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

Creation Time	Sept. 15, 2025, 9:31 a.m.
Last Access Time	Sept. 15, 2025, 10:01 a.m.
File Size	6.5 GB
Keywords	Pipelines 2025 ML - DevOps Udemy to Ultimate CICD MLOps 8 Bootcamp Build
Total Requests	2
Total Files	200
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02. Conceptual Introduction to MLOps/3. Comparing Three Approaches to AI.mp4 350.9 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 302.9 MB
02. Conceptual Introduction to MLOps/5. Comparing Devops vs MLOps.mp4 287.1 MB
02. Conceptual Introduction to MLOps/6. Emergence of MLOps Engineer.mp4 229.5 MB
07. Setting up MLOps CI Workflow with GitHub Actions/9. Modular, Multi Stage MLOps CI Workflow Pipeline.mp4 152.0 MB
02. Conceptual Introduction to MLOps/4. MLOps Case Studies – Learning from the Pioneers.mp4 124.0 MB
07. Setting up MLOps CI Workflow with GitHub Actions/4. Writing an executung out first GitHub Actions Workflow.mp4 118.7 MB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/2. Learning Data Engineering.mp4 117.3 MB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/6. Writing Dockerfile to package Model with FastAPI Wrapper.mp4 116.7 MB
10. GitOps Based Deployments for MLLLM Apps/6. Continuous Delivery with ArgoCD Applications.mp4 115.9 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 106.0 MB
09. Monitoring and Autoscaling a ML Model/5. Adding Instrumentation for FastAPI along with Custom Dashboard.mp4 103.2 MB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/9. Packaging and Model Serving Infra with Docker Compose.mp4 102.7 MB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/7. Debugging and Fixing Image Failures, Launch and Validate FastAPI.mp4 96.5 MB
09. Monitoring and Autoscaling a ML Model/13. Adding a Verticle Pod Autoscaler (VPA).mp4 92.3 MB
09. Monitoring and Autoscaling a ML Model/10. AI Based Troubleshooting Monitoring with ChatGPT.mp4 91.1 MB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/6. Deploying Streamlit Frontent App with Kubernetes.mp4 89.8 MB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/9. Connecting Streamlit with Model using Kubernetes native DNS Based Service Discov.mp4 86.7 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 84.6 MB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/3. Experimental Data Analysis.mp4 82.1 MB
03. Use Case and Environment Setup/4. Understanding End to End ML Practices and MLOps.mp4 81.3 MB
07. Setting up MLOps CI Workflow with GitHub Actions/6. Model Training Step with MLFlow for Tracking.mp4 79.4 MB
09. Monitoring and Autoscaling a ML Model/12. CPU Based Auto Scaling with KEDA.mp4 78.4 MB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/5. Wrapping the Model with FastAPI with Streamlit Client Apps.mp4 77.6 MB
09. Monitoring and Autoscaling a ML Model/4. Exploring Monitoring Metrics with Grafana and Prometheus.mp4 75.5 MB
09. Monitoring and Autoscaling a ML Model/11. Running Load Test and Analysing Autoscaling.mp4 75.4 MB
03. Use Case and Environment Setup/12. Summary.mp4 74.6 MB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/5. Simplest way to build a 3 Node Kubernetes Cluster with KIND.mp4 70.8 MB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/11. Module Summary.mp4 70.6 MB
03. Use Case and Environment Setup/10. Working with Jupyter Notebooks.mp4 68.4 MB
03. Use Case and Environment Setup/5. Environment Setup Overview.mp4 68.1 MB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/8. Packaging and testing Streamlit App.mp4 67.8 MB
03. Use Case and Environment Setup/8. Understanding the Project Directory and Scaffold.mp4 66.8 MB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/1. Module Intro.mp4 66.1 MB
03. Use Case and Environment Setup/1. Module Intro.mp4 66.0 MB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/1. Module Intro.mp4 65.7 MB
07. Setting up MLOps CI Workflow with GitHub Actions/8. Configurating Registry Token and publishing Image to DockerHub.mp4 65.2 MB
