Smart Analytics, Machine Learning, and AI on Google Cloud (SAMLAI)

Total time
Location
At location
Starting date and place

Smart Analytics, Machine Learning, and AI on Google Cloud (SAMLAI)

Fast Lane Institute for Knowledge Transfer GmbH
Logo Fast Lane Institute for Knowledge Transfer GmbH
Provider rating: starstarstarstarstar_half 8.9 Fast Lane Institute for Knowledge Transfer GmbH has an average rating of 8.9 (out of 32 reviews)

Need more information? Get more details on the site of the provider.

Starting dates and places

placeHamburg
29 Aug 2024
placeMünchen
7 Nov 2024

Description

Voraussetzungen

Participants should have completed the Google Cloud Big Data and Machine Learning Fundamentals course or have equivalent experience.

Zielgruppe

Data Engineers

Detaillierter Kursinhalt

Module 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 …

Read the complete description

Frequently asked questions

There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.

Didn't find what you were looking for? See also: Artificial Intelligence, Cloud Computing, Web Analytics, IT Security, and Web Accessibility.

Voraussetzungen

Participants should have completed the Google Cloud Big Data and Machine Learning Fundamentals course or have equivalent experience.

Zielgruppe

Data Engineers

Detaillierter Kursinhalt

Module 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.
There are no reviews yet.

Share your review

Do you have experience with this course? Submit your review and help other people make the right choice. As a thank you for your effort we will donate $1.- to Stichting Edukans.

There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.