Course Content
Introduction to MLOps
0/7
What is MLOps ?
20:22
Story of Evolution of MLOps, LLMOps and AgenticAIOps
16:38
Comparing Three Approaches to AI
22:28
MLOps Case Studies – Learning from the Pioneers
12:02
Comparing Devops vs MLOps
20:59
Emergence of MLOps Engineer
14:30
Introduction to MLOps
00:15:00
Getting started with the Use Case and Environment Setup
0/12
Module Intro
03:20
Use Case – House Price Predictor – Regression
07:06
Understanding End to End ML Practices and MLOps
19:30
Environment Setup Overview
09:24
Setting up Docker / Podman with Compose
04:40
Launching MLflow for Experiemnt Tracking
07:32
Understanding the Project Directory and Scaffold
07:46
Setting up Python Virtual Environment with UV
06:09
Working with Jupyter Notebooks
06:55
Summary
03:49
Lab Guide – Environment Setup
Getting Started with Use Case and Environment Setup
00:15:00
From Raw Data to Models
Understanding Data Science with Feature Engineering and Experimentation
0/12
Module Intro
03:22
Learning Data Engineering
12:54
Experimental Data Analysis
08:08
Understaing Feature Engineering Concepts
05:51
Building New Features for House Price Predictor
05:25
Preparing for Model Experimentation
06:18
Data Splitting with x_train, y_train, x_test, y_test
04:58
Defining Algorithms and Hyperparameter Grids
05:34
Running Model Experiments to find the Best Model and Hyperparamters
09:24
Module Summary
03:28
Lab Guide
From Raw Data to Models
00:15:00
Packaging Model along with FastAPI Wrapper and Streamlit with Containers
0/12
Module Intro
03:20
Handover from Data Scientist to ML Engineer / MLOps
06:36
Running Feature Engineering and Preprocessing Jobs
04:41
Building and Training Final Model with Configs from Data Scientists
06:03
Wrapping the Model with FastAPI with Streamlit Client Apps
06:47
Writing Dockerfile to package Model with FastAPI Wrapper
17:47
Debugging and Fixing Image Failures, Launch and Validate FastAPI
11:22
Packaging and testing Streamlit App
09:30
Packaging and Model Serving Infra with Docker Compose
14:53
Summary
04:22
Packaging Model with FastAPI, Streamlit, and Containers
00:15:00
Lab Guide
Setting up MLOps CI Workflow with GitHub Actions
0/12
Moule Intro
02:58
DAGs, GitHub Actions and our MLOps CI Workflow
12:40
Understanding GitHub Actions Syntax
07:39
Writing an executung out first GitHub Actions Workflow
13:51
Adding Data and Feature Engineering Steps with Model Training
05:53
Model Training Step with MLFlow for Tracking
07:43
Adding Image Build and Publish Step with Docker
05:53
Configurating Registry Token and publishing Image to DockerHub
06:51
Modular, Multi Stage MLOps CI Workflow Pipeline
14:16
Summary
02:39
Lab Guide
Setting up MLOps CI Workflow with GitHub Actions
00:15:00
Building Scalable Prod Inference Infrastructure with Kubernetes
0/13
Module Intro
02:00
Designing Scalable Infrastructure for Model Inference
04:35
Introduction to Kubernetes for Machine Learning
10:29
Kubernetes Core Concepts – Pods, Deployments and Services
09:13
Simplest way to build a 3 Node Kubernetes Cluster with KIND
10:04
Deploying Streamlit Frontent App with Kubernetes
12:05
Exposing the Streamlit App with Kubernetes NodePort Service
06:25
Creating Deployment & Service for the Model wrapped in FastAPI
07:11
Connecting Streamlit with Model using Kubernetes native DNS Based Service Discovery
11:10
Easy way to Generate Kubernetes Manifets and YAML
06:30
Summary
01:58
Scalable Inference Infrastructure with Kubernetes
00:15:00
Lab Guide
Monitoring and Autoscaling a ML Model – Prometheus, Grafana, KEDA, HPA, VPA
0/16
Module Intro
01:20
Project Spec
02:36
Installing Prometheus and Grafana with Helm
06:05
Exploring Monitoring Metrics with Grafana and Prometheus
08:17
Adding Instrumentation for FastAPI along with Custom Dashboard
11:29
Automatic Capacity Scaling Concepts
04:02
Installing KEDA and Configuring Resource Spec
05:22
Configuring Scaled Objects with KEDA
07:26
Getting started with Load Testing Model Inference
07:29
AI Based Troubleshooting Monitoring with ChatGPT
08:53
Running Load Test and Analysing Autoscaling
09:37
CPU Based Auto Scaling with KEDA
10:45
Adding a Verticle Pod Autoscaler (VPA)
11:42
Lab Guide – Setting up Monitoring with Prometheus and Grafana
Lab Guide – Autoscaling with KEDA, HPA and VPA
Summary
02:15
GitOps Based Deployments for ML/LLM Apps
0/8
Module Intro
00:40
GitOps Concepts
04:57
GitOps Pricinple 2 : Start Revision Controling the Code
06:06
GitOps Principle 4 : Setup a Agent – ArgoCD
04:05
Overview of Argo Application CRD
04:56
Continuous Delivery with ArgoCD Applications
12:01
End to End CI and CD Pielines for ML App
23:38
Module Summary – 333
02:53
Ultimate MLOps Bootcamp (Copy 1)
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