Machine Learning Ops: Google Cloud - Real World Data Science

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1. Introduction & prerequisites

1.1 Github Repository for the course.html

1. Hello & Introduction.mp4

01:16

2.1 Discord Server.html

2. Discord Server for this Course.mp4

00:42

3. Lab-Create GCP Trial Account for the course.mp4

01:17

4. Lab-Download gcloud-cli & project configuration.mp4

02:30

5. Course prerequisites and installations.mp4

02:23

6. Course Overview & section walkthrough.mp4

02:25

7. GCP Services used in the course.mp4

01:17

2. Introduction to ML Ops

1. Introduction To ML-Ops.mp4

03:24

2. Key Components Principles in ML-Ops.mp4

02:30

3. CICD using GCP CloudBuild,Artifact & Container Registry and CloudRun

1.1 CICD-Section-Source-Code.zip

1. Introduction to CICD on GCP.mp4

03:21

2. Introduction to GCP Container Registry and Artifact Registry.mp4

02:24

3. Lab Enable necessary APIs and install modules.mp4

03:19

4. Introduction To GCP CloudRun for ML Models.mp4

01:59

5. Overview of Steps for Flask Application - Local development.mp4

01:06

6. Lab Deploy Flask application using ContainerArtifact Registry and CloudRun.mp4

12:53

7. Lab Execute PyTest locally using ChatGPT.mp4

04:34

8. Introduction to GCP CloudBuild Service.mp4

01:38

9. Lab Deploy Flask application using GCP CloudBuild.mp4

05:17

10. Lab Setup Cloudbuild Triggers from GitHub Repo.mp4

05:04

11. XGBoost Model Overview for Coupon Recommendations Model.mp4

04:41

12. Lab Deploy and implement Model Serving Flask Application and Pytest Locally.mp4

07:41

13. Lab Deploy ML Model to CloudRun using CloudBuild.mp4

05:00

14. Overview of AB Testing for ML Models using CloudRun.mp4

02:39

15. Lab Deploy New Version of ML Model & Update version traffic.mp4

05:36

16. Assignment - Deploy Bike Rentals Regression Model & perform CICD.mp4

02:39

4. Continuous Model Training using Cloud Composer-Airflow

1.1 continuous-training-section-source-code.zip

1. Overview of Data science model for Bank Marketing Campaign.mp4

03:23

2. Introduction to Continuous Training.mp4

03:20

3. Introduction to Airflow For Continuous Training.mp4

01:48

4. Lab Create Setup Airflow composer Env and Vertex AI Workbench.mp4

04:45

5. Lab Execute Model Training using Jupyter-Nbk on GCP.mp4

05:47

6. Lab Execute Airflow Dag for Machine Learning Workflow.mp4

08:25

7. Lab Continuous Training Pipeline in Action.mp4

01:47

8. Implications of Failure scenarios in Continuous Training.mp4

02:21

9. Lab Trigger Continuous Training to capture model logs and setup alerting.mp4

07:05

10. Overview of CICD for Model Training.mp4

01:49

11. Lab CICD of Model Training Code using Cloud-Build,PyTest and Github.mp4

09:03

12. Lab Setup CloudBuild triggers.mp4

03:28

13. Assignment Part-1 Setup Continuous Training for a Marketing ROI Model.mp4

02:25

14. Assignment Part-2 Perform CICD of the Data Science ROI Model.mp4

01:21

15. Assignment Part-3 Deploy Model Serving Application to GCP CloudRun.mp4

02:17

5. Vertex AI For Data Science & Machine Learning

1. Section Overview.mp4

01:11

2. Introduction to Vertex AI Model Training Service.mp4

03:10

3. Overview of Bike Share Rentals Regression Model.mp4

01:37

4. Lab Vertex AI Model Training using Web Console and Gcloud CLI.mp4

11:59

5. Introduction to Vertex AI Model Registry.mp4

02:18

6. Lab Python SDK-Vertex AI Model Training,Model Registry and Model Deployment.mp4

09:37

7. Lab Execute Online & Batch prediction Service using Python SDK and jupter nbks.mp4

04:07

8. Lab-Walkthrough Batch Prediction Output & Online Prediction jobs using Cloud Run.mp4

10:10

9. Lab-Deploy and implement Batch Prediction Job using GCP Cloud Functions.mp4

07:50

10. Lab Overview of CICD using Vertex AI.mp4

02:41

11. Lab Vertex AI CICD of Data science model using CloudBuild.mp4

08:46

12. Assignment Deploy XGBoost Model to Vertex AI.mp4

02:37

6. Vertex AI-Kubeflow Pipelines for ML Workflow Orchestration

1. Introduction to Kubeflow for ML Orchestration.mp4

02:18

2. Different Components in Kubeflow Pipelines.mp4

04:25

3. Lab Deploy a simple pipeline for XgBoost Model.mp4

11:59

4. Lab Trigger Xgboost Model using compiled json for continuous training.mp4

03:51

