Real-world End to End Machine Learning Ops on Google Cloud

Real-world End to End Machine Learning Ops on Google Cloud, available at $49.99, has an average rating of 4.58, with 88 lectures, based on 100 reviews, and has 1162 subscribers.

You will learn about Comprehensive understanding of Google Cloud Platforms suite for MLOps, diving deep into tools like Airflow,Cloud Build, Google Container and Artifact Registry Hands-on proficiency in orchestrating, deploying, and monitoring machine learning workflows using GCP Composer/Airflow and Vertex AI services. Best practices and methodologies to ensure scalable, reproducible, and efficient machine learning pipelines on the cloud. Insights and techniques tailored to help in preparation for the GCP Professional ML Certification exam, bolstering your credentials in the cloud ML domain. This course is ideal for individuals who are Data scientists and machine learning engineers looking to streamline their ML workflows and deploy models efficiently using Google Cloud Platform. or Cloud professionals aiming to specialize in machine learning operations and seeking hands-on experience with GCPs suite of tools. or Developers and IT professionals who want to understand the intersection of cloud computing and machine learning, and how to harness them together effectively. or Teams or individuals preparing for the GCP Professional ML Certification exam and seeking comprehensive coverage of the required topics. or Anyone interested in staying updated with the latest trends in cloud-based machine learning and MLOps practices. It is particularly useful for Data scientists and machine learning engineers looking to streamline their ML workflows and deploy models efficiently using Google Cloud Platform. or Cloud professionals aiming to specialize in machine learning operations and seeking hands-on experience with GCPs suite of tools. or Developers and IT professionals who want to understand the intersection of cloud computing and machine learning, and how to harness them together effectively. or Teams or individuals preparing for the GCP Professional ML Certification exam and seeking comprehensive coverage of the required topics. or Anyone interested in staying updated with the latest trends in cloud-based machine learning and MLOps practices.

Enroll now: Real-world End to End Machine Learning Ops on Google Cloud

Summary

Title: Real-world End to End Machine Learning Ops on Google Cloud

Price: $49.99

Average Rating: 4.58

Number of Lectures: 88

Number of Published Lectures: 88

Number of Curriculum Items: 88

Number of Published Curriculum Objects: 88

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

Comprehensive understanding of Google Cloud Platforms suite for MLOps, diving deep into tools like Airflow,Cloud Build, Google Container and Artifact Registry

Hands-on proficiency in orchestrating, deploying, and monitoring machine learning workflows using GCP Composer/Airflow and Vertex AI services.

Best practices and methodologies to ensure scalable, reproducible, and efficient machine learning pipelines on the cloud.

Insights and techniques tailored to help in preparation for the GCP Professional ML Certification exam, bolstering your credentials in the cloud ML domain.

Who Should Attend

Data scientists and machine learning engineers looking to streamline their ML workflows and deploy models efficiently using Google Cloud Platform.

Cloud professionals aiming to specialize in machine learning operations and seeking hands-on experience with GCPs suite of tools.

Developers and IT professionals who want to understand the intersection of cloud computing and machine learning, and how to harness them together effectively.

Teams or individuals preparing for the GCP Professional ML Certification exam and seeking comprehensive coverage of the required topics.

Anyone interested in staying updated with the latest trends in cloud-based machine learning and MLOps practices.

Target Audiences

Data scientists and machine learning engineers looking to streamline their ML workflows and deploy models efficiently using Google Cloud Platform.

Cloud professionals aiming to specialize in machine learning operations and seeking hands-on experience with GCPs suite of tools.

Developers and IT professionals who want to understand the intersection of cloud computing and machine learning, and how to harness them together effectively.

Teams or individuals preparing for the GCP Professional ML Certification exam and seeking comprehensive coverage of the required topics.

Anyone interested in staying updated with the latest trends in cloud-based machine learning and MLOps practices.

Google Cloud Platform is gaining momentum in today’s cloud landscape, and MLOps is becoming indispensable for streamlined machine learning projects

In the fascinating journey of Data Science, there’s a significant step between creating a model and making it operational. This step is often overlooked but is crucial – it’s called Machine Learning Ops (MLOps). Google Cloud Platform (GCP) offers some powerful tools to help streamline this process, and in this course, we’re going to delve deep into them.

