Course
Digicomp Code SMARTA
Smart Analytics, Machine Learning, and AI on Google Cloud («SMARTA»)
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.
Duration
1 day
Price
850.–
Course documents
Official Google Cloud courseware
Course facts
- Differentiating between ML, AI and deep learning
- Discussing the use of ML API’s on unstructured data
- Executing BigQuery commands from notebooks
- Creating ML models by using SQL syntax in BigQuery
- Creating ML models without coding by using AutoML
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.
1 Introduction to Analytics and AI
- What is AI?
- From ad hoc data analysis to data-driven decisions
- Options for ML models on Google Cloud
- Describe the relationship between ML, AI, and deep learning
- Identify ML options on Google Cloud
2 Prebuilt ML Model APIs for Unstructured Data
- The difficulties of unstructured data
- ML APIs for enriching data
- Discuss challenges when working with unstructured data
- Identify ready-to-use ML API’s for unstructured data
3 Big Data Analytics with Notebooks
- Defining notebooks
- BigQuery magic and ties to Pandas
- Introduce notebooks as a tool for prototyping ML solutions.
- Execute BigQuery commands from notebooks
4 Production ML Pipelines
- Ways to do ML on Google Cloud
- Vertex AI Pipelines
- TensorFlow Hub
- Describe options available for building custom ML models.
- Describe the use of tools like Vertex AI and TensorFlow Hub
5 Custom Model Building with SQL in BigQuery ML
- BigQuery ML for quick model building
- Supported models
- Create ML models by using SQL syntax in BigQuery
- Demonstrate building different kinds of ML models by using BigQuery ML
6 Custom Model Building with AutoML
- Why use AutoML?
- AutoML Vision
- AutoML NLP
- AutoML Tables
- Explore various AutoML products used in machine learning
- Identify ready-to-use ML API’s for unstructured data
Data Engineers
Participants should have completed the Google Cloud Big Data and Machine Learning Fundamentals course or have equivalent experience.
Products
- Cloud Natural Language API
- Vertex AI
- Vertex AI Pipelines
- AI Platform
- BigQuery ML
- AutoML