09. Monitoring and Autoscaling a ML Model/9. Getting started with Load Testing Model Inference.mp4 64.7 MB
10. GitOps Based Deployments for MLLLM Apps/8. Summary.mp4 63.8 MB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/8. Defining Algorithms and Hyperparameter Grids.mp4 62.3 MB
09. Monitoring and Autoscaling a ML Model/8. Configuring Scaled Objects with KEDA.mp4 61.3 MB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/5. Building New Features for House Price Predictor.mp4 59.8 MB
07. Setting up MLOps CI Workflow with GitHub Actions/1. Moule Intro.mp4 58.0 MB
07. Setting up MLOps CI Workflow with GitHub Actions/3. Understanding GitHub Actions Syntax.mp4 57.6 MB
09. Monitoring and Autoscaling a ML Model/3. Installing Prometheus and Grafana with Helm.mp4 57.3 MB
03. Use Case and Environment Setup/7. Launching MLflow for Experiemnt Tracking.mp4 57.3 MB
02. Conceptual Introduction to MLOps/1. What is MLOps.mp4 57.2 MB
10. GitOps Based Deployments for MLLLM Apps/5. Overview of Argo Application CRD.mp4 56.7 MB
07. Setting up MLOps CI Workflow with GitHub Actions/5. Adding Data and Feature Engineering Steps with Model Training.mp4 56.5 MB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/6. Preparing for Model Experimentation.mp4 56.0 MB
07. Setting up MLOps CI Workflow with GitHub Actions/11. Summary.mp4 54.0 MB
07. Setting up MLOps CI Workflow with GitHub Actions/7. Adding Image Build and Publish Step with Docker.mp4 53.4 MB
07. Setting up MLOps CI Workflow with GitHub Actions/2. DAGs, GitHub Actions and our MLOps CI Workflow.mp4 53.2 MB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/4. Building and Training Final Model with Configs from Data Scientists.mp4 52.1 MB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/7. Data Splitting with x_train, y_train, x_test, y_test.mp4 51.0 MB
05. Bonus Understanding the Core ML Algorithms/2. Linear Regression.mp4 50.3 MB
09. Monitoring and Autoscaling a ML Model/16. Summary.mp4 49.6 MB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/8. Creating Deployment Service for the Model wrapped in FastAPI.mp4 47.7 MB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/3. Introduction to Kubernetes for Machine Learning.mp4 47.4 MB
10. GitOps Based Deployments for MLLLM Apps/3. GitOps Pricinple 2 Start Revision Controling the Code.mp4 46.9 MB
05. Bonus Understanding the Core ML Algorithms/7. Neural Networking.mp4 45.2 MB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/3. Running Feature Engineering and Preprocessing Jobs.mp4 44.8 MB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/7. Exposing the Streamlit App with Kubernetes NodePort Service.mp4 44.4 MB
03. Use Case and Environment Setup/9. Setting up Python Virtual Environment with UV.mp4 42.9 MB
10. GitOps Based Deployments for MLLLM Apps/4. GitOps Principle 4 Setup a Agent - ArgoCD.mp4 42.9 MB
09. Monitoring and Autoscaling a ML Model/7. Installing KEDA and Configuring Resource Spec.mp4 42.8 MB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/1. Module Intro.mp4 42.0 MB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/12. Summary.mp4 41.5 MB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/4. Kubernetes Core Concepts - Pods, Deployments and Services.mp4 39.6 MB
05. Bonus Understanding the Core ML Algorithms/5. Random Forest.mp4 39.3 MB
03. Use Case and Environment Setup/6. Setting up Docker Podman with Compose.mp4 36.6 MB
09. Monitoring and Autoscaling a ML Model/6. Automatic Capacity Scaling Concepts.mp4 34.5 MB
03. Use Case and Environment Setup/2. Use Case - House Price Predictor - Regression.mp4 31.9 MB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/10. Easy way to Generate Kubernetes Manifets and YAML.mp4 31.7 MB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/2. Handover from Data Scientist to ML Engineer MLOps.mp4 30.4 MB
05. Bonus Understanding the Core ML Algorithms/9. Module Summary.mp4 29.6 MB
09. Monitoring and Autoscaling a ML Model/1. Module Intro.mp4 29.5 MB
