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Google Professional Machine Learning Engineer Exam Prep

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.

Certificate

All participants will receive a Certificate of Completion from Tertiary Courses after achieved at least 75% attendance.

Disclaimer

Note that we are not affiliated with Google. To get the official certification, please register on Google Certification site below.

Register for Google Cloud Certification

Once you are prepared for the exam, you can register for the certification here. Note that the course fee does not include the certification exam fee.

Funding and Grant

HRD Corp Claimable Course for Employers Registered with HRD Corp

HRDF claimable

Course Code: M1009

Course Booking

MYR4,000.00

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Post-Course Support

We provide free 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 asap.

Course 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.
Note the minimal class size to start a class is 3 Pax.


Course Details

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 dDesign 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

Prerequisite

This course is for beginner. No programming or coding knowledge is required. ;

Software Requirement

Please sign up a Google Cloud Platform (GCP) account on https://cloud.google.com/

HRDF Funding

Please refer to this video https://youtu.be/Kzpd-V1F9Xs

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How to submit grant applications for HRD Corp Claimable Courses

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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

Ajay B :  Ajay is a ACLP certied trainer. Ajay is a vendor neutral cloud consultant and training expert on Cloud , with several Private cloud deployments in India and cloud migration knowledge .He is a Cloud and DevOps enthusiast with consulting, deployment and training expertise on OpenStack, AWS, Google Cloud ,Azure, Jenkins, and Docker

Ajay has 18 + years Industry experience as IT entrepreneur and 9 years in Cloud and Devops technical consulting, implementation and training area, currently working in capacity of Vice President – Cloud and Devops services handling singapore and India

Anil Bidari: Anil  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

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