5. Lab Execute end-to-end kubeflow pipeline with model evaluation.mp4

03:15

6. Lab Assignment Deploy a Scikit-Learn Credit Scoring Model to Vertex Pipelines.mp4

02:43

7. Introduction to Vertex AI Experiments.mp4

02:22

8. Lab Use different model hyperparameters for Xgboost with Vertex AI Experiments.mp4

06:36

9. LabTrain Different Data science Classification models using Experiments.mp4

04:53

10. Assignment Perform Experiments for Bike share Regression Model.mp4

01:24

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

1. Introduction to Hyperparameter Tuning on Vertex AI.mp4

02:12

2. Lab Implement Hyperparameter Tuning for BikeShare Regression Model.mp4

09:09

3. Lab Result Walkthrough & Assignment Overview.mp4

02:28

4. Lab Result Walkthrough & Assignment Overview.mp4

03:21

5. Lab Deploy Model Endpoint With Explainability Parameters.mp4

04:23

6. Lab Execute explainability for online predictions and Interpret results.mp4

04:35

7. Lab Execute explainability for online predictions and Interpret results.mp4

02:24

8. Assignment Perform Explainability for XgBoost Models.mp4

01:52

9. Introduction to Model Versioning using Vertex AI Model Registry.mp4

01:47

10. Lab Deploy different versions of XgBoost Model to Model Registry.mp4

09:31

11. Introduction to Vertex AI FeatureStore.mp4

03:17

12. Lab Create Feature store objects.mp4

03:32

13. Lab Ingest Data from Pandas DF into Feature Store.mp4

03:56

14. Lab Read Data From Vertex AI Feature Store into Pandas Df.mp4

06:11

15. Introduction to AutoML.mp4

01:25

16. Lab-Train and Deploy Classification Model using AutoML.mp4

04:52

17. Lab - Train and Deploy Regression Model using AutoML.mp4

04:19

8. Generative AI on Google Cloud

1.1 genai-section-source-code.zip

1. Introduction to Generative AI.mp4

04:12

2. Introduction to Large language models - PaLM 2.mp4

01:49

3. Important keywords and concepts in LLM.mp4

02:54

4. Lab-Generative AI Studio.mp4

05:44

5. Lab - Execute LLM using Python & Jupyter Nbk.mp4

05:11

6. Lab - Deploy text classification LLM Model using Python & Cloud Run.mp4

06:41

7. Lab-Deploy Document Summarization Application using Python & Cloud Run.mp4

05:21

8. Lab- Generate Fashion Image Descriptions using Python.mp4

04:59

Description

From Model Development to Deployment: Streamlining Machine Learning Workflows on Google Cloud

What You'll Learn?

Comprehensive understanding of Google Cloud Platform's 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 is this for?

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 GCP's 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.

What You Need to Know?

Basic experience in developing Data science models ,concepts and terminologies

Working knowledge of Python, as the course will involve hands-on coding and scripting

Prior basic understanding and experience on using Google Cloud platform

Desire to expand and deepen skill sets in MLOps and cloud-based machine learning solutions

More details

Description
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 â€“ 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 courseÂ :Â
CI/CDÂ Using Cloud Build,Container and Artifact Registry
Continuous Training using Airflow for MLÂ Workflow Orchestration:
Writing Test Cases
Vertex AIÂ Ecosystem using Python
Kubeflow Pipelines for ML Workflow and reusable ML components
Deploy Useful Applications using PaLM LLMÂ of GCPÂ Generative AIÂ
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!
Who this course is for:
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 GCP's 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.

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I am a Business oriented Data Architect with a vast experience in the field of Software Development,Distributed processing and data engineering on cloud . I have worked on different cloud platforms such as AWS & GCP and also with on-prem hadoop clusters. I also give seminars on Distributed processing using Spark , real time streaming and analytics and best practices for ETL and data governance.I am also a passionate coder ,love writing and building optimal data pipelines for robust data processing and streaming solutions .

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