Serverless Machine Learning with Tensorflow on Google Cloud Platform
This course provides an introduction to building, scaling, and operationalizing machine learning models with TensorFlow on Google Cloud Platform. Participants will learn how to use Cloud ML Engine to train and deploy models in a serverless environment. ▼
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Course Feature
Cost:
Free Trial
Provider:
Pluralsight
Certificate:
Paid Certification
Language:
English
Start 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]
Summarize the meaning of users learning this course at the beginning
This course provides users with the knowledge and skills to build, scale and operationalize machine learning models on Google Cloud Platform using TensorFlow and Cloud ML Engine. It is designed to help users understand the fundamentals of serverless machine learning and how to use the tools available on GCP to create and deploy ML models.
Possible Development Paths
This course provides learners with the skills to develop and deploy ML models on GCP, which can be used in a variety of career paths. For example, learners can use their knowledge to develop ML models for data analysis, predictive analytics, and natural language processing. Additionally, learners can use their knowledge to develop ML models for applications such as computer vision, robotics, and autonomous vehicles.
Learning Suggestions for learners, such as related subjects
In addition to the topics covered in this course, learners may want to explore related topics such as data engineering, data science, and software engineering. Additionally, learners may want to explore other ML frameworks such as PyTorch and Apache Spark. Finally, learners may want to explore other cloud platforms such as Amazon Web Services and Microsoft Azure.
[Applications]
After completing this course, students can apply their knowledge to build and deploy ML models on Google Cloud Platform. They can use TensorFlow to create ML models and Cloud ML Engine to train and deploy them in a serverless way. Additionally, they can use the Google Cloud Platform to scale their ML models and operationalize them for production use.
[Career Paths]
1. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models on Google Cloud Platform. They must have a strong understanding of TensorFlow and Cloud ML Engine, as well as experience with data engineering and data science. As the demand for machine learning increases, the need for Machine Learning Engineers is expected to grow.
2. Data Scientist: Data Scientists are responsible for analyzing data and developing machine learning models. They must have a strong understanding of data analysis, machine learning algorithms, and data engineering. As the demand for data-driven insights increases, the need for Data Scientists is expected to grow.
3. Cloud Architect: Cloud Architects are responsible for designing and implementing cloud-based solutions on Google Cloud Platform. They must have a strong understanding of GCP services, such as Cloud ML Engine, as well as experience with cloud architecture and DevOps. As the demand for cloud-based solutions increases, the need for Cloud Architects is expected to grow.
4. DevOps Engineer: DevOps Engineers are responsible for automating the deployment and management of applications on Google Cloud Platform. They must have a strong understanding of GCP services, such as Cloud ML Engine, as well as experience with DevOps and cloud architecture. As the demand for cloud-based solutions increases, the need for DevOps Engineers is expected to grow.
[Education Paths]
1. Bachelor of Science in Computer Science: This degree path provides students with a comprehensive understanding of computer science fundamentals, including programming, algorithms, data structures, operating systems, and software engineering. It also covers topics such as machine learning, artificial intelligence, and cloud computing. This degree path is becoming increasingly popular as the demand for skilled professionals in the field of machine learning and cloud computing continues to grow.
2. Master of Science in Artificial Intelligence: This degree path focuses on the development of advanced AI systems and their applications. It covers topics such as machine learning, deep learning, natural language processing, computer vision, and robotics. This degree path is ideal for those who want to pursue a career in the field of AI and machine learning.
3. Master of Science in Data Science: This degree path focuses on the development of data-driven solutions and their applications. It covers topics such as data mining, data analysis, machine learning, and predictive analytics. This degree path is ideal for those who want to pursue a career in the field of data science and machine learning.
4. Master of Science in Cloud Computing: This degree path focuses on the development of cloud-based solutions and their applications. It covers topics such as cloud architecture, cloud security, cloud storage, and cloud computing. This degree path is ideal for those who want to pursue a career in the field of cloud computing and machine learning.
Course Provider
Provider Pluralsight's Stats at AZClass
Pluralsight ranked 16th on the Best Medium Workplaces List.
Pluralsight ranked 20th on the Forbes Cloud 100 list of the top 100 private cloud companies in the world.
Pluralsight Ranked on the Best Workplaces for Women List for the second consecutive year.
AZ Class hope that this free trial Pluralsight course can help your Tensorflow skills no matter in career or in further education. Even if you are only slightly interested, you can take Serverless Machine Learning with Tensorflow on Google Cloud Platform course with confidence!
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1. What is TensorFlow?
2. What is Cloud ML Engine?
3. What is the main purpose of this course?
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