DevOps to MLOps Bootcamp - Build & Deploy ML Systems End-to-End

Creation Time	March 3, 2026, 5:14 p.m.
Last Access Time	March 3, 2026, 5:14 p.m.
File Size	2.7 GB
Keywords	Deploy ML - DevOps Bootcamp to Build MLOps End-to-End Systems
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Total Files	201
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Chapter 8 GitOps Based Deployments for ML LLM Apps/007. End-to-End CI and CD Pipelines for ML App.mp4 109.1 MB
Chapter 1 Introduction to MLOps/004. Comparing Three Approaches to AI.mp4 91.7 MB
Chapter 1 Introduction to MLOps/003. Story of Evolution of MLOps, LLMOps and AgenticAIOps.mp4 89.2 MB
Chapter 1 Introduction to MLOps/006. Comparing DevOps vs MLOps.mp4 74.7 MB
Chapter 1 Introduction to MLOps/002. What is MLOps.mp4 73.8 MB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/009. Modular, Multi-Stage MLOps CI Workflow Pipeline.mp4 67.7 MB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/006. Writing Dockerfile to Package Model with FastAPI Wrapp.mp4 59.4 MB
Chapter 1 Introduction to MLOps/007. Emergence of MLOps Engineer.mp4 56.9 MB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/004. Writing and Executing Our First GitHub Actions Workflow.mp4 55.8 MB
Chapter 1 Introduction to MLOps/001. Course Introduction.mp4 54.5 MB
Chapter 3 From Raw Data to Models/002. Learning Data Engineering.mp4 54.5 MB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/009. Packaging and Model Serving Infra with Docker Compose.mp4 51.1 MB
Chapter 1 Introduction to MLOps/005. MLOps Case Studies Learning from the Pioneers.mp4 50.3 MB
Chapter 8 GitOps Based Deployments for ML LLM Apps/006. Continuous Delivery with ArgoCD Applications.mp4 47.7 MB
Chapter 3 From Raw Data to Models/009. Running Model Experiments to Find the Best Model and Hyperparameters.mp4 47.1 MB
Chapter 7 Monitoring and Autoscaling an ML Model/005. Adding Instrumentation for FastAPI along with Custom Dashboard.mp4 46.5 MB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/007. Debugging and Fixing Image Failures, Launch and Valida.mp4 45.8 MB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/010. Summary.mp4 45.6 MB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/006. Deploying Streamlit Frontend App with Kubernetes.mp4 45.1 MB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/009. Connecting Streamlit with Model using Kubernetes Native DNS Bas.mp4 44.8 MB
Chapter 7 Monitoring and Autoscaling an ML Model/013. Adding a Vertical Pod Autoscaler (VPA).mp4 44.8 MB
Chapter 2 Getting Started with the Use Case and Environment Setup/003. Understanding End to End ML Practices and MLOps.mp4 42.0 MB
Chapter 7 Monitoring and Autoscaling an ML Model/010. AI Based Troubleshooting Monitoring with ChatGPT.mp4 41.3 MB
Chapter 2 Getting Started with the Use Case and Environment Setup/010. Summary.mp4 39.6 MB
Chapter 7 Monitoring and Autoscaling an ML Model/012. CPU Based Auto Scaling with KEDA.mp4 37.3 MB
Chapter 3 From Raw Data to Models/010. Module Summary.mp4 37.2 MB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/006. Model Training Step with MLFlow for Tracking.mp4 36.9 MB
Chapter 3 From Raw Data to Models/003. Experimental Data Analysis.mp4 36.5 MB
Chapter 2 Getting Started with the Use Case and Environment Setup/001. Module Intro.mp4 36.5 MB
Chapter 7 Monitoring and Autoscaling an ML Model/011. Running Load Test and Analyzing Autoscaling.mp4 36.1 MB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/005. Wrapping the Model with FastAPI with Streamlit Client .mp4 35.0 MB
Chapter 3 From Raw Data to Models/001. Module Intro.mp4 34.1 MB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/001. Module Intro.mp4 33.8 MB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/008. Packaging and Testing Streamlit App.mp4 33.7 MB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/005. Simplest Way to Build a 3 Node Kubernetes Cluster with KIND.mp4 33.0 MB
Chapter 2 Getting Started with the Use Case and Environment Setup/007. Understanding the Project Directory and Scaffold.mp4 32.4 MB
Chapter 2 Getting Started with the Use Case and Environment Setup/009. Working with Jupyter Notebooks.mp4 32.1 MB
Chapter 7 Monitoring and Autoscaling an ML Model/004. Exploring Monitoring Metrics with Grafana and Prometheus.mp4 31.7 MB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/008. Configuring Registry Token and Publishing Image to DockerHub.mp4 30.7 MB
