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Google Professional Machine Learning Engineer Training

Course Information

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

Venue

12 Woodlands Square #07-85/86/87 Woods Square Tower 1, Singapore 737715. 5 mins walk from Woodlands (NS9) MRT station.

The venue is disabled-friendly.

Course Brochure

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Certification

  • Certificate of Achievement from Tertiary Infotech Academy Pte Ltd - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Achievement from Tertiary Infotech Academy Pte Ltd.

Certification Exam

Once you are prepared for the exam, you can register for the certification here. We are  Kryterion Authorized Testing Center. You can take the certification exam at our test center. Note that the course fee does not include the certification exam fee. 

Google Professional Machine Learning Engineer Training

Course Code: C740

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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 →

Funding Options

This course is not funded.

A funded WSQ version of the same course is available: WSQ - Google Professional Machine Learning Engineer Training. SkillsFuture Credit, SFEC and PSEA may apply to the WSQ course, subject to eligibility.

Course Fee

$1,400.00 (GST-exclusive)
$1,526.00 (GST-inclusive)

Course Date

Course Time

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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.

Post-Course Support

  • We provide free consultation related to the subject matter after the course.
  • Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

Cancellation & Reschedule Policy

  • You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commences.
  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
  • Note the venue of the training is subject to changes due to availability of the classroom.

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

Truman Ng is a ACTA certified trainer that graduated with Bachelor Degree in Electrical Engineering from NUS in year 2002. He designed Artificial Intelligence (AI) controller for DC-DC Power Convertor by using Fuzzy Logic and Neural Network (NN) as his university Final Year Project.
Truman has over 15 years project experiences across Database & Web Design, PLC machinery, Data Center Design , Structure Cabling System(SCS) and Enterprise Network Design and Implementation. He used to be a network architect for Hewlett Packard, working with a group of virtual team from the US in handling network design and projects in the States.
Truman is the founder of Nexplore (S) Pte Ltd. He provides solutions of Cloud SaaS, IaaS & PaaS and Software Defined Network (SDN), VoIP and Internet Security. He was engaged by Huawei Global Training Center to provide 60+ consultations and trainings for Internet Service Providers(ISP) from Malaysia, Singapore, Brunei, Philippines, Australia, Poland, Iran, South Africa, Swaziland, Cote Dlvoire, Syria, Uzbekistan, New Zealand and countries over the world.As achievement, Truman has successfully completed 100+ IT network projects for Bank, Hotel and Factory within 5 years.Truman is certified in PMP, Cisco CCNP, CCIP, CCDP, HP Ase and Huawei HCNP, HCIE R&S, HCNA Cloud, HCNA Security, etc

Solomon Soh is an experienced Data Scientist and AI Trainer with a strong record of teaching and mentoring in Python programming, data analytics, and machine learning. Currently a Data Science Trainer with IBM Singapore, he has coached teams on projects involving natural language processing, computer vision, and chatbots, achieving a 96% learner satisfaction rating for his communication and technical expertise. His career spans roles at Workforce Optimizer, Certis Cisco, Ernst & Young, and IQVIA, where he applied Python-driven analytics to improve operations, optimize staffing, and deliver actionable insights. His academic background includes a double degree in Economics and Psychology from Singapore Management University (Summa Cum Laude, triple major in Analytics), an MBA, and a Master’s in Financial Engineering.

is a ACLP certified trainer. He is an Enterprise Cloud and DevOps Consultant , responsible for  helping clients to move Virtual data centre to Private Cloud based on OpenStack and Public Cloud ( AWS, Azure and Google cloud) . Consulting and training experience on Devops tool chain like github , Jenkins, Sonarqube, Docker & kubernetes, Cloud foundry, Openshift, Ansible and SaltStack. Lot of my Role is involved design and implementation of a solution and training

Dr. Alfred Ang is the founder of Tertiary Courses. He is a serial entrepreneur. He founded OSWeb2Design Singapore Pte Ltd in 2007 offering web development, e-commerce store development, graphics design, ebook publishing, mobile apps development, and digital marketing services. He established the first online gardening store in Singapore, Eco City Hydroponics Pte Ltd in 2000, offering a wide range of gardening products such as seeds, plant nutrients, hydroponics kits etc. Eco City Hydroponics has become the most popular and successful gardening store in Singapore. He founded Tertiary Infotech Pte Ltd in 2012 and transformed the business to a training platform, Tertiary Courses in 2014. Tertiary Courses offers a wide range of SkillsFuture courses for PMETs to upgrade their skills and knowledge. He also established Tertiary Courses Malaysia in 2016. He also founded Tertiary Robotics in 2015 offering Arduino, Raspberry Pi, Microbit and Robotics products
Dr. Alfred Ang earned his Ph.D. from National University of Singapore in 2000, majoring in Electrical and Electronics Engineering. He also completed an online MBA course with U21 Global based in Australia. He obtained his B.Sc (Hons) from National University of Singapore in 1992, majoring in Physics. He topped his Physics cohort for 3 consecutive years and funded his degree study with Book price, awards and tuition. He has worked in Defence, Electronics and Semiconductor Industries. His current interests include Machine Learning, Deep Learning, Artificial Intelligence, Internet of Things, Robotics and Programming.
Dr. Alfred Ang is IBM certified instructor for AI Practitioners course. He is a ACTA certified trainer and DACE certified curriculum developer. He was Distinguished Toastmasters (DTM) and Senior Member of IEEE. He has published more than 20 peer reviewed papers and co-inventors for more than 20 inventions.

Review

Customer Reviews (2)

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. (Posted on 8/22/2019)
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