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Google Cloud
This course covers designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML models.
This course covers designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML models.
This course is no longer available. Find something similar by browsing:
TensorFlow
Machine Learning
Keras
Data Pipelines
Google Cloud
Vertex AI
Neural Networks
Register for this course and see more details by visiting:
OpenCourser.com/course/ks350n/tensorflow
What's inside
Syllabus
Introduction to the Course
Introduction to the TensorFlow Ecosystem
Design and Build an Input Data Pipeline
Building Neural Networks with the TensorFlow and Keras API
Read more
Syllabus
Introduction to the Course
Introduction to the TensorFlow Ecosystem
Design and Build an Input Data Pipeline
Building Neural Networks with the TensorFlow and Keras API
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Taught by Google Cloud, who are well-known for work in their field
Examines TensorFlow, Keras, and Vertex AI, which are highly relevant in industry
Develops experience with TensorFlow, which is a foundational technology and respected in industry
Covers building TensorFlow input data pipelines, which is a core skill in the field
Develops specialized ML models, which is a valuable skill for many technical roles
Teaches ML model accuracy improvement, which is critical in production use
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Reviews summary
Production-ready tensorflow on google cloud
According to learners, this course provides a largely positive and highly practical approach to deploying TensorFlow models on Google Cloud. Students consistently praise the hands-on labs, especially those focusing on Vertex AI, for solidifying their understanding of scalable ML. The course excels in explaining how to design and build efficient input data pipelines using `tf.data` and effectively utilize the Keras API for model development. While the instructor's explanations are often clear, some found the pace fast or noted that it assumes prior ML and GCP knowledge. Recent reviews sometimes mention minor discrepancies in lab environments due to evolving GCP interfaces, requiring slight self-debugging. Overall, it's considered invaluable for ML engineers seeking to productionize models.
Strong coverage of TensorFlow, Keras, and data pipelines.
"I especially appreciated the modules on data pipelines and specialized models."
"The focus on `tf.data` and Keras API was spot on."
"The section on Keras particularly clear."
"I learned so much about building efficient data pipelines with `tf.data` and how to train models using Vertex AI."
Equips learners with skills for deploying models at scale.
"...solidified my understanding of deploying and managing models at scale."
"This course significantly boosted my confidence in using TensorFlow in a production environment."
"The focus on `tf.data` pipelines and Keras was excellent. It provided a clear path to productionizing models."
"This course is a game-changer for anyone wanting to move their ML models to the cloud."
Reinforces concepts through highly valuable practical exercises.
"The hands-on labs using Vertex AI were incredibly helpful and really solidified my understanding of deploying and managing models at scale."
"Excellent practical course! I learned so much about building efficient data pipelines... The real-world examples were invaluable."
"The labs are well-designed and directly applicable. This course filled a significant gap in my knowledge regarding scalable TensorFlow deployment on GCP."
"The hands-on coding and projects are the strongest part of the course for me."
Course moves quickly, beneficial for experienced learners.
"The course covers a lot of ground quickly. It's a great overview of TensorFlow and its integration with Google Cloud."
"The pace is quite fast, assuming a strong background in both ML and GCP. Good for refreshing concepts if you already work with these tools."
Minor discrepancies may exist between course and current GCP UI.
"While the content is relevant, I encountered some issues with the lab environments not matching the video instructions exactly, which caused some frustration."
"Some of the material felt a bit dated, especially with the rapid evolution of TensorFlow. The practical exercises sometimes lagged behind the latest GCP UI."
"Expect to encounter minor discrepancies between the course material and the latest Google Cloud console interface. This requires some self-debugging, which can be time-consuming."
Requires existing background in ML and Google Cloud.
"The pace is quite fast, assuming a strong background in both ML and GCP. Not ideal for beginners..."
"Found this course very challenging. It jumps straight into complex topics without much foundational review. If you're not already highly proficient in TensorFlow and cloud platforms, you'll struggle."
"I struggled with the prerequisites. The course assumes significant prior knowledge of both machine learning and cloud infrastructure."
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 TensorFlow on Google Cloud with these
activities:
Develop a TensorFlow project
Show steps
Apply your TensorFlow skills to solve a real-world problem.
Show steps
-
Define a problem statement and identify the relevant dataset.
-
Design and implement a TensorFlow model to address the problem.
-
Train and evaluate the model, iteratively refining its performance.
-
Deploy the model and monitor its effectiveness.
Show all one activities
Develop a TensorFlow project
Show steps
Apply your TensorFlow skills to solve a real-world problem.
Show steps
Show steps
Show steps
- Define a problem statement and identify the relevant dataset.
- Design and implement a TensorFlow model to address the problem.
- Train and evaluate the model, iteratively refining its performance.
- Deploy the model and monitor its effectiveness.
Show all one activities
Career center
Learners who complete TensorFlow on Google Cloud will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist builds, trains, and evaluates machine learning models. To be successful, this career requires a strong foundation in designing and building data pipelines and ML models. This course provides the key knowledge for building and evaluating machine learning models with TensorFlow and Keras, which every Data Scientist must know.