Chapter 2 Getting Started with the Use Case and Environment Setup/004. Environment Setup Overview.mp4 30.5 MB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/001. Module Intro.mp4 30.5 MB
Chapter 7 Monitoring and Autoscaling an ML Model/009. Getting Started with Load Testing Model Inference.mp4 29.9 MB
Chapter 7 Monitoring and Autoscaling an ML Model/008. Configuring Scaled Objects with KEDA.mp4 29.8 MB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/010. Summary.mp4 28.3 MB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/002. DAGs, GitHub Actions and our MLOps CI Workflow.mp4 28.3 MB
Chapter 3 From Raw Data to Models/005. Building New Features for House Price Predictor.mp4 28.1 MB
Chapter 7 Monitoring and Autoscaling an ML Model/003. Installing Prometheus and Grafana with Helm.mp4 28.1 MB
Chapter 2 Getting Started with the Use Case and Environment Setup/006. Launching MLflow for Experiment Tracking.mp4 28.0 MB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/005. Adding Data and Feature Engineering Steps with Model Training.mp4 27.3 MB
Chapter 3 From Raw Data to Models/006. Preparing for Model Experimentation.mp4 27.3 MB
Chapter 3 From Raw Data to Models/008. Defining Algorithms and Hyperparameter Grids.mp4 26.5 MB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/003. Understanding GitHub Actions Syntax.mp4 26.4 MB
Chapter 3 From Raw Data to Models/007. Data Splitting with x train, y train, x test, y test.mp4 25.9 MB
Chapter 8 GitOps Based Deployments for ML LLM Apps/008. Summary.mp4 25.5 MB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/007. Adding Image Build and Publish Step with Docker.mp4 25.0 MB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/004. Building and Training Final Model with Configs from Da.mp4 23.7 MB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/008. Creating Deployment & Service for the Model Wrapped in FastAPI.mp4 23.3 MB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/007. Exposing the Streamlit App with Kubernetes NodePort Service.mp4 23.0 MB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/003. Introduction to Kubernetes for Machine Learning.mp4 22.2 MB
Chapter 7 Monitoring and Autoscaling an ML Model/007. Installing KEDA and Configuring Resource Spec.mp4 21.9 MB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/003. Running Feature Engineering and Preprocessing Jobs.mp4 21.6 MB
Chapter 2 Getting Started with the Use Case and Environment Setup/008. Setting up Python Virtual Environment with UV.mp4 21.5 MB
Chapter 8 GitOps Based Deployments for ML LLM Apps/003. GitOps Principle 2 - Start Revision Controlling the Code.mp4 21.4 MB
Chapter 8 GitOps Based Deployments for ML LLM Apps/005. Overview of Argo Application CRD.mp4 20.1 MB
Chapter 7 Monitoring and Autoscaling an ML Model/014. Summary.mp4 19.4 MB
Chapter 8 GitOps Based Deployments for ML LLM Apps/004. GitOps Principle 4 - Setup an Agent - ArgoCD.mp4 19.1 MB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/004. Kubernetes Core Concepts Pods, Deployments and Services.mp4 18.9 MB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/001. Module Intro.mp4 18.2 MB
Chapter 2 Getting Started with the Use Case and Environment Setup/005. Setting up Docker Podman with Compose.mp4 16.7 MB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/010. Easy Way to Generate Kubernetes Manifests and YAML.mp4 16.0 MB
Chapter 7 Monitoring and Autoscaling an ML Model/006. Automatic Capacity Scaling Concepts.mp4 15.8 MB
Chapter 2 Getting Started with the Use Case and Environment Setup/002. Use Case House Price Predictor Regression.mp4 15.2 MB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/011. Summary.mp4 15.1 MB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/002. Handover from Data Scientist to ML Engineer MLOps.mp4 13.4 MB
Chapter 3 From Raw Data to Models/004. Understanding Feature Engineering Concepts.mp4 13.4 MB
Chapter 7 Monitoring and Autoscaling an ML Model/001. Module Intro.mp4 12.3 MB
Chapter 8 GitOps Based Deployments for ML LLM Apps/002. GitOps Concepts.mp4 10.4 MB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/002. Designing Scalable Infrastructure for Model Inference.mp4 9.1 MB
Chapter 8 GitOps Based Deployments for ML LLM Apps/001. Module Intro.mp4 5.8 MB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S01 - Introduction to MLOps/M102-Story-of-AI-Infrastructure-Ops.pdf 4.3 MB
