar as causas do comportamento de um modelo observado e implementar um pipeline que seja imune a um tipo de dependência.

Como projetar sistemas de ML com alto desempenho

Neste módulo, você vai identificar as considerações sobre desempenho para modelos de machine learning. Os modelos de machine learning não são todos iguais. Em alguns modelos, o foco é melhorar o desempenho de E/S. Em outros, o objetivo é otimizar a velocidade de computação.

Como criar sistemas de ML híbridos

Entender as ferramentas e os sistemas disponíveis e quando usar modelos híbridos de machine learning.

Resumo

Links dos PDFs de todos os módulos

Good to know

Know what's good

, what to watch for

, 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

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

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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

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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

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Enhance understanding by exchanging ideas and discussing machine learning best practices with peers.

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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

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Deepen your understanding of TensorFlow and TPUs through hands-on exercises.

Browse courses on TensorFlow

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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

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Follow tutorials on distributed machine learning

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Expand knowledge by exploring tutorials on distributed machine learning systems.

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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

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Software Engineer

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

See salaries and explore the career path for Product Manager

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.

See salaries and explore the career path for Business 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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Operations Research 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.

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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.

See salaries and explore the career path for Market Research Analyst

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.

See salaries and explore the career path for User Experience Researcher

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.

See salaries and explore the career path for Technical Writer

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.

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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.

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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.

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