Data Engineer
Data Engineer
A Data Engineer designs and builds scalable data pipelines to store, process, and analyze large datasets. To be successful, this career requires a deep understanding of data pipelines and how to build them in a scalable manner. This course helps build a foundation in designing and building scalable data pipelines using TensorFlow.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer deploys and maintains ML models in production. To be successful, this career requires a strong foundation in building and deploying ML models. This course helps build a foundation in building, deploying, and maintaining ML models with TensorFlow and Keras.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. To be successful, this career requires knowledge in software development and data science techniques. This course may be helpful in building a foundation in data science techniques and how to apply these techniques in software development.
ML Architect
ML Architect
A ML Architect designs and oversees the implementation of ML systems. To be successful, this career requires a deep understanding of ML models and how to design and implement them in a scalable manner. This course may be helpful in building a foundation in designing and implementing ML systems with TensorFlow.
Data Analyst
Data Analyst
A Data Analyst analyzes data to extract insights and make recommendations. To be successful, this career requires knowledge in data analysis techniques, including machine learning. This course may be helpful in building a foundation in machine learning techniques and how these techniques can be used to extract insights and make recommendations from data.
Big Data Engineer
Big Data Engineer
A Big Data Engineer designs and builds data pipelines to store and process large datasets. To be successful, this career requires a deep understanding of data pipelines and how to build them at scale. This course helps build a foundation in designing and building scalable data pipelines using TensorFlow.
Cloud Engineer
Cloud Engineer
A Cloud Engineer designs and manages cloud infrastructure. To be successful, this career requires knowledge in cloud computing and data science techniques. This course may be helpful in building a foundation in data science techniques and how to apply these techniques in cloud computing.
Business Analyst
Business Analyst
A Business Analyst analyzes business data to identify opportunities and solve problems. To be successful, this career requires knowledge in data analysis techniques, including machine learning. This course may be helpful in building a foundation in machine learning techniques and how these techniques can be used to analyze business data.
Product Manager
Product Manager
A Product Manager oversees the development and launch of software products. To be successful, this career requires knowledge in software development and data science techniques. This course may be helpful in building a foundation in data science techniques and how to apply these techniques in software product development.
Quant Analyst
Quant Analyst
A Quant Analyst applies mathematical and statistical techniques to analyze financial data. To be successful, this career requires a deep understanding of mathematical and statistical techniques, including machine learning. This course may be helpful in building a foundation in machine learning techniques and how these techniques can be used to analyze financial data.
Statistician
Statistician
A Statistician analyzes data to draw conclusions and make predictions. To be successful, this career requires a deep understanding of mathematical and statistical techniques, including machine learning. This course may be helpful in building a foundation in machine learning techniques and how these techniques can be used to analyze data and make predictions.
Data Visualization Engineer
Data Visualization Engineer
A Data Visualization Engineer designs and develops data visualizations to present data in a clear and concise manner. To be successful, this career requires knowledge in data visualization techniques and data science techniques. This course may be helpful in building a foundation in data science techniques and how to apply these techniques in data visualization.
Data Scientist (NLP)
Data Scientist (NLP)
A Data Scientist (NLP) specializes in applying machine learning techniques to natural language data. To be successful, this career requires a deep understanding of machine learning techniques and natural language processing techniques. This course may be helpful in building a foundation in machine learning techniques and how these techniques can be used to analyze natural language data.
Machine Learning Researcher
Machine Learning Researcher
A Machine Learning Researcher develops new and improved machine learning algorithms and techniques. To be successful, this career requires a deep understanding of machine learning theory and a strong foundation in mathematics and computer science. This course may be helpful in building a foundation in machine learning theory and how to apply this theory to develop new and improved machine learning algorithms and techniques.
For more career information including salaries, visit:
OpenCourser.com/course/ks350n/tensorflow
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
TensorFlow on Google Cloud.
Covers the fundamentals of deep learning and provides practical guidance on building and deploying deep learning models with Keras. A valuable resource for learners interested in applying deep learning to real-world problems.
Provides a practical guide to building and training deep learning models using TensorFlow 2 and Keras. It covers a wide range of topics, from data preprocessing to model evaluation.
Presents a coding-first approach to deep learning, leveraging the Fastai and PyTorch libraries. Suitable for learners with some programming experience who want to dive into practical ML model development.
Provides a comprehensive overview of ML concepts and algorithms from an R perspective. Valuable for learners who prefer R for data analysis and ML tasks or who are interested in comparing different programming languages for ML.
Offers a practical approach to ML model development using Python libraries. Provides hands-on examples and exercises that reinforce concepts covered in the course.
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Provides a concise introduction to TensorFlow and its applications in ML. Suitable for learners with limited programming experience or those seeking a quick overview of TensorFlow.
Introduces deep learning concepts through intuitive explanations and visual representations. Helpful for learners seeking a conceptual understanding of deep learning without excessive technical details.
Provides a gentle introduction to TensorFlow for beginners. It covers the basics of TensorFlow and how to use it to build and train simple neural networks.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ks350n/tensorflow
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