Topics covered in the course  : 

CI/CD Using Cloud Build,Container and Artifact Registry

Continuous Training using Airflow for ML Workflow Orchestration:

Writing Test Cases

Vertex AI Ecosystem using Python

Kubeflow Pipelines for ML Workflow and reusable ML components

Deploy Useful Applications using PaLM LLM of GCP Generative AI 

Why Take This Course?

Tailored for Beginners with programming background: A basic understanding and expertise of data science is enough to start. We’ll guide you through everything else.

Practical Learning: We believe in learning by doing. Throughout the course, real-world projects will help you grasp the concepts and apply them confidently.

GCP Professional ML Certification Prep: While the aim is thorough understanding and implementation, this course will also provide a strong foundation for those aiming for the GCP Professional ML Certification.

Your Takeaways

By the end of this course, you won’t just understand the theory behind MLOps, you’ll be equipped to implement it. The practical experience gained will empower you to handle real-world ML challenges with confidence.

The relevance of machine learning in today’s world is undeniable, and with the rise of its importance, there’s an increasing demand for professionals skilled in MLOps. This course is designed to bridge the gap between model development and operational excellence, making ML more than just a coding exercise but a tangible asset in solving real-world problems.

So, if you’re eager to elevate your ML journey and understand how to make your models truly effective on a platform as powerful as Google Cloud, this course awaits you. Dive in, explore, learn, and let’s make ML work for the real world together!

Course Curriculum

Chapter 1: Introduction & prerequisites

Lecture 1: Hello & Introduction

Lecture 2: Github Repository for this course

Lecture 3: Discord Server for this Course

Lecture 4: Lab-Create GCP Trial Account for the course

Lecture 5: Lab-Download gcloud-cli & project configuration

Lecture 6: Course prerequisites and installations

Lecture 7: Course Overview & section walkthrough

Lecture 8: GCP Services used in the course

Chapter 2: Introduction to ML Ops

Lecture 1: Introduction To ML-Ops

Lecture 2: Key Components Principles in ML-Ops

Chapter 3: CI/CD using GCP CloudBuild,Artifact & Container Registry and CloudRun

Lecture 1: Introduction to CI/CD on GCP

Lecture 2: Introduction to GCP Container Registry and Artifact Registry

Lecture 3: Lab : Enable necessary APIs and install modules

Lecture 4: Introduction To GCP CloudRun for ML Models

Lecture 5: Overview of Steps for Flask Application – Local development

Lecture 6: Lab : Deploy Flask application using Container/Artifact Registry and CloudRun

Lecture 7: Lab: Execute PyTest locally using ChatGPT

Lecture 8: Introduction to GCP CloudBuild Service

Lecture 9: Lab : Deploy Flask application using GCP CloudBuild

Lecture 10: Lab : Setup Cloudbuild Triggers from GitHub Repo

Lecture 11: XGBoost Model Overview for Coupon Recommendations Model

Lecture 12: Lab : Deploy and implement Model Serving Flask Application and Pytest Locally

Lecture 13: Lab : Deploy ML Model to CloudRun using CloudBuild

Lecture 14: Overview of A/B Testing for ML Models using CloudRun

Lecture 15: Lab : Deploy New Version of ML Model & Update version traffic

Lecture 16: Assignment – Deploy Bike Rentals Regression Model & perform CI/CD

Chapter 4: Continuous Model Training using Cloud Composer-Airflow

Lecture 1: Overview of Data science model for Bank Marketing Campaign

Lecture 2: Introduction to Continuous Training

Lecture 3: Introduction to Airflow For Continuous Training

Lecture 4: Lab: Create Setup Airflow composer Env and Vertex AI Workbench

Lecture 5: Lab: Execute Model Training using Jupyter-Nbk on GCP

Lecture 6: Lab: Execute Airflow Dag for Machine Learning Workflow

Lecture 7: Lab : Continuous Training Pipeline in Action

Lecture 8: Implications of Failure scenarios in Continuous Training

Lecture 9: Lab: Trigger Continuous Training to capture model logs and setup alerting

Lecture 10: Overview of CI/CD for Model Training

Lecture 11: Lab : CI/CD of Model Training Code using Cloud-Build,PyTest and Github

Lecture 12: Lab :Setup CloudBuild triggers

Lecture 13: Assignment Part-1 : Setup Continuous Training for a Marketing ROI Model