Chapter 7 Monitoring and Autoscaling an ML Model/002. Project Spec.mp4 3.8 MB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S01 - Introduction to MLOps/M104-Case-Studies.pdf 3.6 MB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S01 - Introduction to MLOps/M103-Understanding-ML-LLM-Agentic-AI.pdf 3.5 MB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S01 - Introduction to MLOps/M105-The-Emergence-of-the-MLOps-Engineer.pdf 2.9 MB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S01 - Introduction to MLOps/M106-MLOps-vs-DevOps-Understanding-the-Evolution.pdf 2.5 MB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S01 - Introduction to MLOps/M101v2-What-is-MLOps.pdf 2.4 MB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S07 - Monitoring and Autoscaling an ML Model/Lab 8 - Setting up Model Monitoring.pdf 946.8 KB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S07 - Monitoring and Autoscaling an ML Model/Lab 9 - Autoscaling Models.pdf 180.6 KB
Chapter 1 Introduction to MLOps/004. Comparing Three Approaches to AI.en.srt 36.7 KB
Chapter 1 Introduction to MLOps/006. Comparing DevOps vs MLOps.en.srt 35.7 KB
Chapter 1 Introduction to MLOps/002. What is MLOps.en.srt 33.8 KB
Chapter 8 GitOps Based Deployments for ML LLM Apps/007. End-to-End CI and CD Pipelines for ML App.en.srt 33.2 KB
Chapter 2 Getting Started with the Use Case and Environment Setup/003. Understanding End to End ML Practices and MLOps.en.srt 32.8 KB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/006. Writing Dockerfile to Package Model with FastAPI Wrapper.en.srt 26.6 KB
Chapter 1 Introduction to MLOps/003. Story of Evolution of MLOps, LLMOps and AgenticAIOps.en.srt 26.5 KB
Chapter 1 Introduction to MLOps/007. Emergence of MLOps Engineer.en.srt 24.2 KB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/009. Packaging and Model Serving Infra with Docker Compose.en.srt 22.3 KB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/009. Modular, Multi-Stage MLOps CI Workflow Pipeline.en.srt 21.8 KB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/002. DAGs, GitHub Actions and our MLOps CI Workflow.en.srt 20.9 KB
Chapter 3 From Raw Data to Models/002. Learning Data Engineering.en.srt 20.3 KB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/004. Writing and Executing Our First GitHub Actions Workflow.en.srt 19.5 KB
Chapter 1 Introduction to MLOps/005. MLOps Case Studies Learning from the Pioneers.en.srt 19.2 KB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/006. Deploying Streamlit Frontend App with Kubernetes.en.srt 18.7 KB
Chapter 8 GitOps Based Deployments for ML LLM Apps/006. Continuous Delivery with ArgoCD Applications.en.srt 17.4 KB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/003. Introduction to Kubernetes for Machine Learning.en.srt 17.2 KB
Chapter 7 Monitoring and Autoscaling an ML Model/005. Adding Instrumentation for FastAPI along with Custom Dashboard.en.srt 16.9 KB
Chapter 7 Monitoring and Autoscaling an ML Model/013. Adding a Vertical Pod Autoscaler (VPA).en.srt 16.5 KB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/009. Connecting Streamlit with Model using Kubernetes Native DNS Based Service Discovery.en.srt 16.3 KB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/005. Simplest Way to Build a 3 Node Kubernetes Cluster with KIND.en.srt 16.2 KB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/007. Debugging and Fixing Image Failures, Launch and Validate FastAPI.en.srt 16.1 KB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/004. Kubernetes Core Concepts Pods, Deployments and Services.en.srt 14.9 KB
Chapter 7 Monitoring and Autoscaling an ML Model/012. CPU Based Auto Scaling with KEDA.en.srt 14.7 KB
Chapter 2 Getting Started with the Use Case and Environment Setup/004. Environment Setup Overview.en.srt 14.3 KB
Chapter 3 From Raw Data to Models/009. Running Model Experiments to Find the Best Model and Hyperparameters.en.srt 14.1 KB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/008. Packaging and Testing Streamlit App.en.srt 14.0 KB
Chapter 7 Monitoring and Autoscaling an ML Model/010. AI Based Troubleshooting Monitoring with ChatGPT.en.srt 13.0 KB
Chapter 7 Monitoring and Autoscaling an ML Model/011. Running Load Test and Analyzing Autoscaling.en.srt 12.6 KB
