What is MLOps?
? MLOps Labs for Free: https://kode.wiki/3JoF2e0
Struggling with model drift and deployment chaos? This beginner-friendly video explains MLOps in simple terms + includes FREE hands-on MLOps labs!
No experience needed! This video breaks down MLOps (Machine Learning Operations) into easy-to-understand concepts with real examples. You'll discover why maintaining ML models is expensive, how to solve model and data drift, and why MLflow is essential for production ML systems.
What's Inside:
? Simple MLOps explanation (no jargon!)
? Understanding model drift vs data drift
? Free practice labs with hands-on exercises
Before MLOps, deploying ML models was fast and cheap, but maintaining them was a nightmare! MLOps bridges this gap by automating pipelines, versioning models, and ensuring continuous availability. Think of it like DevOps, but for machine learning systems.
? Hands-On Labs Included:
The tutorial demonstrates tracking experiments with MLflow, comparing hyperparameters across multiple runs, evaluating different ML algorithms, and registering models for production deployment. You'll work with linear regression, random forests, and gradient boosting while learning industry best practices.
? MLOps Labs for Free: https://kode.wiki/3JoF2e0
⏰ TIMESTAMPS:
00:00 - What is MLOps?
00:32 - The Hidden Technical Debt Problem
01:03 - Model Drift vs Data Drift Explained
01:41 - How MLOps Solves These Challenges
03:53 - Introduction to MLflow
04:47 - Hands-On Labs Overview
05:22 - Task 1: Tracking Your First Experiment
07:24 - Task 2: Hyperparameter Tuning
09:33 - Task 3: Model Comparison
11:05 - Task 4: Model Registry & Production
Perfect for ML engineers, data scientists, DevOps engineers, and anyone building production machine learning systems.
? Start Your AI Journey with KodeKloud: https://kode.wiki/41NLyks
? Subscribe to KodeKloud for more MLOps tutorials and hands-on labs!
#MLOps #MLflow #MachineLearning #DataScience #ModelDrift #DataDrift #DevOps #AI #kodekloud
Struggling with model drift and deployment chaos? This beginner-friendly video explains MLOps in simple terms + includes FREE hands-on MLOps labs!
No experience needed! This video breaks down MLOps (Machine Learning Operations) into easy-to-understand concepts with real examples. You'll discover why maintaining ML models is expensive, how to solve model and data drift, and why MLflow is essential for production ML systems.
What's Inside:
? Simple MLOps explanation (no jargon!)
? Understanding model drift vs data drift
? Free practice labs with hands-on exercises
Before MLOps, deploying ML models was fast and cheap, but maintaining them was a nightmare! MLOps bridges this gap by automating pipelines, versioning models, and ensuring continuous availability. Think of it like DevOps, but for machine learning systems.
? Hands-On Labs Included:
The tutorial demonstrates tracking experiments with MLflow, comparing hyperparameters across multiple runs, evaluating different ML algorithms, and registering models for production deployment. You'll work with linear regression, random forests, and gradient boosting while learning industry best practices.
? MLOps Labs for Free: https://kode.wiki/3JoF2e0
⏰ TIMESTAMPS:
00:00 - What is MLOps?
00:32 - The Hidden Technical Debt Problem
01:03 - Model Drift vs Data Drift Explained
01:41 - How MLOps Solves These Challenges
03:53 - Introduction to MLflow
04:47 - Hands-On Labs Overview
05:22 - Task 1: Tracking Your First Experiment
07:24 - Task 2: Hyperparameter Tuning
09:33 - Task 3: Model Comparison
11:05 - Task 4: Model Registry & Production
Perfect for ML engineers, data scientists, DevOps engineers, and anyone building production machine learning systems.
? Start Your AI Journey with KodeKloud: https://kode.wiki/41NLyks
? Subscribe to KodeKloud for more MLOps tutorials and hands-on labs!
#MLOps #MLflow #MachineLearning #DataScience #ModelDrift #DataDrift #DevOps #AI #kodekloud
KodeKloud
...