Author
Released
10/23/2018- Hosting options: Serverless, containers, and virtual machines
- Enabling the GCP ML AIs
- Preparing data with Cloud Dataflow and Dataprep
- Modeling predictions for images, video, text to speech, and cloud translation
- Machine learning with AutoML
- Advanced machine learning and deep learning
- Machine learning architectures
Skill Level Intermediate
Duration
Views
- [Lynn] Have you been thinking about using machine learning in your application? For example, have you been considering the impact of categorizing the input from your customers, images or text, and grouping the categories into priorities so that you could take action faster? We'll cover problems such as selecting from a large number of machine learning services, evaluating if models are serverless, on containers or on virtual machines and identifying how to build complete solutions which include machine learning services.
Hi, I'm Lynn Langit, a cloud architect who builds machine learning solutions. We have lots to cover, so let's get started.
Related Courses
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Introduction
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What you should know1m 13s
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About using cloud services1m 33s
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1. Machine Learning on Google Cloud Platform
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GCP AI servers vs. platforms5m 11s
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Enable GCP ML APIs4m 25s
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2. Machine Learning API Services
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Overview of GCP ML APIs2m 38s
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Predict via BigQuery ML6m 29s
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3. Machine Learning with AutoML
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Understand AutoML Vision4m 57s
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4. Advanced Machine Learning
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Why build custom ML models?6m 59s
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Use Cloud ML Engine8m 2s
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Scale custom ML models3m 47s
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Understanding deep learning4m 33s
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Work with TensorBoard4m 45s
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GPUs and TPUs for TensorFlow5m 21s
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5. Machine Learning Architectures
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Chatbot with ML2m 42s
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GCP ML service for IoT apps3m 10s
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Conclusion
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Next steps1m 30s
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Video: Build complete solutions with machine learning services