Google Cloud Machine Learning Engineer Certification - Course Overview

GCP machine learning certification training designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer considers responsible AI throughout the ML development process,and collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable solutions for optimal performance.

After Completing Google Cloud Machine Learning Course, Students Will be Able To:

  • Acquire knowledge on all aspects of data analysis and ML model architecture, ML pipeline interaction such as training, inferencing, retraining, monitoring and improving the models.
  • Attain skills required to clear the GCP Professional Machine Learning Engineer Certification Exam.

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Main Highlights of GCP ML Engineer Training

  • Our training modules have 50% - 60% hands-on lab sessions to encourage Thinking-Based Learning (TBL).
  • Interactive-rich virtual and face-to-face classroom teaching to inculcate Problem-Based Learning (PBL).
  • Industry certified instructor-led training and mentoring sessions to develop Competency-Based Learning (CBL).
  • Well-structured use-cases to simulate challenges encountered in a Real-World environment.
  • Integrated teaching assistance and support through experts designed Learning Management System (LMS) and ExamReady platform.
  • Integrated teaching assistance and support through experts designed Learning Management System (LMS) and ExamReady platform.

Who can take up a Google Cloud Machine Learning Engineer Course?

  • Data scientists interested in learning how to build and deploy machine learning models on GCP.
  • Software developers who want to develop machine learning applications on GCP.
  • IT professionals who want to learn how to deploy and manage machine learning models on GCP.
  • Business analysts who want to understand how machine learning can be used to solve business problems on GCP.
  • Students and academics who want to learn about machine learning on GCP.
  • Professionals want to better their careers and improve their abilities in the area of machine learning on GCP.

Prerequisites

  • Google Cloud Associate Engineer certification.

    • Salient Google Cloud Machine Learning Engineer Certification Training Benefits

    • Industry-recognized certification: Google Cloud Machine Learning Engineer Certification is an industry-recognized certification that demonstrates your proficiency in designing and building machine learning models on the Google Cloud Platform (GCP).
    • Expert guidance: The certification training is created by specialists with years of expertise in the industry of machine learning and GCP. They provide valuable guidance, tips, and tricks that will help you prepare for the exam.
    • Comprehensive curriculum: The certification training covers a comprehensive curriculum that includes all the key concepts and skills required to become a certified Google Cloud Machine Learning Engineer.
    • Hands-on experience: The training provides hands-on experience with GCP tools and services such as BigQuery, Cloud Storage, and AI Platform. This will give you practical experience working with real-world datasets and problems.
    • Flexibility: The certification training is designed to be you can develop your skills at your own speed and according to your schedule because it is flexible.
    • Latest technologies: The certification training is regularly updated to include the latest technologies and best practices in machine learning on GCP.
    • Community support: The certification training provides access to a network of specialists and learners help answer your questions and provide additional resources to support your learning.
    • Career advancement: Earning a Google Cloud Machine Learning Engineer Certification can open up new career opportunities and demonstrate your commitment to ongoing learning and professional development.
    • Cost-effective: The certification training is cost-effective, allowing you to acquire the necessary skills and knowledge pass the certification exam without breaking the bank.
    • High demand: With the increasing demand for machine learning engineers, the Google Cloud Machine Learning Engineer Certification has become a highly sought-after certification in the marketplace, providing you with a competitive advantage job market.

    GCP Machine Learning Engineer Certification Roadmap

    • Let's start with the fundamentals: If you're new to machine learning or GCP, start with the basics. CloudThat offers training programs that cover the fundamentals of machine learning and GCP, including data preprocessing, model development, and deployment.
    • Gain practical experience: After learning the basics, gain practical experience by working on real-world projects. CloudThat offers hands-on labs and workshops that allow you to work with GCP tools and services such as AI Platform, BigQuery, and Cloud Storage.
    • Prepare for the exam: Once you have gained practical experience, it's time to start preparing for the Google Cloud Machine Learning Engineer Certification exam. CloudThat offers a comprehensive certification training program that covers all the topics and skills needed to pass the exam.
    • Practice with mock tests: To prepare for the exam, practice with mock tests. CloudThat provides mock tests and quizzes that simulate the actual certification exam, allowing you to examine your readiness and find opportunities for improvement.
    • Get certified: After you have completed the training and practice exams, it's time to take the certification exam. Once you pass the exam, you will earn the Google Cloud Machine Learning Engineer Certification, demonstrating your expertise in building and deploying machine learning models on GCP.
    • Stay up-to-date: Finally, stay up-to-date with the latest developments in machine learning and GCP. CloudThat offers several education programs that cover the most recent technologies and best practises in the sector, ensuring that your skills remain relevant and in-demand.

    Course Outline Download Course Outline

    • Big Data and Machine Learning on Google Cloud
    • Data Engineering for Streaming Data
    • Big Data with BigQuery
    • Machine Learning Options on Google Cloud
    • The Machine Learning Workflow with Vertex AI

    Hands-On

    • AI Platform: Qwik Start
    • Dataprep: Qwik Start
    • Dataflow: Qwik Start - Templates
    • Dataflow: Qwik Start - Python
    • Dataproc: Qwik Start - Console
    • Dataproc: Qwik Start - Command Line

    • What It Means to be AI-First
    • How Google Does ML
    • Machine Learning Development with Vertex AI
    • Machine Learning Development with Vertex Notebooks
    • Best Practices for Implementing Machine Learning on Vertex AI
    • Responsible AI Development

