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Google Cloud Certified Professional Machine Learning Engineer Training

Course Information

  • Sessions 4 days
  • Duration 30 hrs
  • Level Intermediate
  • 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

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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.

Google Cloud Certified Professional Machine Learning Engineer Training

Course Code: C997

9 Review(s) | Add Your Review

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What's This Course About

Dive into the profound capabilities of Machine Learning on the Google Cloud Platform with Tertiary Courses. Our in-depth curriculum sheds light on the myriad of hosting options available, be it Serverless, container-based, or via virtual machines, ensuring that you're equipped to make informed decisions tailored to your specific needs. Grasp the essence of enabling GCP's ML AIs and hone your skills in preparing data through Cloud Dataflow and Dataprep, pivotal for any robust ML pipeline.

As we advance, delve into the intriguing world of modeling predictions for diverse media including images, video, text-to-speech, and cloud translation. Our hands-on approach ensures you're adept at employing AutoML for streamlined ML tasks. We further delve into intricate machine learning and deep learning modules, wrapping up with a comprehensive understanding of modern ML architectures. This course is an indispensable asset for those enthusiastic about harnessing the full potential of machine learning on GCP.

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 →

WSQ Funding

Full Fee 5,600.00 Before GST
GST 504.00 9% of fee
Baseline Nett 3,304.00 SG/PR age 21+ · 50% funded
MCES / SME Nett 2,184.00 SG age 40+ · 70% funded
Funding and Grant Applications

No funding is available for this course

Course Fee

MYR5,600.00

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

This course prepares you for the N/A certification exam, covering all official exam domains and their approximate weightings:

Domain 1 Architecting low-code AI solutions (13%)

  • Build models in BigQuery ML or Gemini Enterprise Agent Platform AutoML (classification, regression, forecasting, clustering) based on the business problem
  • Perform feature engineering/selection using BigQuery ML; generate predictions using BigQuery ML
  • Train models using Agent Platform AutoML; fine-tune Gemini models using BigQuery
  • Evaluate and select the appropriate model from Agent Platform Model Garden for a given task
  • Build applications using industry-specific APIs (Document AI, Vision, Translate) and tune models for specific use cases (Gemini, Imagen, Veo)
  • Optimize Gemini-based applications for cost, latency, and availability

Domain 2 Collaborating within and across teams to manage data and models (16%)

  • Organize and explore tabular, text, and image data for efficient experimenting, training, and serving
  • Choose the right preprocessing tool by scale/complexity (BigQuery SQL, Dataflow, Apache Spark, in-memory Python frameworks)
  • Create/consolidate features in Agent Platform Feature Store; ensure data privacy and handle PII
  • Prototype models in Agent Platform Workbench or Colab Enterprise notebooks using PyTorch, sklearn, JAX and Model Garden models
  • Choose the right environment for experimentation (Experiments on Agent Platform, Agent Platform Pipelines, Kubeflow Pipelines)
  • Evaluate predictive and gen AI solutions (metrics, LLM-as-a-judge) and track model artifacts/versions/lineage

Domain 3 Scaling prototypes into ML models (21%)

  • Choose model type (ARIMA, DNN, LLM), product (AutoML, BigQuery ML, Pipelines), deployment strategy, and modeling technique given interpretability needs
  • Organize and ingest structured/unstructured training data (Cloud Storage, BigQuery) into training pipelines
  • Train models via different SDKs (Agent Platform custom training, Kubeflow on GKE, AutoML, Tabular Workflows)
  • Troubleshoot ML model training failures and perform hyperparameter tuning
  • Fine-tune foundational models from Agent Platform/Model Garden and judge when tuning is warranted
  • Evaluate compute/accelerator options (CPU, GPU, TPU) and distributed training strategies (data/model parallelism)

Domain 4 Serving and scaling models (20%)

  • Deploy models for batch and online inference (Agent Platform, Model Garden, Cloud Run, GKE)
  • Package/serve models from different frameworks (PyTorch, XGBoost) using prebuilt/custom containers
  • Organize and version models in Gemini Enterprise Agent Platform Model Registry
  • Implement rollout strategies (A/B testing, canary deployments) and inference pre/postprocessing
  • Manage and serve features via Agent Platform Feature Store; deploy to public/private endpoints
  • Choose appropriate serving hardware (CPU/GPU/TPU/edge) and scale serving backend based on throughput

Domain 5 Automating and orchestrating ML pipelines (18%)

  • Validate data and models within end-to-end ML pipelines
  • Build and orchestrate pipelines using managed/unmanaged services or templates (Agent Platform Pipelines, Managed Service for Apache Airflow, Ray on Agent Platform)
  • Ensure consistent data preprocessing between training and serving
  • Determine an appropriate model retraining policy
  • Deploy models in CI/CD/CT pipelines (e.g., Cloud Build)

Domain 6 Monitoring AI solutions (13%)

  • Build secure AI systems against data/model exfiltration, malicious prompting, and sensitive-data leakage to LLMs (Regex, safety filters, Model Armor)
  • Align with responsible AI practices, including monitoring for bias
  • Provide model explainability on Agent Platform (e.g., Agent Platform Inference)
  • Configure Model Monitoring on Gemini Enterprise Agent Platform for continuous evaluation metrics
  • Monitor for training-serving skew, data drift, concept drift, and feature attribution drift
  • Monitor, test, and evaluate gen AI solutions

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:

TBD

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)

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
Very informative and interactive. The small class size meant we got a lot of personal attention. (Posted on 09/06/2024)
Very useful course 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
I found the course extremely useful and relevant to my job. Highly recommend it to others. (Posted on 04/05/2024)
Very informative 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
Solid content and a supportive trainer. I would definitely sign up for more courses here. (Posted on 09/03/2024)
Fantastic experience 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
Solid content and a supportive trainer. I would definitely sign up for more courses here. (Posted on 03/02/2024)

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