Incorporating machine learning into data pipelines increases the ability of businesses to extract insights from their data. This course covers several ways machine learning can be included in data pipelines on Google Cloud depending on the level of customization required. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions using Vertex AI. Learners will get hands-on experience building machine learning models on Google Cloud using QwikLabs.
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課程信息
對員工進行熱門技能培訓能否為您的公司帶來益處?
體驗 Coursera 企業版您將學到的內容有
Differentiate between ML, AI and Deep Learning.
Discuss the use of ML API’s on unstructured data.
Execute BigQuery commands from Notebooks.
Create ML models by using SQL syntax in BigQuery and without coding using AutoML.
對員工進行熱門技能培訓能否為您的公司帶來益處?
體驗 Coursera 企業版提供方
授課大綱 - 您將從這門課程中學到什麼
Introduction
Introduction to Analytics and AI
Prebuilt ML model APIs for Unstructured Data
Big Data Analytics with Notebooks
Production ML Pipelines
Custom Model building with SQL in BigQuery ML
Custom Model Building with AutoML
Summary
審閱
- 5 stars69.07%
- 4 stars23.88%
- 3 stars4.46%
- 2 stars1.46%
- 1 star1.11%
來自SMART ANALYTICS, MACHINE LEARNING, AND AI ON GOOGLE CLOUD的熱門評論
Great Big Picture about ML options on GCP, with good highlighting to main advantages and differences for each option.
Very good course to experience all the diverse offerings for ML on GCP.
This course helped me to do smart analytics, and in my current job I was able to apply Machine Learning easily on GCP, and I helped my team to the AI platform like experts.
Great insight about using machine learning on Google cloud platform. I am impressed
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