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AI Platform
Qwik Start
Google Cloud Training
Google Cloud Training
This is a self-paced lab that takes place in the Google Cloud console. In this lab you train and deploy a TensorFlow model to AI Platform for serving (prediction). Watch these short videos Harness the Power of Machine Learning with AI Platform and AI Platform: Qwik Start - Qwiklabs Preview.
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What's inside
Syllabus
AI Platform: Qwik Start
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Exposes learners to TensorFlow, a popular machine learning library, which is standard in the data science industry
Teaches learners how to deploy machine learning models to AI Platform for serving (prediction), a valuable skill in modern data science
Offers self-paced labs, a convenient and flexible learning format that accommodates diverse learner schedules
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AI Platform: Qwik Start.
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Career center
Learners who complete AI Platform: Qwik Start will develop knowledge and skills
that may be useful to these careers:
AI Engineer
AI Engineer
AI Engineers design, build, and maintain AI systems and applications. They work with data scientists and machine learning engineers to bring AI solutions to life. AI Platform: Qwik Start helps build a foundation for aspiring AI Engineers by introducing fundamental concepts for training and deploying prediction models.
Cloud Architect
Cloud Architect
Cloud Architects design and manage cloud computing systems. They work with businesses to migrate their applications and data to the cloud. AI Platform: Qwik Start helps build a foundation for aspiring Cloud Architects by introducing foundational concepts for deploying AI models in the cloud.
Product Manager
Product Manager
Product Managers plan and oversee the development of products. They work with engineers, designers, and other stakeholders to ensure that products meet the needs of users. AI Platform: Qwik Start helps build a foundation for aspiring Product Managers who wish to develop AI-powered products by introducing concepts for training and deploying prediction models.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines that collect, store, and process data. AI Platform: Qwik Start may be useful for Data Engineers by introducing foundational concepts for training and deploying prediction models that can be used in data pipelines.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. Many Software Engineers specialize in developing AI-powered applications. AI Platform: Qwik Start could help Software Engineers gain the fundamental knowledge necessary to develop and deploy models for prediction.
DevOps Engineer
DevOps Engineer
DevOps Engineers work to bridge the gap between development and operations teams. They ensure that software is deployed and maintained efficiently. AI Platform: Qwik Start could help DevOps Engineers gain foundational knowledge for working with AI models in production.
Risk Analyst
Risk Analyst
Risk Analysts assess and manage risks for businesses. AI Platform: Qwik Start may be useful for Risk Analysts by introducing foundational concepts for training and deploying prediction models that can be used to assess and manage risks.
Data Scientist
Data Scientist
In the role of Data Scientist, one would collect and analyze both structured and unstructured data. Using this data, they make predictions and create models that solve business problems. AI Platform: Qwik Start may be useful for a Data Scientist because it introduces foundational concepts for training and deploying prediction models.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models to solve complex business challenges. AI Platform: Qwik Start may be useful for a Machine Learning Engineer by introducing foundational concepts for training and deploying prediction models.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data and make recommendations for investments and other financial decisions. AI Platform: Qwik Start may be useful for Financial Analysts by introducing foundational concepts for training and deploying prediction models that can be used to analyze financial data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze data and make predictions. AI Platform: Qwik Start may be useful for Quantitative Analysts by introducing foundational concepts for training and deploying prediction models.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess and manage risks for insurance companies and other financial institutions. AI Platform: Qwik Start may be useful for Actuaries by introducing foundational concepts for training and deploying prediction models that can be used to assess and manage risks.
Business Analyst
Business Analyst
Business Analysts help businesses understand their data and make better decisions. AI Platform: Qwik Start could help aspiring Business Analysts learn the basics of training and deploying AI models for solving business problems.
Project Manager
Project Manager
Project Managers plan and oversee the execution of projects. They work with stakeholders to define project scope, timelines, and budgets. AI Platform: Qwik Start could be useful for Project Managers who are working on AI-related projects by introducing foundational concepts for training and deploying prediction models.
Data Analyst
Data Analyst
Data Analysts clean, analyze, and interpret data to help businesses make informed decisions. AI Platform: Qwik Start may be useful to Data Analysts by introducing foundational concepts for training and deploying models that help automate parts of their workflow.
For more career information including salaries, visit:
OpenCourser.com/course/1v8ifo/ai
Reading list
We've selected 11 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
AI Platform: Qwik Start.
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Comprehensive reference for deep learning, covering both theoretical and practical aspects. It provides a detailed overview of deep learning architectures, training algorithms, and applications, offering a thorough foundation for understanding the field and its relevance to AI Platform.
Offers a comprehensive treatment of pattern recognition and machine learning, covering both theoretical foundations and practical applications. It provides a thorough understanding of the underlying principles and algorithms used in AI Platform, offering a deep dive into the field.
Classic reference for statistical learning, providing a comprehensive overview of the field. It covers both theoretical foundations and practical applications, offering insights into the statistical principles and techniques used in AI Platform, making it a valuable resource for understanding the underlying statistical concepts.
Offers a comprehensive overview of artificial intelligence (AI), covering a wide range of topics from foundations to advanced techniques. It provides a comprehensive introduction to the field of AI, offering a broad understanding of the concepts and principles that underpin AI Platform.
Presents a probabilistic approach to machine learning, emphasizing the mathematical foundations and statistical principles behind machine learning models. It provides a comprehensive treatment of probabilistic modeling and inference, offering a deeper understanding of AI Platform's underlying probabilistic nature.
Focuses specifically on deep learning and provides detailed guidance on building and training neural networks using TensorFlow. It includes numerous code examples and practical insights, making it a valuable resource for understanding the underlying principles of deep learning.
Provides a comprehensive introduction to Bayesian reasoning and its applications in machine learning. It covers the theoretical foundations of Bayesian probability, as well as practical techniques for building and evaluating Bayesian models, offering a deeper understanding of the probabilistic underpinnings of AI Platform.
Offers a practical approach to machine learning, covering a wide range of topics from data manipulation to model deployment. It emphasizes hands-on learning through interactive exercises and code examples using popular libraries like Scikit-Learn, Keras, and TensorFlow.
Provides a comprehensive introduction to machine learning, covering both supervised and unsupervised learning algorithms. It includes theoretical foundations, practical examples, and discussion of real-world applications, offering a well-rounded understanding of machine learning concepts.
Classic reference for reinforcement learning, providing a comprehensive overview of the field. It covers both theoretical foundations and practical applications, offering insights into how reinforcement learning can be used to solve complex problems.
Offers a practical and accessible introduction to machine learning, with a focus on hands-on coding and real-world examples. It provides a quick start for learners who want to get up and running with machine learning quickly, offering a practical foundation for using AI Platform.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1v8ifo/ai
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Effort
1 hour
Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Exposes learners to TensorFlow, a popular machine learning library, which is standard in the data science industry
Teaches learners how to deploy machine learning models to AI Platform for serving (prediction), a valuable skill in modern data science
Offers self-paced labs, a convenient and flexible learning format that accommodates diverse learner schedules
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