Real-world End to End Machine Learning Ops on Google Cloud
From Model Development to Deployment: Streamlining Machine Learning Workflows on Google Cloud
Lo que serás capaz de hacer
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!
- 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.
Temario
UNIDAD 1 - Introduction & prerequisites · 8 leccionesUNIDAD 1 - Introduction & prerequisites
UNIDAD 1 - Introduction & prerequisites
- Hello & Introduction
- Github Repository for this course
- Discord Server for this Course
- Lab-Create GCP Trial Account for the course
- Lab-Download gcloud-cli & project configuration
- Course prerequisites and installations
- Course Overview & section walkthrough
- GCP Services used in the course
UNIDAD 2 - Introduction to ML Ops · 2 leccionesUNIDAD 2 - Introduction to ML Ops
UNIDAD 2 - Introduction to ML Ops
- Introduction To ML-Ops
- Key Components Principles in ML-Ops
UNIDAD 3 - CI/CD using GCP CloudBuild,Artifact & Container Registry and CloudRun · 16 leccionesUNIDAD 3 - CI/CD using GCP CloudBuild,Artifact & Container Registry and CloudRun
UNIDAD 3 - CI/CD using GCP CloudBuild,Artifact & Container Registry and CloudRun
- Introduction to CI/CD on GCP
- Introduction to GCP Container Registry and Artifact Registry
- Lab : Enable necessary APIs and install modules
- Introduction To GCP CloudRun for ML Models
- Overview of Steps for Flask Application - Local development
- Lab : Deploy Flask application using Container/Artifact Registry and CloudRun
- Lab: Execute PyTest locally using ChatGPT
- Introduction to GCP CloudBuild Service
- Lab : Deploy Flask application using GCP CloudBuild
- Lab : Setup Cloudbuild Triggers from GitHub Repo
- XGBoost Model Overview for Coupon Recommendations Model
- Lab : Deploy and implement Model Serving Flask Application and Pytest Locally
- Lab : Deploy ML Model to CloudRun using CloudBuild
- Overview of A/B Testing for ML Models using CloudRun
- Lab : Deploy New Version of ML Model & Update version traffic
- Assignment - Deploy Bike Rentals Regression Model & perform CI/CD
UNIDAD 4 - Continuous Model Training using Cloud Composer-Airflow · 15 leccionesUNIDAD 4 - Continuous Model Training using Cloud Composer-Airflow
UNIDAD 4 - Continuous Model Training using Cloud Composer-Airflow
- Overview of Data science model for Bank Marketing Campaign
- Introduction to Continuous Training
- Introduction to Airflow For Continuous Training
- Lab: Create Setup Airflow composer Env and Vertex AI Workbench
- Lab: Execute Model Training using Jupyter-Nbk on GCP
- Lab: Execute Airflow Dag for Machine Learning Workflow
- Lab : Continuous Training Pipeline in Action
- Implications of Failure scenarios in Continuous Training
- Lab: Trigger Continuous Training to capture model logs and setup alerting
- Overview of CI/CD for Model Training
- Lab : CI/CD of Model Training Code using Cloud-Build,PyTest and Github
- Lab :Setup CloudBuild triggers
- Assignment Part-1 : Setup Continuous Training for a Marketing ROI Model
- Assignment Part-2 : Perform CICD of the Data Science ROI Model
- Assignment Part-3 : Deploy Model Serving Application to GCP CloudRun
UNIDAD 5 - Vertex AI For Data Science & Machine Learning · 13 leccionesUNIDAD 5 - Vertex AI For Data Science & Machine Learning
UNIDAD 5 - Vertex AI For Data Science & Machine Learning
- Section Overview
- Introduction to Vertex AI Model Training Service
- Overview of Bike Share Rentals Regression Model
- Lab : Vertex AI Model Training using Web Console and Gcloud CLI
- Introduction to Vertex AI Model Registry
- Lab : Python SDK-Vertex AI Model Training,Model Registry and Model Deployment
- Lab: Deploy from Model Registry, Canary Test, and Promote the Challenger version
- Lab:Execute online inference with Traffic Split,Batch Prediction with Python SDK
- Lab: Build a Cloud Run Prediction API in Front of a Vertex AI Endpoint
- Lab : Overview of CI/CD using Vertex AI
- Lab :Vertex AI : CI/CD of Data science model using CloudBuild
- Assignment-1: Real-Time Cart Conversion Scoring on Vertex AI
- Assignment-2: Predicting Product Returns on Vertex AI
UNIDAD 6 - Vertex AI-Kubeflow Pipelines for ML Workflow Orchestration · 10 leccionesUNIDAD 6 - Vertex AI-Kubeflow Pipelines for ML Workflow Orchestration
UNIDAD 6 - Vertex AI-Kubeflow Pipelines for ML Workflow Orchestration
- Introduction to Kubeflow for ML Orchestration
- Different Components in Kubeflow Pipelines
- Lab : Deploy a simple pipeline for XgBoost Model
- Lab : Trigger Xgboost Model using compiled json for continuous training
- Lab : Execute end-to-end kubeflow pipeline with model evaluation
