Part 1 - Mastering MLOps : Introduction

📢 Welcome to AICouncil! 🚀 In this first video of our MLOps tutorial series, we dive deep into the essentials of MLOps: Machine Learning + Operations. This series is designed to help you streamline the deployment and management of your ML models with ease and efficiency.
🔍 What's Inside This Video:
Understanding MLOps:
Automation: Learn how to use tools and scripts to perform repetitive tasks without human intervention, such as automatic model retraining and deployment when new data is available.
Agility: Discover how to quickly adapt and update models in response to changing data patterns and business requirements.
Collaboration: Enable seamless communication among team members using platforms like MLflow or Kubeflow.
Stages of MLOps:
Data Collection & Preparation:
Data ingestion, preparation & exploration
Defining goals and identifying data sources
Preparing, labeling, and exploring raw data
Converting raw data into features
Model Development & Training:
Getting data ready for ML models
Performing model training & validation
Evaluating ML models
ML Service Deployment:
Integrating with existing applications
Creating front ends
Containerizing applications
Setting up API services
Model endpoint creation
Continuous Feedback & Monitoring:
Tracking data & infrastructure
Monitoring models
Measuring application metrics
Essential Tools:
Git: The cornerstone of version control in MLOps.
MLflow, Jenkins, Grafana: Enhance collaboration, automation, and monitoring.
Cloud Computing: AWS, Azure, and more.
Deep Dive into Git:
Version Control: Manage changes to code, ensuring trackable updates and history.
Collaboration & Team Workflow: Facilitate team collaboration with branching, merging, and pull requests.
Experiment Tracking & Branching: Track different model versions and configurations; create branches for experimentation.
CI/CD & IaC: Automate testing and deployment; manage infrastructure through code.
Artifact Management: Store and track binaries and models.
Auditability & Compliance: Maintain detailed history for regulatory reviews.
📂 Hands-on Demo:
Triggering Python code files through the command prompt.
Creating and managing a local Git repository.
Join us on this exciting journey and empower your ML projects with the best MLOps practices!
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Keywords & Hashtags:
#MLOps #MachineLearning #Automation #Agility #Collaboration #DevOps #DataScience #MLflow #Git #Jenkins #Grafana #AWS #Azure #CI #CD #MLTutorial #AI #AICouncil #Python #DataEngineering #TechTutorial #ModelDeployment #DataPreparation #ContinuousIntegration #ContinuousDeployment #ExperimentTracking

[@kashifsadiq4145]

Great Series

[@kashifsadiq4145]

Cover in more detail mlops tools

[@aicouncil535]

Yes Will cover complete MLOps in this series