Smart Analytics, Machine Learning, and AI on Google Cloud

External: Coursera Courses ↗ · Coursera

Open Course on External: Coursera

Free to audit · Opens on External: Coursera

Smart Analytics, Machine Learning, and AI on Google Cloud

Coursera · Beginner ·🔄 Data Engineering ·3mo ago

Skills: ML Pipelines80%

Key Takeaways

Utilizes AutoML, Notebooks, and BigQuery machine learning on Google Cloud for data pipelines

Original Description

Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. 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 by using Vertex AI.

Watch on External: Coursera ↗ (saves to browser)

Sign in to unlock AI tutor explanation · ⚡30

More on: ML Pipelines

View skill →

Building a Dog Breed Identifier App from scratch - DogNet

Building a Dog Breed Identifier App from scratch - DogNet

Aladdin Persson

Complete Dockers For Data Science Tutorial In One Shot

Complete Dockers For Data Science Tutorial In One Shot

Part 6 | Deploy ML Model on Kubernetes | Auto-Scaling with HPA and Monitoring with Prometheus

Part 6 | Deploy ML Model on Kubernetes | Auto-Scaling with HPA and Monitoring with Prometheus

Abonia Sojasingarayar

Vertex Pipelines: Qwik Start

Coding a Multimodal (Vision) Language Model from scratch in PyTorch with full explanation

Coding a Multimodal (Vision) Language Model from scratch in PyTorch with full explanation

Automate R scripts with GitHub Actions: Deploy a model

Related AI Lessons

How I built the OSS alternatives directory: GitHub ETL, Turso, and the UPSERT trap I hit

Learn how to build a data pipeline for an open-source alternatives directory using GitHub ETL, Turso, and Claude Haiku summaries

Dev.to · MORINAGA

Apache Iceberg in Production: Compaction, Catalogs, and the Pitfalls Nobody Warns You About

Learn how to use Apache Iceberg in production, including compaction, catalogs, and common pitfalls to avoid, to improve data engineering workflows

Dev.to · Gabriel Henrique

Your First Task as a Data Engineer in a New Company? Make the ETL Pipeline Testable

As a new data engineer, make the ETL pipeline testable to ensure data quality and reliability

Towards Data Science

From DataStage and Informatica to Databricks Medallion Architecture: Why Migration Is More Than Code Conversion

Learn how to migrate legacy ETL systems like DataStage to modern architectures like Databricks Medallion, and why it's more than just code conversion

Dev.to · Amit Kumar Singh

A Moment Frozen in Time | Arnav Iyengar | TEDxJenks Youth