05. Bonus Understanding the Core ML Algorithms/4. Decision Tree.mp4 28.2 MB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/4. Understaing Feature Engineering Concepts.mp4 28.2 MB
05. Bonus Understanding the Core ML Algorithms/3. Logistic Regression.mp4 26.6 MB
05. Bonus Understanding the Core ML Algorithms/6. Support Vector Machine (SVM).mp4 25.3 MB
10. GitOps Based Deployments for MLLLM Apps/2. GitOps Concepts.mp4 23.2 MB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/2. Designing Scalable Infrastructure for Model Inference.mp4 18.5 MB
05. Bonus Understanding the Core ML Algorithms/1. Module Intro.mp4 16.4 MB
10. GitOps Based Deployments for MLLLM Apps/1. Module Intro.mp4 14.7 MB
01. About this Course/1. Understand the MLOps Project you will Build in the Course.mp4 10.8 MB
09. Monitoring and Autoscaling a ML Model/2. Project Spec.mp4 6.7 MB
02. Conceptual Introduction to MLOps/2. M102-Story-of-AI-Infrastructure-Ops.pdf 4.3 MB
02. Conceptual Introduction to MLOps/4. M104-Case-Studies.pdf 3.6 MB
02. Conceptual Introduction to MLOps/3. M103-Understanding-ML-LLM-Agentic-AI.pdf 3.5 MB
02. Conceptual Introduction to MLOps/6. M105-The-Emergence-of-the-MLOps-Engineer.pdf 2.9 MB
02. Conceptual Introduction to MLOps/5. M106-MLOps-vs-DevOps-Understanding-the-Evolution.pdf 2.5 MB
02. Conceptual Introduction to MLOps/1. M101v2-What-is-MLOps.pdf 2.4 MB
07. Setting up MLOps CI Workflow with GitHub Actions/10. Lab 6 - MLOps CI PipelineWorkflow with GitHub Actions.pdf 1.1 MB
09. Monitoring and Autoscaling a ML Model/14. Lab 8 - Setting up Model Monitoring.pdf 946.8 KB
03. Use Case and Environment Setup/11. Lab 3 - Environment Setup.pdf 261.8 KB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/10. Lab 5 - Containerize and Deploy the Model with Streamlit App.pdf 219.5 KB
09. Monitoring and Autoscaling a ML Model/15. Lab 9 - Autoscaling Models.pdf 180.6 KB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/10. Lab 4 - From Data to Model.pdf 146.4 KB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/11. Lab 7 - Deploying to Kubernetes.pdf 51.5 KB
02. Conceptual Introduction to MLOps/3. Comparing Three Approaches to AI.vtt 29.5 KB
02. Conceptual Introduction to MLOps/1. What is MLOps.vtt 28.7 KB
02. Conceptual Introduction to MLOps/5. Comparing Devops vs MLOps.vtt 28.5 KB
10. GitOps Based Deployments for MLLLM Apps/7. End to End CI and CD Pielines for ML App.vtt 27.0 KB
03. Use Case and Environment Setup/4. Understanding End to End ML Practices and MLOps.vtt 26.9 KB
02. Conceptual Introduction to MLOps/2. Story of Evolution of MLOps, LLMOps and AgenticAIOps.vtt 22.1 KB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/6. Writing Dockerfile to package Model with FastAPI Wrapper.vtt 21.8 KB
02. Conceptual Introduction to MLOps/6. Emergence of MLOps Engineer.vtt 20.2 KB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/9. Packaging and Model Serving Infra with Docker Compose.vtt 18.2 KB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/2. Learning Data Engineering.vtt 17.9 KB
07. Setting up MLOps CI Workflow with GitHub Actions/9. Modular, Multi Stage MLOps CI Workflow Pipeline.vtt 17.8 KB
07. Setting up MLOps CI Workflow with GitHub Actions/2. DAGs, GitHub Actions and our MLOps CI Workflow.vtt 17.6 KB
07. Setting up MLOps CI Workflow with GitHub Actions/4. Writing an executung out first GitHub Actions Workflow.vtt 16.4 KB
02. Conceptual Introduction to MLOps/4. MLOps Case Studies – Learning from the Pioneers.vtt 16.1 KB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/6. Deploying Streamlit Frontent App with Kubernetes.vtt 16.0 KB
10. GitOps Based Deployments for MLLLM Apps/6. Continuous Delivery with ArgoCD Applications.vtt 14.4 KB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/3. Introduction to Kubernetes for Machine Learning.vtt 13.9 KB
09. Monitoring and Autoscaling a ML Model/13. Adding a Verticle Pod Autoscaler (VPA).vtt 13.5 KB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/7. Debugging and Fixing Image Failures, Launch and Validate FastAPI.vtt 13.4 KB
