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Google Professional Machine Learning Engineer Training

Course Information

  • Sessions 4 days
  • Duration 30 hrs
  • Level Beginner
  • Assessment NA

Venue

Kuala Lumpur: G-3A-02, Suite Pejabat Korporat, KL Gateway, No 2, Jalan kerinchi, Gerbang kernichi Lestari, 59200 Kuala Lumpur, Malaysia
Penang: Jalan Sungai Dua, 11700 Penang, Malaysia.

Course Brochure

Download Course Brochure

Certification

By end of the course, learners should be able to:

  • LO1: Identify AI ethics and governance principles.
  • LO2: Apply AI ethics and governance framework on AI projects.

Google Professional Machine Learning Engineer Training

Course Code: C740

9 Review(s) | Add Your Review

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What's This Course About

Embark on a transformative journey towards becoming a Google Professional Machine Learning Engineer. Our comprehensive preparation course is meticulously designed to cover all the critical facets of machine learning, ensuring you gain the expertise needed to pass the certification exam confidently. By engaging with this course, you will dive deep into the development of scalable machine learning models, understanding complex data pipelines, and deploying robust ML projects using Google Cloud technologies. This certification signifies to employers that you possess the acumen to leverage machine learning in a way that drives powerful, innovative solutions.

This advanced training program goes beyond the fundamentals, providing insights into machine learning algorithms, model optimization, and problem-solving techniques crucial for real-world applications. You will learn how to approach machine learning engineering with an ethical and socially responsible lens while mastering the skills to build, test, and deploy AI systems that are scalable and reliable. Our curriculum is crafted to ensure that upon completion, you will not only be prepared for the Google Professional Machine Learning Engineer exam but also equipped to propel your career forward in the thriving field of AI and machine learning.

Bonus: Free Practice Exams

Get exam-ready on our Practice Exam Portal — train in realistic Practice Mode and timed Exam Mode, then retake them as many times as you like before the real exam.

Start Practising →

Course Fee

MYR1,400.00

Course Date

Course Time

Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Disclaimer: The course dates displayed on our website are tentative and subject to trainer availability. We will confirm the final date after checking with the trainer. You are also welcome to email us your preferred date at sales@tertiarycourses.com.my, and we will do our best to coordinate with the trainer's schedule.

Post-Course Support

  • We may provide consultation related to the subject matter after the course.
  • Please email your queries to sales@tertiarycourses.com.my and we will forward your queries to the subject matter experts and get back to you as soon as possible.

Cancellation & Reschedule Policy

  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
  • Note: the venue of the training is subject to changes due to class size and availability of the classroom. The minimum class size to start a class is 3 Pax.

Course Details

Course Details

What You'll Learn

Topic 1 Google Cloud Big Data and Machine Learning Fundamentals

  • Data-to-AI lifecycle on Google Cloud and the major products of big data and machine learning.
  • Design streaming pipelines with Dataflow and Pub/Sub and design streaming pipelines with Dataflow and Pub/Sub.
  • Options to build machine learning solutions on Google Cloud.
  • Machine learning workflow and the key steps with Vertex AI and build a machine learning pipeline using AutoML.

Topic 2 How Google does Machine Learning

  • Vertex AI Platform and how it's used to quickly build, train, and deploy AutoML machine learning models without writing any code
  • Best practices for implementing machine learning on Google Cloud
  • Leverage Google Cloud tools and environment to do ML
  • Responsible AI best practices

Topic 3 Launching into Machine Learning

  • 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

Topic 4 TensorFlow on Google Cloud

  • 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.

Topic 5 Feature Engineering

  • 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.

Topic 6 Machine Learning in the Enterprise

  • 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

Topic 7 Production Machine Learning Systems

  • Compare static versus dynamic training and inference
  • Manage model dependencies
  • Set up distributed training for fault tolerance, replication, and more
  • Export models for portability

Topic 8 Machine Learning Operations (MLOps)

  • 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.
  • ML Pipelines on Google Cloud

Course Info

Promotion Code

Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.

Minimum Entry Requirement

Knowledge and Skills

  • Able to operate using computer functions
  • Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

  • Positive Learning Attitude
  • Enthusiastic Learner

Experience

  • Minimum of 1 year of working experience.

Target Age Group: 18-65 years old

Minimum Software/Hardware Requirement

Software:

You can sign up for the following:

Hardware: Window or Mac Laptops

Job Roles

Job Roles

  • Data Scientist
  • Machine Learning Engineer
  • AI Engineer
  • Data Analyst
  • Software Engineer
  • Cloud Solutions Architect
  • Research Scientist
  • Application Developer
  • Big Data Engineer
  • Business Intelligence Developer
  • Robotics Engineer
  • Quantitative Analyst
  • Systems Analyst
  • Product Manager
  • Technical Program Manager

Trainers

Trainers

Saeid is co-founder of Skymics Sdn Bhd. He has 8 years of experience in the field of IoT (Internet of Things) and Information Technology. He is a certified IBM IoT Practitioner and instructor, and a Certified Citizen Data Scientist Train-The-Trainer. He has been co-inventor of 3 inventions during the last 4 years.

Review

Customer Reviews (9)

Hands-on and practical Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Great course materials and well-paced lessons. The exercises really helped me understand the topic. (Posted on 05/09/2024)
Clear and easy to follow Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Excellent delivery and good balance between theory and practice. Learned a lot in a short time. (Posted on 16/07/2024)
Learned a lot Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Well organised course with clear objectives. The step-by-step approach made it easy to follow. (Posted on 11/02/2024)
Worth every cent Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
The training was very practical and hands-on. I could apply what I learned to my work immediately. (Posted on 05/01/2024)

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