Chapter 7 Monitoring and Autoscaling an ML Model/004. Exploring Monitoring Metrics with Grafana and Prometheus.en.srt 12.6 KB
Chapter 3 From Raw Data to Models/003. Experimental Data Analysis.en.srt 12.6 KB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/003. Understanding GitHub Actions Syntax.en.srt 12.5 KB
Chapter 2 Getting Started with the Use Case and Environment Setup/007. Understanding the Project Directory and Scaffold.en.srt 12.0 KB
Chapter 2 Getting Started with the Use Case and Environment Setup/006. Launching MLflow for Experiment Tracking.en.srt 11.6 KB
Chapter 2 Getting Started with the Use Case and Environment Setup/002. Use Case House Price Predictor Regression.en.srt 11.2 KB
Chapter 7 Monitoring and Autoscaling an ML Model/008. Configuring Scaled Objects with KEDA.en.srt 10.8 KB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/005. Wrapping the Model with FastAPI with Streamlit Client Apps.en.srt 10.4 KB
Chapter 7 Monitoring and Autoscaling an ML Model/009. Getting Started with Load Testing Model Inference.en.srt 10.0 KB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/008. Configuring Registry Token and Publishing Image to DockerHub.en.srt 10.0 KB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/007. Exposing the Streamlit App with Kubernetes NodePort Service.en.srt 9.9 KB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/010. Easy Way to Generate Kubernetes Manifests and YAML.en.srt 9.9 KB
Chapter 2 Getting Started with the Use Case and Environment Setup/009. Working with Jupyter Notebooks.en.srt 9.9 KB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/002. Handover from Data Scientist to ML Engineer MLOps.en.srt 9.9 KB
Chapter 7 Monitoring and Autoscaling an ML Model/003. Installing Prometheus and Grafana with Helm.en.srt 9.8 KB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/008. Creating Deployment & Service for the Model Wrapped in FastAPI.en.srt 9.8 KB
Chapter 8 GitOps Based Deployments for ML LLM Apps/003. GitOps Principle 2 - Start Revision Controlling the Code.en.srt 9.4 KB
Chapter 3 From Raw Data to Models/006. Preparing for Model Experimentation.en.srt 9.3 KB
Chapter 1 Introduction to MLOps/001. Course Introduction.en.srt 9.3 KB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/006. Model Training Step with MLFlow for Tracking.en.srt 9.1 KB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/notebooks/03_experimentation.ipynb 9.1 KB
Chapter 3 From Raw Data to Models/004. Understanding Feature Engineering Concepts.en.srt 9.0 KB
Chapter 2 Getting Started with the Use Case and Environment Setup/008. Setting up Python Virtual Environment with UV.en.srt 9.0 KB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/004. Building and Training Final Model with Configs from Data Scientists.en.srt 8.9 KB
Chapter 3 From Raw Data to Models/008. Defining Algorithms and Hyperparameter Grids.en.srt 8.7 KB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/007. Adding Image Build and Publish Step with Docker.en.srt 8.6 KB
Chapter 8 GitOps Based Deployments for ML LLM Apps/005. Overview of Argo Application CRD.en.srt 8.0 KB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/streamlit_app/app.py 7.8 KB
Chapter 7 Monitoring and Autoscaling an ML Model/007. Installing KEDA and Configuring Resource Spec.en.srt 7.8 KB
Chapter 3 From Raw Data to Models/005. Building New Features for House Price Predictor.en.srt 7.6 KB
Chapter 8 GitOps Based Deployments for ML LLM Apps/002. GitOps Concepts.en.srt 7.6 KB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/002. Designing Scalable Infrastructure for Model Inference.en.srt 7.3 KB
Chapter 2 Getting Started with the Use Case and Environment Setup/005. Setting up Docker Podman with Compose.en.srt 7.1 KB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/010. Summary.en.srt 7.1 KB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/003. Running Feature Engineering and Preprocessing Jobs.en.srt 7.0 KB
Chapter 3 From Raw Data to Models/007. Data Splitting with x train, y train, x test, y test.en.srt 7.0 KB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/005. Adding Data and Feature Engineering Steps with Model Training.en.srt 6.9 KB
Chapter 8 GitOps Based Deployments for ML LLM Apps/004. GitOps Principle 4 - Setup an Agent - ArgoCD.en.srt 6.3 KB