    Hands-On

    • Vertex AI: Qwik Start
    • Using an Image Dataset to Train an AutoML Model

    • Introduction
    • Get to Know Your Data: Improve Data through Exploratory Data Analysis
    • Machine Learning in Practice
    • Training AutoML Models Using Vertex AI
    • BigQuery Machine Learning: Develop ML Models Where Your Data Lives
    • Optimization
    • Generalization and Sampling

    Hands-On

    • Exploratory Data Analysis Using Python and BigQuery
    • Using BigQuery ML to Predict Penguin Weight

    • Introduction to the TensorFlow ecosystem
    • Design and Build an Input Data Pipeline
    • Building Neural Networks with the TensorFlow and Keras API
    • Training at Scale with Vertex AI

    Hands-On

    • Classifying Structured Data using Keras Preprocessing Layers
    • Build a DNN using the Keras Functional API

    • Introduction to Vertex AI Feature Store
    • Raw Data to Features
    • Feature Engineering
    • Preprocessing and Feature Creation
    • Feature Crosses - TensorFlow Playground
    • Introduction to TensorFlow Transform

    Hands-On

    • Using Feature Store
    • Performing Basic Feature Engineering in BQML
    • Performing Basic Feature Engineering in Keras

    • Introduction
    • Understanding the ML Enterprise Workflow
    • Data in the Enterprise
    • Science of Machine Learning and Custom Training
    • Vertex Vizier Hyperparameter Tuning
    • Prediction and Model Monitoring Using Vertex AI
    • Vertex AI Pipelines
    • Best Practices for ML Development

    Hands-On

    • Vertex Pipelines: Qwik Start
    • Cloud Natural Language API: Qwik Start
    • Google Cloud Speech API: Qwik Start
    • Video Intelligence: Qwik Start

    • Machine Learning (ML) on Google Cloud Platform (GCP)
    • Explore the Data
    • Create the dataset
    • Build the Model
    • Operationalize the model

    Hands-On

    • Identify Damaged Car Parts with Vertex AutoML Vision
    • Deploy a BigQuery ML Customer Churn Classifier to Vertex AI for Online Predictions

    • Introduction to Advanced Machine Learning on Google Cloud
    • Architecting Production ML Systems
    • Designing Adaptable ML Systems
    • Designing High-Performance ML Systems
    • Building Hybrid ML Systems

    Hands-On

    • Structured data prediction using Vertex AI Platform
    • Serving ML Predictions in Batch and Real Time
    • Distributed Training with Keras
    • Using Kubeflow Pipelines with AI Platform

    • Introduction to Computer Vision and Pre-built ML Models for Image Classification
    • Vertex AI and AutoML Vision on Vertex AI
    • Custom Training with Linear, Neural Network and Deep Neural Network models
    • Convolutional Neural Networks
    • Dealing with Image Data

    Hands-On

    • Using the What-If Tool with Image Recognition Models
    • Identifying Bias in Mortgage Data using Cloud AI Platform and the What-if Tool
    • Compare Cloud AI Platform Models using the What-If Tool to Identify potential bias

    • Working with Sequences
    • Recurrent Neural Networks
    • Dealing with Longer Sequences
    • Text Classification
    • Reusable Embeddings
    • Encoder-Decoder Models

    Hands-On

    • Time Series Prediction with a DNN Model
    • Time Series Prediction with a Two-Layer RNN Model
    • Text Classification using TensorFlow/Keras on AI Platform
    • Text generation using tensor2tensor on Cloud AI Platform

    • Recommendation Systems Overview
    • Content-Based Recommendation Systems
    • Collaborative Filtering Recommendations Systems
    • Neural Networks for Recommendation Systems
    • Reinforcement Learning

    Hands-On

    • Using Neural Networks for Content-Based Filtering
    • Collaborative Filtering on Google Analytics data
    • ML on GCP: Hybrid Recommendations with the MovieLens Dataset
    • Applying Contextual Bandits for Recommendations with Tensorflow and TF-Agents

    • Why and When do we Need MLOps
    • Understanding the Main Kubernetes Components (Optional)
    • Introduction to AI Platform Pipelines
    • Training, Tuning and Serving on AI Platform
    • Kubeflow Pipelines on AI Platform
    • CI/CD for Kubeflow Pipelines on AI Platform

    Hands-On

    • Working with Cloud Build
    • Creating Google Kubernetes Engine Deployments
    • Using custom containers with AI Platform Training
    • Continuous Training Pipeline with Kubeflow Pipeline and Cloud AI Platform
    • CI/CD for a Kubeflow pipeline

    • Introduction to TFX Pipelines
    • Pipeline orchestration with TFX
    • Custom components and CI/CD for TFX pipelines
    • ML Metadata with TFX
    • Continuous Training with multiple SDKs, KubeFlow & AI Platform Pipelines
    • Continuous Training with Cloud Composer
    • ML Pipelines with MLflow

    Hands-On

    • TFX Standard Components Walkthrough
    • TFX on Cloud AI Platform Pipelines
    • CI/CD for a TFX pipeline
    • Continuous Training Pipelines with Cloud Composer

    Certification

    • By earning Google Cloud Certified Machine Learning Engineer certification, you can be competent Google Cloud certified professional.
    • Demonstrate ability to design, build, and productionize ML models to solve business challenges using Google Cloud technologies and ML models.
    • On successful completion of GCP Machine Learning Engineer training, aspirants receive a Course Completion Certificate from us.
    • By successfully clearing the Google Cloud Machine Learning Engineer exam, aspirants earn Google Certification.

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