Google Cloud
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
Google Cloud

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

Advance your career as a Cloud ML Engineer

Taught in English

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Professional Certificate - 8 course series

Earn a career credential that demonstrates your expertise

4.6

(1,956 reviews)

Intermediate level

Recommended experience

2 months at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Learn the skills needed to be successful in a machine learning engineering role

  • Prepare for the Google Cloud Professional Machine Learning Engineer certification exam

  • Understand how to design, build, productionalize ML models to solve business challenges using Google Cloud technologies

  • Understand the purpose of the Professional Machine Learning Engineer certification and its relationship to other Google Cloud certifications

Details to know

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Recently updated!

February 2024

Professional Certificate - 8 course series

Earn a career credential that demonstrates your expertise

4.6

(1,956 reviews)

Intermediate level

Recommended experience

2 months at 10 hours a week
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Advance your career with in-demand skills

  • Receive professional-level training from Google Cloud
  • Demonstrate your technical proficiency
  • Earn an employer-recognized certificate from Google Cloud
  • Prepare for an industry certification exam
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Professional Certificate - 8 course series

Introduction to AI and Machine Learning on Google Cloud

Course 19 hours4.9 (21 ratings)

What you'll learn

  • Recognize the data-to-AI technologies and tools offered by Google Cloud.

  • Use generative AI capabilities in applications.

  • Choose between different options to develop an AI project on Google Cloud.

  • Build ML models end-to-end by using Vertex AI.

Launching into Machine Learning

Course 214 hours4.6 (4,273 ratings)

What you'll learn

  • Describe how to improve data quality and perform exploratory data analysis

  • Build and train AutoML Models using Vertex AI and BigQuery ML

  • Optimize and evaluate models using loss functions and performance metrics

  • Create repeatable and scalable training, evaluation, and test datasets

TensorFlow on Google Cloud

Course 313 hours4.4 (2,745 ratings)

What you'll learn

  • Create TensorFlow and Keras machine learning models and describe their key components.

  • Use the tf.data library to manipulate data and large datasets.

  • Use the Keras Sequential and Functional APIs for simple and advanced model creation.

  • Train, deploy, and productionalize ML models at scale with Vertex AI.

Skills you'll gain

Category: Tensorflow
Category: Machine Learning
Category: Cloud Computing

Feature Engineering

Course 48 hours4.5 (1,750 ratings)

What you'll learn

  • Describe Vertex AI Feature Store and compare the key required aspects of a good feature.

  • Perform feature engineering using BigQuery ML, Keras, and TensorFlow.

  • Discuss how to preprocess and explore features with Dataflow and Dataprep.

  • Use tf.Transform.

Machine Learning in the Enterprise

Course 518 hours4.6 (1,447 ratings)

What you'll learn

  • Describe data management, governance, and preprocessing options

  • Identify when to use Vertex AutoML, BigQuery ML, and custom training

  • Implement Vertex Vizier Hyperparameter Tuning

  • Explain how to create batch and online predictions, setup model monitoring, and create pipelines using Vertex AI

Production Machine Learning Systems

Course 618 hours4.6 (969 ratings)

What you'll learn

  • Compare static versus dynamic training and inference

  • Manage model dependencies

  • Set up distributed training for fault tolerance, replication, and more

  • Export models for portability

Machine Learning Operations (MLOps): Getting Started

Course 72 hours4.0 (416 ratings)

What you'll learn

  • Identify and use core technologies required to support effective MLOps.

  • Adopt the best CI/CD practices in the context of ML systems.

  • Configure and provision Google Cloud architectures for reliable and effective MLOps environments.

  • Implement reliable and repeatable training and inference workflows.

Skills you'll gain

Category: Tensorflow
Category: Python Programming
Category: Machine Learning
Category: keras
Category: Build Input Data Pipeline

ML Pipelines on Google Cloud

Course 810 hours3.4 (74 ratings)

What you'll learn

Skills you'll gain

Category: Tensorflow
Category: Bigquery
Category: Machine Learning
Category: Data Cleansing

Instructor

Google Cloud Training
Google Cloud
1,304 Courses2,483,915 learners

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Google Cloud

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