09. Monitoring and Autoscaling a ML Model/5. Adding Instrumentation for FastAPI along with Custom Dashboard.vtt 13.3 KB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/9. Connecting Streamlit with Model using Kubernetes native DNS Based Service Discov.vtt 13.2 KB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/5. Simplest way to build a 3 Node Kubernetes Cluster with KIND.vtt 12.4 KB
09. Monitoring and Autoscaling a ML Model/12. CPU Based Auto Scaling with KEDA.vtt 12.1 KB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/4. Kubernetes Core Concepts - Pods, Deployments and Services.vtt 12.1 KB
03. Use Case and Environment Setup/5. Environment Setup Overview.vtt 11.8 KB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/9. Running Model Experiments to find the Best Model and Hyperparamters.vtt 11.6 KB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/8. Packaging and testing Streamlit App.vtt 11.4 KB
09. Monitoring and Autoscaling a ML Model/10. AI Based Troubleshooting Monitoring with ChatGPT.vtt 11.1 KB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/3. Experimental Data Analysis.vtt 10.7 KB
07. Setting up MLOps CI Workflow with GitHub Actions/3. Understanding GitHub Actions Syntax.vtt 10.6 KB
09. Monitoring and Autoscaling a ML Model/11. Running Load Test and Analysing Autoscaling.vtt 10.5 KB
09. Monitoring and Autoscaling a ML Model/4. Exploring Monitoring Metrics with Grafana and Prometheus.vtt 10.3 KB
03. Use Case and Environment Setup/8. Understanding the Project Directory and Scaffold.vtt 10.1 KB
05. Bonus Understanding the Core ML Algorithms/8. Boosting Algorithms (XGBoost, LightGBM etc.).vtt 10.0 KB
01. About this Course/1. Understand the MLOps Project you will Build in the Course.vtt 9.7 KB
03. Use Case and Environment Setup/2. Use Case - House Price Predictor - Regression.vtt 9.7 KB
03. Use Case and Environment Setup/7. Launching MLflow for Experiemnt Tracking.vtt 9.4 KB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/5. Wrapping the Model with FastAPI with Streamlit Client Apps.vtt 9.0 KB
09. Monitoring and Autoscaling a ML Model/8. Configuring Scaled Objects with KEDA.vtt 8.9 KB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/2. Handover from Data Scientist to ML Engineer MLOps.vtt 8.6 KB
03. Use Case and Environment Setup/10. Working with Jupyter Notebooks.vtt 8.5 KB
07. Setting up MLOps CI Workflow with GitHub Actions/8. Configurating Registry Token and publishing Image to DockerHub.vtt 8.5 KB
09. Monitoring and Autoscaling a ML Model/9. Getting started with Load Testing Model Inference.vtt 8.4 KB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/7. Exposing the Streamlit App with Kubernetes NodePort Service.vtt 8.2 KB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/8. Creating Deployment Service for the Model wrapped in FastAPI.vtt 8.0 KB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/10. Easy way to Generate Kubernetes Manifets and YAML.vtt 8.0 KB
09. Monitoring and Autoscaling a ML Model/3. Installing Prometheus and Grafana with Helm.vtt 7.9 KB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/6. Preparing for Model Experimentation.vtt 7.8 KB
10. GitOps Based Deployments for MLLLM Apps/3. GitOps Pricinple 2 Start Revision Controling the Code.vtt 7.7 KB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/4. Understaing Feature Engineering Concepts.vtt 7.7 KB
07. Setting up MLOps CI Workflow with GitHub Actions/6. Model Training Step with MLFlow for Tracking.vtt 7.5 KB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/4. Building and Training Final Model with Configs from Data Scientists.vtt 7.5 KB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/8. Defining Algorithms and Hyperparameter Grids.vtt 7.4 KB
03. Use Case and Environment Setup/9. Setting up Python Virtual Environment with UV.vtt 7.3 KB
