Smart Analytics, Machine Learning, and AI on Google CloudSAMLAIGOGoogleGO-SAMLAI1.0<ul>
<li>Differentiate between ML, AI and deep learning.</li><li>Discuss the use of ML API’s on unstructured data.</li><li>Execute BigQuery commands from notebooks.</li><li>Create ML models by using SQL syntax in BigQuery.</li><li>Create ML models without coding by using AutoML</li></ul><p>Participants should have completed the Google Cloud Big Data and Machine Learning Fundamentals course or have equivalent experience.</p><p>Data Engineers</p><h4>Module 1 - Introduction to Analytics and AI</h4><p>
<strong>Topics:</strong>
</p>
<ul>
<li>What is AI?</li><li>From ad hoc data analysis to data-driven decisions</li><li>Options for ML models on Google Cloud</li></ul><p><strong>Objectives:</strong>
</p>
<ul>
<li>Describe the relationship between ML, AI, and deep learning</li><li>Identify ML options on Google Cloud</li></ul><h4>Module 2 - Prebuilt ML Model APIs for Unstructured Data</h4><p>
<strong>Topics:</strong>
</p>
<ul>
<li>The difficulties of unstructured data</li><li>ML APIs for enriching data</li></ul><p><strong>Objectives:</strong>
</p>
<ul>
<li>Discuss challenges when working with unstructured data</li><li>Identify ready-to-use ML API’s for unstructured data</li></ul><h4>Module 3 - Big Data Analytics with Notebooks</h4><p>
<strong>Topics:</strong>
</p>
<ul>
<li>Defining notebooks</li><li>BigQuery magic and ties to Pandas</li></ul><p><strong>Objectives:</strong>
</p>
<ul>
<li>Introduce notebooks as a tool for prototyping ML solutions.</li><li>Execute BigQuery commands from notebooks.</li></ul><h4>Module 4 - Production ML Pipelines</h4><p>
<strong>Topics:</strong>
</p>
<ul>
<li>Ways to do ML on Google Cloud</li><li>Vertex AI Pipelines</li><li>TensorFlow Hub</li></ul><p><strong>Objectives:</strong>
</p>
<ul>
<li>Describe options available for building custom ML models.</li><li>Describe the use of tools like Vertex AI and TensorFlow Hub.</li></ul><h4>Module 5 - Custom Model Building with SQL in BigQuery ML</h4><p>
<strong>Topics:</strong>
</p>
<ul>
<li>BigQuery ML for quick model building</li><li>Supported models</li></ul><p><strong>Objectives:</strong>
</p>
<ul>
<li>Create ML models by using SQL syntax in BigQuery.</li><li>Demonstrate building different kinds of ML models by using BigQuery ML.</li></ul><h4>Module 6 - Custom Model Building with AutoML</h4><p>
<strong>Topics:</strong>
</p>
<ul>
<li>Why use AutoML?</li><li>AutoML Vision</li><li>AutoML NLP</li><li>AutoML Tables</li></ul><p><strong>Objectives:</strong>
</p>
<ul>
<li>Explore various AutoML products used in machine learning.</li><li>Identify ready-to-use ML API’s for unstructured data.</li></ul>- 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.
- Create ML models without coding by using AutoMLParticipants should have completed the Google Cloud Big Data and Machine Learning Fundamentals course or have equivalent experience.Data EngineersModule 1 - Introduction to Analytics and AI
Topics:
- What is AI?
- From ad hoc data analysis to data-driven decisions
- Options for ML models on Google Cloud
Objectives:
- Describe the relationship between ML, AI, and deep learning
- Identify ML options on Google Cloud
Module 2 - Prebuilt ML Model APIs for Unstructured Data
Topics:
- The difficulties of unstructured data
- ML APIs for enriching data
Objectives:
- Discuss challenges when working with unstructured data
- Identify ready-to-use ML API’s for unstructured data
Module 3 - Big Data Analytics with Notebooks
Topics:
- Defining notebooks
- BigQuery magic and ties to Pandas
Objectives:
- Introduce notebooks as a tool for prototyping ML solutions.
- Execute BigQuery commands from notebooks.
Module 4 - Production ML Pipelines
Topics:
- Ways to do ML on Google Cloud
- Vertex AI Pipelines
- TensorFlow Hub
Objectives:
- Describe options available for building custom ML models.
- Describe the use of tools like Vertex AI and TensorFlow Hub.
Module 5 - Custom Model Building with SQL in BigQuery ML
Topics:
- BigQuery ML for quick model building
- Supported models
Objectives:
- Create ML models by using SQL syntax in BigQuery.
- Demonstrate building different kinds of ML models by using BigQuery ML.
Module 6 - Custom Model Building with AutoML
Topics:
- Why use AutoML?
- AutoML Vision
- AutoML NLP
- AutoML Tables
Objectives:
- Explore various AutoML products used in machine learning.
- Identify ready-to-use ML API’s for unstructured data.1 Tag950.00950.00950.00790.00950.00