Udemy - Ultimate DevOps to MLOps Bootcamp - Build ML CICD Pipelines (8.2025)

• 02. Conceptual Introduction to MLOps/3. Comparing Three Approaches to AI.mp4-350.94 MB
• 02. Conceptual Introduction to MLOps/2. Story of Evolution of MLOps, LLMOps and AgenticAIOps.mp4-308.4 MB
• 10. GitOps Based Deployments for MLLLM Apps/7. End to End CI and CD Pielines for ML App.mp4-302.91 MB
• 02. Conceptual Introduction to MLOps/5. Comparing Devops vs MLOps.mp4-287.07 MB
• 02. Conceptual Introduction to MLOps/6. Emergence of MLOps Engineer.mp4-229.53 MB
• 07. Setting up MLOps CI Workflow with GitHub Actions/9. Modular, Multi Stage MLOps CI Workflow Pipeline.mp4-151.95 MB
• 02. Conceptual Introduction to MLOps/4. MLOps Case Studies – Learning from the Pioneers.mp4-123.99 MB
• 07. Setting up MLOps CI Workflow with GitHub Actions/4. Writing an executung out first GitHub Actions Workflow.mp4-118.71 MB
• 04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/2. Learning Data Engineering.mp4-117.28 MB
• 06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/6. Writing Dockerfile to package Model with FastAPI Wrapper.mp4-116.67 MB
• 10. GitOps Based Deployments for MLLLM Apps/6. Continuous Delivery with ArgoCD Applications.mp4-115.91 MB
• 04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/9. Running Model Experiments to find the Best Model and Hyperparamters.mp4-105.99 MB
• 09. Monitoring and Autoscaling a ML Model/5. Adding Instrumentation for FastAPI along with Custom Dashboard.mp4-103.15 MB
• 06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/9. Packaging and Model Serving Infra with Docker Compose.mp4-102.73 MB
• 06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/7. Debugging and Fixing Image Failures, Launch and Validate FastAPI.mp4-96.51 MB
• 09. Monitoring and Autoscaling a ML Model/13. Adding a Verticle Pod Autoscaler (VPA).mp4-92.34 MB
• 09. Monitoring and Autoscaling a ML Model/10. AI Based Troubleshooting Monitoring with ChatGPT.mp4-91.05 MB
• 08. Building Scalable Prod Inference Infrastructure with Kubernetes/6. Deploying Streamlit Frontent App with Kubernetes.mp4-89.83 MB
• 08. Building Scalable Prod Inference Infrastructure with Kubernetes/9. Connecting Streamlit with Model using Kubernetes native DNS Based Service Discov.mp4-86.65 MB
• 06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/11. Summary.mp4-85.8 MB
• 05. Bonus Understanding the Core ML Algorithms/8. Boosting Algorithms (XGBoost, LightGBM etc.).mp4-84.57 MB
• 04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/3. Experimental Data Analysis.mp4-82.09 MB
• 03. Use Case and Environment Setup/4. Understanding End to End ML Practices and MLOps.mp4-81.28 MB
• 07. Setting up MLOps CI Workflow with GitHub Actions/6. Model Training Step with MLFlow for Tracking.mp4-79.43 MB
• 09. Monitoring and Autoscaling a ML Model/12. CPU Based Auto Scaling with KEDA.mp4-78.41 MB
• 06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/5. Wrapping the Model with FastAPI with Streamlit Client Apps.mp4-77.62 MB
• 09. Monitoring and Autoscaling a ML Model/4. Exploring Monitoring Metrics with Grafana and Prometheus.mp4-75.46 MB
• 09. Monitoring and Autoscaling a ML Model/11. Running Load Test and Analysing Autoscaling.mp4-75.37 MB
• 03. Use Case and Environment Setup/12. Summary.mp4-74.64 MB
• 08. Building Scalable Prod Inference Infrastructure with Kubernetes/5. Simplest way to build a 3 Node Kubernetes Cluster with KIND.mp4-70.82 MB
• 04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/11. Module Summary.mp4-70.57 MB
• 03. Use Case and Environment Setup/10. Working with Jupyter Notebooks.mp4-68.44 MB
• 03. Use Case and Environment Setup/5. Environment Setup Overview.mp4-68.07 MB
• 06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/8. Packaging and testing Streamlit App.mp4-67.75 MB
• 03. Use Case and Environment Setup/8. Understanding the Project Directory and Scaffold.mp4-66.77 MB
• 04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/1. Module Intro.mp4-66.13 MB
• 03. Use Case and Environment Setup/1. Module Intro.mp4-65.98 MB
• 06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/1. Module Intro.mp4-65.75 MB
• 07. Setting up MLOps CI Workflow with GitHub Actions/8. Configurating Registry Token and publishing Image to DockerHub.mp4-65.19 MB
• 09. Monitoring and Autoscaling a ML Model/9. Getting started with Load Testing Model Inference.mp4-64.69 MB
• 10. GitOps Based Deployments for MLLLM Apps/8. Summary.mp4-63.77 MB
• 04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/8. Defining Algorithms and Hyperparameter Grids.mp4-62.31 MB
• 09. Monitoring and Autoscaling a ML Model/8. Configuring Scaled Objects with KEDA.mp4-61.35 MB
• 04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/5. Building New Features for House Price Predictor.mp4-59.78 MB
• 07. Setting up MLOps CI Workflow with GitHub Actions/1. Moule Intro.mp4-58.04 MB
• 07. Setting up MLOps CI Workflow with GitHub Actions/3. Understanding GitHub Actions Syntax.mp4-57.65 MB
• 09. Monitoring and Autoscaling a ML Model/3. Installing Prometheus and Grafana with Helm.mp4-57.28 MB
• 03. Use Case and Environment Setup/7. Launching MLflow for Experiemnt Tracking.mp4-57.25 MB
• 02. Conceptual Introduction to MLOps/1. What is MLOps.mp4-57.25 MB
• 10. GitOps Based Deployments for MLLLM Apps/5. Overview of Argo Application CRD.mp4-56.67 MB