Mastering Machine Learning with Best GCP Professional Certification Courses

Overview

Google Cloud Platform (GCP) offers a professional machine learning course that equips learners with the knowledge and skills to design and deploy intelligent systems on the cloud. The course is designed for professionals who want to master machine learning techniques and implement them on GCP. This blog will provide an overview of the GCP professional machine learning course.

The GCP professional machine learning course is divided into the following modules

• Google Cloud Big Data and the Machine Learning Fundamentals
• How Google Does Machine Learning
• Launching into Machine Learning
• TensorFlow on Google Cloud
• Feature engineering
• Machine Learning in the Enterprise
• Production Machine Learning Systems
• Computer Vision Fundamentals with Google Cloud
• Natural Language Processing on Google Cloud
• Recommendation Systems on Google Cloud
• Machine Learning Operations (MLOps): Getting Started
• ML Pipelines on Google Cloud
• Perform Foundational Data, ML, and the AI Tasks in Google Cloud
• Build and Deploy the Machine Learning Solutions on Vertex AI

Module 1: Google Cloud Big Data and Machine Learning Fundamentals

This module introduces Google Cloud Big Data and Machine learning products and services which support the data-to-AI lifecycle.  It focuses on significant data challenges, different phases of ML model development (Machine learning workflow), BigQuery ML, and an overview of machine learning options provided by Google Cloud as pre-built APIs, AutoML, VertexAI, and custom training.

The Google Cloud big data and the machine learning tools and services that enable the data-to-AI lifecycle are introduced in this module. It examines the procedures, difficulties, and advantages of using Vertex AI to develop a large data pipeline and machine learning models on Google Cloud.

Module 2: How Google Does Machine Learning

This module examines what machine learning is and the issues it may resolve. The best practices for putting machine learning into practice are also covered in the module. Vertex AI, a single platform to quickly design, train, and deploy AutoML machine learning models, is offered.

The five stages of transforming a prospective use case to a machine learning-driven use case are covered in the module, along with the reasons why it’s crucial not to miss any of them. Recognizing the biases that ML may exacerbate and learning how to spot them.

Module 3: Launching into Machine Learning

The first topic covered in the module is data, specifically how to do exploratory data analysis and enhance data quality. In this module, we’ll go through Vertex AI AutoML and discuss creating, training, and using ML models without writing a single line of code. You will comprehend Big Query ML’s advantages. Next, go through machine learning (ML) model optimization and how generalization and sampling may be used to evaluate the quality of ML models for specific training.

Module 4: TensorFlow on Google Cloud

In this module, you’ll learn how to design and construct a TensorFlow input data pipeline, develop machine learning models using Keras and TensorFlow, increase the accuracy of ML models, write ML models for scaled applications, and create specialized ML models.

Module 5: Feature Engineering

The advantages of utilizing the Vertex AI Feature Store, ways to increase the precision of ML models and methods for determining which data columns provide the best features are all covered in this module. This module’s labs and content on feature engineering use BigQuery ML, Keras, and TensorFlow.

Module 6: Machine Learning in the Enterprise

Through a case study, this module approaches the ML Workflow from a real-world perspective. Numerous ML business needs and use cases are presented to an ML team. The team has to be aware of the tools needed for data management and governance and the optimal method for preparing data.

Three alternatives for developing ML models for two use cases are given to the team. The course outlines the rationale for using AutoML, BigQuery ML, or customized training to accomplish their goals.

Module 7: Production Machine Learning Systems

This module focuses on implementing several types of production ML systems—static, dynamic, and continuous training; static and dynamic inference; batch and online processing—which is covered. You examine the different TensorFlow abstraction levels, distributed training choices, and how to create distributed training models with your estimators. An overview of Kubeflow, designing machine learning pipelines using Kubeflow, will be discussed as part of this module.

Module 8: Computer Vision Fundamentals with Google Cloud

This module discusses several computer vision use cases in detail before highlighting various machine learning approaches to resolving these use cases. The approaches range from developing bespoke image classifiers using linear models, deep neural network (DNN) models, or convolutional neural network (CNN) models to experimenting with pre-built ML models using pre-built ML APIs like AutoML Vision.

The module demonstrates how to enhance a model’s accuracy through feature extraction, augmentation, and hyperparameter tweaking while attempting to prevent data overfitting.

Module 9: Natural Language Processing on Google Cloud

The products and methods to resolve NLP issues on Google Cloud are introduced in this course. Different NLP models, such as ANN, TensorFlow, DNN, RNN, LSTM, and GRU, and their advantages and disadvantages, are also discussed. It also covers using Vertex AI and TensorFlow to build a neural network project for natural language processing.

Module 10: Recommendation Systems on Google Cloud

This module discusses an overview of recommendation systems and different types of recommendation systems (Content-based, Collaborative- filtering, etc.). Here you will construct an ML pipeline that serves as a recommendation system using previous knowledge of classification models and embeddings.

Module 11: Machine Learning Operations (MLOps): Getting Started

This module covers MLOps tools and best practices for installing, assessing, running, and monitoring production ML systems on Google Cloud. The deployment, testing, monitoring, and automation of ML systems in production are the main goals of the discipline known as MLOps. Professionals in machine learning engineering employ tools for model assessment and continual development. To provide rapidity and rigor in deploying the best-performing models, they collaborate with (or are themselves) Data Scientists who create models.

Module 12: ML Pipelines on Google Cloud

You will be studying in this module from ML Engineers and Trainers who work on the most cutting-edge ML pipeline development at Google Cloud. TensorFlow Extended (or TFX), Google’s production machine learning platform based on TensorFlow for administration of ML pipelines and metadata, will be covered. Here,  you will discover how to maintain ML information and how to automate your workflow using continuous integration and deployment. The discussion will then explore how to automate and reuse machine learning pipelines across several ML frameworks, including TensorFlow, Py torch, scikit-learn, and XGBoost. Additionally, you’ll learn how to use a different Google Cloud product.

Module 13: Perform Foundational Data, ML, and AI Tasks in Google Cloud

This small quest will help you to have hands-on on basic features of machine learning and AI technologies such as BigQuery, Cloud Speech API, Cloud Natural API, AI Platform (Vertex AI), Dataflow, Cloud Dataprep, Dataproc, and Video Intelligence API.

Module 14: Build and Deploy Machine Learning Solutions on Vertex AI

By finishing this quest, you will better understand how to use Google Cloud’s unified Vertex AI platform and its AutoML and custom training services to train, evaluate, tune, explain, and deploy machine learning solutions.

This module is highly recommended for Data Scientists and Machine Learning engineers.

Here are a few multiple-choice questions (MCQs) related to the GCP professional machine learning course:

1. Which GCP tool can be used to train and evaluate machine learning models?
2. Google Cloud Storage
3. Google BigQuery
4. Cloud ML Engine
5. Google Compute Engine

Answer: C. Cloud ML Engine

2. Which GCP professional machine learning course module focuses on recommendation systems?
3. Building ML Models on GCP
4. Image Understanding
5. Sequence Models
6. Recommendation Systems

Links related to the GCP professional machine learning course

GCP Professional Machine Learning Engineer Certification
https://cloud.google.com/certification/machine-learning-engineer

GCP Professional Machine Learning Engineer Certification in CloudThat
Google Cloud Machine Learning Engineer Certification Training – (cloudthat.com)

Google Cloud Platform (GCP) Documentation for Machine Learning:
https://cloud.google.com/ml-engine/docs

GitHub Repository for GCP Machine Learning Samples:
https://github.com/GoogleCloudPlatform/cloudml-samples

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