07. Setting up MLOps CI Workflow with GitHub Actions/7. Adding Image Build and Publish Step with Docker.vtt 7.2 KB
05. Bonus Understanding the Core ML Algorithms/2. Linear Regression.vtt 6.8 KB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/5. Building New Features for House Price Predictor.vtt 6.7 KB
05. Bonus Understanding the Core ML Algorithms/7. Neural Networking.vtt 6.6 KB
10. GitOps Based Deployments for MLLLM Apps/5. Overview of Argo Application CRD.vtt 6.5 KB
05. Bonus Understanding the Core ML Algorithms/5. Random Forest.vtt 6.4 KB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/7. Data Splitting with x_train, y_train, x_test, y_test.vtt 6.3 KB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/3. Running Feature Engineering and Preprocessing Jobs.vtt 6.2 KB
07. Setting up MLOps CI Workflow with GitHub Actions/5. Adding Data and Feature Engineering Steps with Model Training.vtt 6.1 KB
10. GitOps Based Deployments for MLLLM Apps/2. GitOps Concepts.vtt 6.1 KB
03. Use Case and Environment Setup/6. Setting up Docker Podman with Compose.vtt 6.0 KB
09. Monitoring and Autoscaling a ML Model/7. Installing KEDA and Configuring Resource Spec.vtt 5.9 KB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/2. Designing Scalable Infrastructure for Model Inference.vtt 5.9 KB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/11. Summary.vtt 5.8 KB
05. Bonus Understanding the Core ML Algorithms/4. Decision Tree.vtt 5.5 KB
10. GitOps Based Deployments for MLLLM Apps/4. GitOps Principle 4 Setup a Agent - ArgoCD.vtt 5.3 KB
03. Use Case and Environment Setup/12. Summary.vtt 5.3 KB
09. Monitoring and Autoscaling a ML Model/6. Automatic Capacity Scaling Concepts.vtt 4.5 KB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/1. Module Intro.vtt 4.5 KB
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/11. Module Summary.vtt 4.3 KB
03. Use Case and Environment Setup/1. Module Intro.vtt 4.2 KB
05. Bonus Understanding the Core ML Algorithms/3. Logistic Regression.vtt 4.1 KB
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/1. Module Intro.vtt 3.9 KB
10. GitOps Based Deployments for MLLLM Apps/8. Summary.vtt 3.8 KB
07. Setting up MLOps CI Workflow with GitHub Actions/1. Moule Intro.vtt 3.6 KB
07. Setting up MLOps CI Workflow with GitHub Actions/11. Summary.vtt 3.4 KB
09. Monitoring and Autoscaling a ML Model/2. Project Spec.vtt 3.3 KB
05. Bonus Understanding the Core ML Algorithms/6. Support Vector Machine (SVM).vtt 2.9 KB
09. Monitoring and Autoscaling a ML Model/16. Summary.vtt 2.8 KB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/12. Summary.vtt 2.4 KB
08. Building Scalable Prod Inference Infrastructure with Kubernetes/1. Module Intro.vtt 2.3 KB
05. Bonus Understanding the Core ML Algorithms/9. Module Summary.vtt 1.8 KB
09. Monitoring and Autoscaling a ML Model/1. Module Intro.vtt 1.7 KB
05. Bonus Understanding the Core ML Algorithms/1. Module Intro.vtt 928 bytes
10. GitOps Based Deployments for MLLLM Apps/1. Module Intro.vtt 705 bytes
01. About this Course/2. Join RealOps Builders Community on Discord.html 274 bytes
03. Use Case and Environment Setup/3. Fork and Clone the Repository.html 271 bytes
03. Use Case and Environment Setup/11. Download the Lab Guide.html 91 bytes
09. Monitoring and Autoscaling a ML Model/15. Lab - Setting up ML Autoscaling.html 89 bytes
08. Building Scalable Prod Inference Infrastructure with Kubernetes/11. Download the Lab Guide.html 86 bytes
04. From Data to Models - Understanding Data Science with Feature Engineering and Ex/10. Download the Lab Guide.html 85 bytes
07. Setting up MLOps CI Workflow with GitHub Actions/10. Download the Lab Guide.html 85 bytes
09. Monitoring and Autoscaling a ML Model/14. Lab - Setting up Monitoring with Prometheus and Grafana.html 79 bytes
06. Packaging Model along with FastAPI Wrapper and Streamlit with Containers/10. Download the Lab Guide.html 74 bytes

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

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