End-to-End Machine Learning with TensorFlow on GCP faq

learnersLearners:
instructor Instructor: Google Cloud instructor-icon
duration Duration: 4.00 instructor-icon

This course provides an in-depth exploration of End-to-End Machine Learning with TensorFlow on Google Cloud Platform. Participants will learn to build an end-to-end model from data exploration to deploying an ML model and obtaining predictions.

Course Feature Course Overview Course Provider
Go to class

Course Feature

costCost:

Free Trial

providerProvider:

Pluralsight

certificateCertificate:

Paid Certification

languageLanguage:

English

start dateStart Date:

On-Demand

Course Overview

❗The content presented here is sourced directly from Pluralsight platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [March 06th, 2023]

(Please note the following content is from the official provider.)
This course is set up as a workshop where you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform. It involves building an end-to-end model from data exploration all the way to deploying an ML model and getting predictions from it.
One of the best ways to review something is to work with the concepts and technologies that you have learned. So, this course is set up as a workshop and in this workshop, you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform. It involves building an end-to-end model from data exploration all the way to deploying an ML model and getting predictions from it.
(Please note that we obtained the following content based on information that users may want to know, such as skills, applicable scenarios, future development, etc., combined with AI tools, and have been manually reviewed)
Learners can learn the following from this course:

1. Data Exploration: Learners will gain an understanding of the data exploration process, including data cleaning, feature engineering, and data visualization.

2. Machine Learning: Learners will learn how to build and deploy ML models using TensorFlow on GCP. They will also learn how to evaluate the performance of the models and tune them for better results.

3. Deployment: Learners will learn how to deploy ML models on GCP and get predictions from them. They will also learn how to monitor and maintain the models in production.

4. Best Practices: Learners will gain an understanding of best practices for ML development, such as model selection, hyperparameter tuning, and model evaluation. They will also learn how to use MLOps to automate the ML development process.

[Applications]
After completing this course, participants are encouraged to apply the concepts and technologies learned to their own projects. They can use the Google Cloud Platform to build and deploy their own ML models, and use TensorFlow to explore and analyze data. Additionally, they can use the techniques learned in this course to create their own end-to-end ML models.

[Career Paths]
1. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models. They are responsible for designing, building, and testing machine learning models, as well as deploying them into production. They must have a strong understanding of the underlying algorithms and technologies used in machine learning, as well as the ability to develop and deploy models in a production environment. The demand for Machine Learning Engineers is growing rapidly, as more companies are looking to leverage the power of machine learning to improve their products and services.

2. Data Scientist: Data Scientists are responsible for analyzing large datasets and extracting insights from them. They must have a strong understanding of data analysis techniques, as well as the ability to interpret and communicate the results of their analysis. Data Scientists are also responsible for developing predictive models and deploying them into production. The demand for Data Scientists is also growing rapidly, as more companies are looking to leverage the power of data to improve their products and services.

3. Artificial Intelligence Engineer: Artificial Intelligence Engineers are responsible for developing and deploying AI-based solutions. They must have a strong understanding of the underlying algorithms and technologies used in AI, as well as the ability to develop and deploy AI-based solutions in a production environment. The demand for Artificial Intelligence Engineers is growing rapidly, as more companies are looking to leverage the power of AI to improve their products and services.

4. Cloud Computing Engineer: Cloud Computing Engineers are responsible for developing and deploying cloud-based solutions. They must have a strong understanding of the underlying technologies used in cloud computing, as well as the ability to develop and deploy cloud-based solutions in a production environment. The demand for Cloud Computing Engineers is also growing rapidly, as more companies are looking to leverage the power of cloud computing to improve their products and services.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree path focuses on the fundamentals of computer science, such as programming, algorithms, data structures, and software engineering. It also covers topics such as artificial intelligence, machine learning, and cloud computing. This degree path is becoming increasingly popular as the demand for skilled professionals in the field of computer science continues to grow.

2. Master of Science in Artificial Intelligence: This degree path focuses on the development of intelligent systems and their applications. It covers topics such as natural language processing, computer vision, robotics, and machine learning. This degree path is becoming increasingly popular as the demand for skilled professionals in the field of artificial intelligence continues to grow.

3. Master of Science in Data Science: This degree path focuses on the analysis and interpretation of large datasets. It covers topics such as data mining, machine learning, and predictive analytics. This degree path is becoming increasingly popular as the demand for skilled professionals in the field of data science continues to grow.

4. Master of Science in Cloud Computing: This degree path focuses on the development and deployment of cloud-based applications. It covers topics such as distributed computing, cloud security, and cloud storage. This degree path is becoming increasingly popular as the demand for skilled professionals in the field of cloud computing continues to grow.

Course Provider

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