Chapter 2 Getting Started with the Use Case and Environment Setup/010. Summary.en.srt 6.3 KB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/notebooks/00_data_engineering.ipynb 6.1 KB
Chapter 7 Monitoring and Autoscaling an ML Model/006. Automatic Capacity Scaling Concepts.en.srt 6.0 KB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/src/models/train_model.py 5.7 KB
Chapter 3 From Raw Data to Models/001. Module Intro.en.srt 5.6 KB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/README.md 5.2 KB
Chapter 3 From Raw Data to Models/010. Module Summary.en.srt 5.2 KB
Chapter 2 Getting Started with the Use Case and Environment Setup/001. Module Intro.en.srt 5.2 KB
Chapter 4 Packaging Model along with FastAPI Wrapper and Streamlit with Containers/001. Module Intro.en.srt 5.0 KB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S05 - Setup MLOps CLI Workflow with GitHub Actions/mlops-pipeline.yaml 5.0 KB
Chapter 8 GitOps Based Deployments for ML LLM Apps/008. Summary.en.srt 4.9 KB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/notebooks/01_exploratory_data_analysis.ipynb 4.8 KB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/notebooks/02_feature_engineering.ipynb 4.6 KB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/001. Module Intro.en.srt 4.5 KB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/src/features/engineer.py 4.1 KB
Chapter 7 Monitoring and Autoscaling an ML Model/002. Project Spec.en.srt 4.0 KB
Chapter 5 Setting up MLOps CI Workflow with GitHub Actions/010. Summary.en.srt 3.8 KB
Chapter 7 Monitoring and Autoscaling an ML Model/014. Summary.en.srt 3.6 KB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/data/raw/house_data.csv 3.1 KB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/src/data/run_processing.py 3.0 KB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/011. Summary.en.srt 3.0 KB
Chapter 6 Building Scalable Prod Inference Infrastructure with Kubernetes/001. Module Intro.en.srt 3.0 KB
Chapter 7 Monitoring and Autoscaling an ML Model/001. Module Intro.en.srt 2.3 KB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/src/api/inference.py 2.3 KB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/requirements.txt 1.9 KB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/src/api/main.py 1.4 KB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/LICENSE 1.0 KB
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/LICENSE 1.0 KB
Chapter 8 GitOps Based Deployments for ML LLM Apps/001. Module Intro.en.srt 992 bytes
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/deployment/kubernetes/deployment.yaml 881 bytes
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/src/api/schemas.py 755 bytes
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S01 - Introduction to MLOps/S01 - Useful Resource Links.txt 708 bytes
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/src/api/README.md 646 bytes
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/.gitignore 527 bytes
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/streamlit_app/.streamlit/config.toml 334 bytes
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/streamlit_app/README.md 300 bytes
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/deployment/mlflow/docker-compose.yaml 265 bytes
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/README.md 160 bytes
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/Git Repository for the Course.txt 145 bytes
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/src/api/requirements.txt 121 bytes
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S05 - Setup MLOps CLI Workflow with GitHub Actions/S05 - Useful Resource Links.txt 114 bytes
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S03 - From Data to Models - Understanding Data Science with Feature Engineering and Experimentation/S03 - Useful Resource Links.txt 75 bytes
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/data/processed/README.md 42 bytes
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/streamlit_app/requirements.txt 35 bytes
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/models/trained/README.md 30 bytes
z.DevOps-to-MLOps-Bootcamp-Build-Deploy-ML-Systems-End-to-End-main/S02 - Use Case and Environment Setup/house-price-predictor-main/src/api/utils.py 0 bytes

DevOps to MLOps Bootcamp - Build & Deploy ML Systems End-to-End

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