Lecture 14: Assignment Part-2 : Perform CICD of the Data Science ROI Model

Lecture 15: Assignment Part-3 : Deploy Model Serving Application to GCP CloudRun

Chapter 5: Vertex AI For Data Science & Machine Learning

Lecture 1: Section Overview

Lecture 2: Introduction to Vertex AI Model Training Service

Lecture 3: Overview of Bike Share Rentals Regression Model

Lecture 4: Lab : Vertex AI Model Training using Web Console and Gcloud CLI

Lecture 5: Introduction to Vertex AI Model Registry

Lecture 6: Lab : Python SDK-Vertex AI Model Training,Model Registry and Model Deployment

Lecture 7: Lab : Execute Online & Batch prediction Service using Python SDK and jupter nbks

Lecture 8: Lab-Walkthrough Batch Prediction Output & Online Prediction jobs using Cloud Run

Lecture 9: Lab-Deploy and implement Batch Prediction Job using GCP Cloud Functions

Lecture 10: Lab : Overview of CI/CD using Vertex AI

Lecture 11: Lab :Vertex AI : CI/CD of Data science model using CloudBuild

Lecture 12: Assignment : Deploy XGBoost Model to Vertex AI

Chapter 6: Vertex AI-Kubeflow Pipelines for ML Workflow Orchestration

Lecture 1: Introduction to Kubeflow for ML Orchestration

Lecture 2: Different Components in Kubeflow Pipelines

Lecture 3: Lab : Deploy a simple pipeline for XgBoost Model

Lecture 4: Lab : Trigger Xgboost Model using compiled json for continuous training

Lecture 5: Lab : Execute end-to-end kubeflow pipeline with model evaluation

Lecture 6: Lab Assignment: Deploy a Scikit-Learn Credit Scoring Model to Vertex Pipelines

Lecture 7: Introduction to Vertex AI Experiments

Lecture 8: Lab: Use different model hyperparameters for Xgboost with Vertex AI Experiments

Lecture 9: Lab:Train Different Data science Classification models using Experiments

Lecture 10: Assignment : Perform Experiments for Bike share Regression Model

Chapter 7: Vertex AI-Hyperparameter Tuning Jobs, Explainability AI & Model Versioning

Lecture 1: Introduction to Hyperparameter Tuning on Vertex AI

Lecture 2: Lab : Implement Hyperparameter Tuning for BikeShare Regression Model

Lecture 3: Lab : Result Walkthrough & Assignment Overview

Lecture 4: Lab : Result Walkthrough & Assignment Overview

Lecture 5: Lab : Deploy Model Endpoint With Explainability Parameters

Lecture 6: Lab: Execute explainability for online predictions and Interpret results

Lecture 7: Lab: Execute explainability for online predictions and Interpret results

Lecture 8: Assignment : Perform Explainability for XgBoost Models

Lecture 9: Introduction to Model Versioning using Vertex AI Model Registry

Lecture 10: Lab : Deploy different versions of XgBoost Model to Model Registry

Lecture 11: Introduction to Vertex AI FeatureStore

Lecture 12: Lab : Create Feature store objects

Lecture 13: Lab : Ingest Data from Pandas DF into Feature Store

Lecture 14: Lab : Read Data From Vertex AI Feature Store into Pandas Df

Lecture 15: Introduction to AutoML

Lecture 16: Lab-Train and Deploy Classification Model using AutoML

Lecture 17: Lab – Train and Deploy Regression Model using AutoML

Chapter 8: Generative AI on Google Cloud

Lecture 1: Introduction to Generative AI

Lecture 2: Introduction to Large language models – PaLM 2

Lecture 3: Important keywords and concepts in LLM

Lecture 4: Lab-Generative AI Studio

Lecture 5: Lab – Execute LLM using Python & Jupyter Nbk

Lecture 6: Lab – Deploy text classification LLM Model using Python & Cloud Run

Lecture 7: Lab-Deploy Document Summarization Application using Python & Cloud Run

Lecture 8: Lab- Generate Fashion Image Descriptions using Python

Instructors

Sid Raghunath
Cloud/Data Engineering/Analytics/Architecture

Rating Distribution

1 stars: 2 votes

2 stars: 1 votes

3 stars: 10 votes

4 stars: 32 votes

5 stars: 55 votes

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