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Artificial Intelligence
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Grow with Google

Production Machine Learning Systems

  • up to 8 hours
  • Advanced

Explore the components and best practices for creating high-performance AI systems in production environments. This course will help you understand the key features of effective AI systems beyond just making accurate predictions.

  • Static training
  • Dynamic training
  • Static inference
  • Dynamic inference
  • TensorFlow

Overview

In this course, you will delve into the intricacies of building production-ready machine learning systems. Learn about static and dynamic training and inference, distributed TensorFlow, and TPU usage. Gain insights into model dependency management and exporting models for portability, equipping you with the skills to advance your career in cloud and AI technologies.

  • Web Streamline Icon: https://streamlinehq.com
    Online
    course location
  • Layers 1 Streamline Icon: https://streamlinehq.com
    Spanish
    course language
  • Professional Certification
    upon course completion
  • Self-paced
    course format
  • Live classes
    delivered online

Who is this course for?

Data Scientists

Professionals looking to enhance their skills in deploying machine learning models in production environments.

Machine Learning Engineers

Engineers aiming to understand the best practices for creating high-performance AI systems.

Cloud Professionals

Individuals interested in showcasing their cloud skills and advancing their careers in AI and machine learning.

This course offers a deep dive into the creation of high-performance AI systems, covering essential topics like TensorFlow and TPU usage. Ideal for advanced learners, it equips you with the skills to excel in cloud and AI technologies, enhancing your career prospects.

Pre-Requisites

1 / 3

  • Basic understanding of machine learning concepts

  • Familiarity with TensorFlow

  • Experience with cloud computing platforms

What will you learn?

Static and Dynamic Training and Inference
Comparison of static and dynamic training and inference methods in machine learning systems.
Model Dependency Management
Learn how to manage model dependencies effectively in production environments.
Distributed Training
Organize distributed training for fault tolerance, replication, and more.
Model Exportation
Techniques for exporting models to ensure portability across different platforms.

Upcoming cohorts

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Free