Course Overview

A Professional Machine Learning Engineer 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 this 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.

Upcoming Batches

India Online Enroll
Start Date End Date

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Key Features

  • 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 Should Attend

  • This course is for Professional Machine Learning Engineer who designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques.


  • Google Cloud Associate Engineer certification.

    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


    • 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


    • 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


    • 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


    • 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


    • 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


    • 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


    • 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


    • 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


    • 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


    • 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


    • 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


    • 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


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


    • 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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