Build Production-Ready Machine Learning Systems on Google

Build Production-Ready Machine Learning Systems on Google, available at $19.99, has an average rating of 4.3, with 50 lectures, based on 11 reviews, and has 170 subscribers.

You will learn about 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 This course is ideal for individuals who are People wanting to harness the power of cloud computing via GCP or Learn powerful GCP related technologies, including BigQuery and AutoML or People wanting to implement and deploy machine learning models in GCP It is particularly useful for People wanting to harness the power of cloud computing via GCP or Learn powerful GCP related technologies, including BigQuery and AutoML or People wanting to implement and deploy machine learning models in GCP.

Enroll now: Build Production-Ready Machine Learning Systems on Google

Summary

Title: Build Production-Ready Machine Learning Systems on Google

Price: $19.99

Average Rating: 4.3

Number of Lectures: 50

Number of Published Lectures: 50

Number of Curriculum Items: 50

Number of Published Curriculum Objects: 50

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome to the Course

Lecture 2: Introduction: Data Science Modelling to Deployment

Lecture 3: Data and Code

Lecture 4: Python3 Installation

Lecture 5: Different Data Types

Lecture 6: GCP Use Cases

Chapter 2: Learn GCP

Lecture 1: Where to Start?

Lecture 2: The GCP Ecosystem

Lecture 3: Lets Look at the GCP Interface (And Accessing the Free Trial)

Lecture 4: Set Up Your Budget

Lecture 5: Permissions and Access

Lecture 6: Some Components of GCP Machine Learning

Lecture 7: GCP Buckets

Lecture 8: Virtually Speaking: Virtual Machines (VMs)

Lecture 9: Nuts and Bolts of Google Big Query

Lecture 10: Setup Your Budget

Chapter 3: Theory

Lecture 1: What Is Machine Learning?

Lecture 2: What Is Unsupervised Learning?

Lecture 3: Theory Behind k-Means Clustering

Lecture 4: Introduction to Supervised Machine Learning

Chapter 4: Machine Learning On GCP

Lecture 1: Create Your First GCP Project

Lecture 2: Get Started With Vertex

Lecture 3: Start the JupyterLab

Lecture 4: Upload Your Data In Your Bucket

Lecture 5: More of the Vertex Ecosystem

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

Lecture 1: Introduction to a Sample ML Problem

Lecture 2: Visualise the Predictors

Lecture 3: What Is Principal Component Analysis (PCA)?

Lecture 4: Implement PCA On Predictors

Lecture 5: Prep For Implementing ML

Lecture 6: Implement a Support Vector Machine (SVM) For Classification

Lecture 7: Accuracy Evaluation

Chapter 6: Automating ML Models With AutoML

Lecture 1: Why AutoML?

Lecture 2: Create Dataset

Lecture 3: Create Dataset-Cloud Storage

Lecture 4: A Quick Primer On Running A Tabular Classification Model

Lecture 5: Evaluate Results of AutoML

Lecture 6: Set Up A Tabular Regression Model

Lecture 7: Check Out the Model Registry

Chapter 7: Deploy The Model

Lecture 1: Endpoint Deployment

Lecture 2: Run a Production Scale AutoML Model in Vertex AI-Prep

Lecture 3: Preliminary Data Ingestion Steps

Lecture 4: Develop An Analysis Pipeline

Lecture 5: Model Deployment and Housekeeping

Chapter 8: AutoML For Natural Language processing (NLP)

Lecture 1: Accessing AutoML For NLP

Lecture 2: AutoML For Sentiment Analysis

Lecture 3: The Data Ingestion pipeline For Text Data

Lecture 4: Setting Up For AutoML Sentiment Analysis

Lecture 5: Train the AutoML Sentiment Analysis Pipeline

Lecture 6: Set Up Batch Predictions

Instructors

Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 6 votes
5 stars: 4 votes

Frequently Asked Questions

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