Build Production-Ready Machine Learning Systems On Google

From Concept to Deployment: Mastering ML System Development on GCP

What you'll learn

Introduction to getting started with Google Cloud Platform (GCP)

Reading in and processing text data within GCP

Develop machine learning algorithms in GCP

Deploy your machine learning models

Requirements

Should have prior experience of Python data science

Prior experience of statistical and machine learning techniques will be beneficial

Should have an interest in cloud environments

Description

Machine learning (ML) has become integral to technological advancement, offering innovative solutions across various industries. This course targets explicitly developing and deploying robust, production-ready ML systems using the Google Cloud Platform (GCP). With the increasing demand for scalable and efficient AI models in the real world, this course is designed to bridge the gap between theoretical ML concepts and practical, industry-standard applications on GCP. Plus, this is a significant foray into the all-important and lucrative world of Data Engineering.Why Choose This Course?My academic background includes a PhD from the University of Cambridge, specialising in AI and Machine Learning. Coupled with extensive experience in deploying machine learning solutions in high-stakes environments, I bring a wealth of knowledge and practical insights.This course is tailored for aspiring data scientists, engineers, and AI professionals who aim to excel in implementing ML systems at scale. By joining this course, you will embark on a journey to become proficient in leveraging GCP for deploying sophisticated ML models. Here's what the course offers:In-Depth Understanding of GCP for ML: Learn the fundamentals and advanced features of GCP that make it an ideal platform for ML deployments.Hands-On ML System Development: Engage in practical sessions that cover the end-to-end process of building and deploying ML models on GCP.Real-World Applications and Case Studies: Analyze and learn from real-world case studies where ML systems have been successfully deployed.Advanced Topics in ML: Explore cutting-edge topics in machine learning, including automated ML (AutoML), ML model monitoring, and lifecycle management.Continuous Support and Guidance: Benefit from constant support throughout your learning journey, ensuring you stay on track and maximise your investment.Whether you're looking to enhance your skills for a current role or to break into the field of AI and ML, this course is designed to equip you with the knowledge and skills needed to succeed in today's fast-paced tech industry.Enrol now and take the first step towards mastering the art of building and deploying production-ready machine learning systems on the Google Cloud Platform!

Overview

Section 1: Introduction

Lecture 1 Introduction: Data Science Modelling to Deployment

Lecture 2 Data and Code

Lecture 3 Python3 Installation

Lecture 4 Different Data Types

Lecture 5 GCP Use Cases

Section 2: Learn GCP

Lecture 6 Where to Start?

Lecture 7 The GCP Ecosystem

Lecture 8 Lets Look at the GCP Interface (And Accessing the Free Trial)

Lecture 9 Permissions and Access

Lecture 10 Some Components of GCP Machine Learning

Lecture 11 GCP Buckets

Lecture 12 Virtually Speaking: Virtual Machines (VMs)

Lecture 13 Nuts and Bolts of Google Big Query

Lecture 14 Setup Your Budget

Section 3: Theory

Lecture 15 What Is Machine Learning?

Lecture 16 What Is Unsupervised Learning?

Lecture 17 Theory Behind k-Means Clustering

Lecture 18 Introduction to Supervised Machine Learning

Section 4: Machine Learning On GCP

Lecture 19 Create Your First GCP Project

Lecture 20 Get Started With Vertex

Lecture 21 Start the JupyterLab

Lecture 22 Upload Your Data In Your Bucket

Lecture 23 More of the Vertex Ecosystem

Section 5: Implement a Machine Learning (ML) Model on GCP

Lecture 24 Introduction to a Sample ML Problem

Lecture 25 Visualise the Predictors

Lecture 26 What Is Principal Component Analysis (PCA)?

Lecture 27 Implement PCA On Predictors

Lecture 28 Prep For Implementing ML

Lecture 29 Implement a Support Vector Machine (SVM) For Classification

Lecture 30 Accuracy Evaluation

Section 6: Automating ML Models With AutoML

Lecture 31 Why AutoML?

Lecture 32 Create Dataset

Lecture 33 Create Dataset-Cloud Storage

Lecture 34 A Quick Primer On Running A Tabular Classification Model

Lecture 35 Evaluate Results of AutoML

Lecture 36 Set Up A Tabular Regression Model

Lecture 37 Check Out the Model Registry

Section 7: Deploy The Model

Lecture 38 Endpoint Deployment

Lecture 39 Run a Production Scale AutoML Model in Vertex AI-Prep

Lecture 40 Preliminary Data Ingestion Steps

Lecture 41 Develop An Analysis Pipeline

Lecture 42 Model Deployment and Housekeeping

Section 8: AutoML For Natural Language processing (NLP)

Lecture 43 Accessing AutoML For NLP

Lecture 44 AutoML For Sentiment Analysis

Lecture 45 The Data Ingestion pipeline For Text Data

Lecture 46 Setting Up For AutoML Sentiment Analysis

Lecture 47 Train the AutoML Sentiment Analysis Pipeline

Lecture 48 Set Up Batch Predictions

People wanting to harness the power of cloud computing via GCP,Learn powerful GCP related technologies, including BigQuery and AutoML,People wanting to implement and deploy machine learning models in GCP