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.
About this Course
What you will learn
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.
Syllabus - What you will learn from this course
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
- 5 stars69.02%
- 4 stars23.97%
- 3 stars4.43%
- 2 stars1.45%
- 1 star1.10%
TOP REVIEWS FROM SMART ANALYTICS, MACHINE LEARNING, AND AI ON GOOGLE CLOUD
It was a good decision to do this course as i learn and practiced lot in GCP. Thank you the team for amazing support guidance and instructions. Course content and material was appreciated. Thanks.
Very good course to experience all the diverse offerings for ML on GCP.
A general introduction for enabling data engineer start working on GCP
Good structure and overview of things which can be accomplished in GCP Analytics
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