- Lab Assignment: Deploy a Scikit-Learn Credit Scoring Model to Vertex Pipelines
- Introduction to Vertex AI Experiments
- Lab: Use different model hyperparameters for Xgboost with Vertex AI Experiments
- Lab:Train Different Data science Classification models using Experiments
- Assignment : Perform Experiments for Bike share Regression Model
UNIDAD 7 - Vertex AI-Hyperparameter Tuning Jobs, Explainability AI & Model Versioning · 17 leccionesUNIDAD 7 - Vertex AI-Hyperparameter Tuning Jobs, Explainability AI & Model Versioning
UNIDAD 7 - Vertex AI-Hyperparameter Tuning Jobs, Explainability AI & Model Versioning
- Introduction to Hyperparameter Tuning on Vertex AI
- Lab : Implement Hyperparameter Tuning for BikeShare Regression Model
- Lab : Result Walkthrough & Assignment Overview
- Overview - VertexAI Explainability
- Lab : Deploy Model Endpoint With Explainability Parameters
- Lab: Execute explainability for Online predictions and Interpret results
- Lab: Execute explainability for Batch predictions and Interpret results
- Assignment : Perform Explainability for XgBoost Models
- Introduction to Model Versioning using Vertex AI Model Registry
- Lab : Deploy different versions of XgBoost Model to Model Registry
- Introduction to Vertex AI FeatureStore
- Lab : Create Feature store objects
- Lab : Ingest Data from Pandas DF into Feature Store
- Lab : Read Data From Vertex AI Feature Store into Pandas Df
- Introduction to AutoML
- Lab-Train and Deploy Classification Model using AutoML
- Lab - Train and Deploy Regression Model using AutoML
UNIDAD 8 - Generative AI on Google Cloud · 16 leccionesUNIDAD 8 - Generative AI on Google Cloud
UNIDAD 8 - Generative AI on Google Cloud
- Introduction to Generative AI
- LLM Concepts & Prompting Essentials
- Lab - Your First Gemini Calls on Vertex AI
- Lab - Reliable Structured Output for Data Extraction
- Lab - Deploy a Document Summarization Service on Cloud Run
- Lab - Deploy a Support Ticket Triage API on Cloud Run
- Lab - Function Calling & Connecting Gemini to Live Systems
- Introduction to Grounding and RAG using Gemini
- Lab - Grounding and RAG, Stopping Hallucination
- Introduction to Batch inference using Gemini
- Lab - Batch Inference at Scale
- Introduction to LLM Model Evaluation
- Lab - Evaluating Gemini Output using Python SDK
- The LLM Production Checklist: Safety, Cost, and Observability
- Assignment-1 : Classify a Product Review Backlog with Gemini Batch Prediction
- Assignment-2: Proving a Model Upgrade with the Gen AI Evaluation Service
Tu certificado lo emite la SEP, no solo una plataforma
Al demostrar la competencia recibes un certificado emitido por la Secretaría de Educación Pública a través del Gobierno del Estado de México: un documento oficial que puedes presentar con confianza ante cualquier empleador o institución.
Tu certificado con INEC
- Lo emite la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México
- Documento oficial con folio verificable en línea
- Lo otorga la Universidad Tecnológica Fidel Velázquez
Otras plataformas en línea
- Constancia privada emitida por la misma plataforma
- Solo acredita horas cursadas o asistencia
- Sin respaldo de una autoridad educativa
Tu itinerario, de la inscripción al certificado
- 1
Inscripción con pago único
Pagas $2,800 MXN una sola vez y entras de inmediato al curso.
- 2
Estudias a tu ritmo
8 unidades con videos, lecturas y actividades, disponibles 24/7 durante 12 meses.
- 3
Cuestionarios por unidad
Cuestionarios para comprobar lo aprendido.
- 4
Certificado
Emitido por la SEP a través del Gobierno del Estado de México, con folio verificable.
Un certificado oficial de la SEP
Muestra · verificable por folio y QR
- Emitido por la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México. No es una constancia privada: es un documento oficial, y ese respaldo marca la diferencia frente a otras plataformas en línea.
- Contenido impartido por Sid Raghunath.
- Folio y QR verificables para compartirlo con quien te contrate.
Empieza hoy, a tu ritmo
Un solo pago y acceso inmediato al curso completo durante 12 meses.
- 8 unidades · 97 lecciones · 40 h
- 12 meses de acceso
- Certificado emitido por la SEP (Gob. del Estado de México) con folio verificable
Sobre tu inscripción
¿Cómo es el pago?
Un solo pago de $2,800 MXN. No hay mensualidades ni cargos recurrentes.
¿Cuánto tiempo tengo acceso?
12 meses desde tu inscripción, 24/7, para avanzar a tu ritmo.
¿Quién imparte el contenido?
El curso es impartido por Sid Raghunath; INEC gestiona tu inscripción, evaluación y certificado.
¿Qué pasa cuando vence mi acceso?
Puedes renovar tu cuenta tú mismo desde la plataforma, sin trámites ni esperas.
