WSQ - Google Professional Machine Learning Engineer Training (Synchronous e-Learning)

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

Final Assessment 

• Written Assessment (Short Answers Questions)
• Practical Performance

Promotion Code

Promo or discount cannot be applied to WSQ courses

Minimum Entry Requirement

Knowledge and Skills

• Able to operate using computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
• 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 Year Group : 21-65 years old

Minimum Software/Hardware Requirement

Software:

You can download and install the following software:

• Google Cloud

Hardware: Windows and Mac Laptops

About Progressive Wage Model (PWM)

The Progressive Wage Model (PWM) helps to increase wages of workers through upgrading skills and improving productivity. 

Employers must ensure that their Singapore citizen and PR workers meet the PWM training requirements of attaining at least 1 Workforce Skills Qualification (WSQ) Statement of Attainment, out of the list of approved WSQ training modules.

For more information on PWM, please visit MOM site.

Funding Eligility Criteria

Steps to Apply Skills Future Claim

• The staff will send you an invoice with the fee breakdown.
• Login to the MySkillsFuture portal, select the course you’re enrolling on and enter the course date and schedule.
• Enter the course fee payable by you (including GST) and enter the amount of credit to claim.
• Upload your invoice and click ‘Submit’

SkillsFuture Level-Up Program

The  SkillsFuture Level-Up Programme provides greater structural support for mid-career Singaporeans aged 40 years and above to pursue a substantive skills reboot and stay relevant in a changing economy. For more information, visit SkillsFuture Level-Up Programme

Get Additional Course Fee Support Up to $500 under UTAP

The Union Training Assistance Programme (UTAP) is a training benefit provided to NTUC Union Members with an objective of encouraging them to upgrade with skills training. It is provided to minimize the training cost. If you are a NTUC Union Member then you can get 50% funding (capped at $500 per year) under Union Training Assistance Programme (UTAP).

For more information visit NTUC U Portal – Union Training Assistance Program (UTAP)

Steps to Apply UTAP

• Log in to your U Portal account to submit your UTAP application upon completion of the course.

Note

• SSG subsidy is available for Singapore Citizens, Permanent Residents, and Corporates.
• All Singaporeans aged 25 and above can use their SkillsFuture Credit to pay. For more details, visit www.skillsfuture.gov.sg/credit
• An unfunded course fee can be claimed via SkillsFuture Credit or paid in cash.
• UTAP funding for NTUC Union Members is capped at $250 for 39 years and below and at $500 for 40 years and above.
• UTAP support amount will be paid to training provider first and claimed after end of class by learner.

• 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

Man Guo Chang: Man Guo Chang graduated from Nanyang Technological University, School of Electrical and Electronic Engineering, major in Computer Engineering.

He has more than 25 years of working experience in the Semiconductor field, specialized in IC Testing, Inline Electrical Testing, Product & Yield Engineering, Data Analysis, System Engineering, and Software Development. He is also an ACTA certified trainer, currently providing STEM training to adult learners in the area of Computer Vision, Internet of Things, Embedded Electronics, and Python Programming.