Day 2 - MLflow & DVC MLOps Tools Demo | MLOPs Production Ready Deep Learning Project

Показать описание

🚀MLOps Production Ready Deep Learning Project 🚀

🚀 Welcome to our comprehensive guide on creating a production-ready Deep learning project using MLOps! In this video, we will walk you through every step needed to successfully Implement, deploy, and manage your Deep learning models in a production environment.

Check out my other playlists:

This channel focuses on providing content on Data Science, Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Natural language processing, Python programming, etc. in Bangla and English.

My mission is to provide inspiration, motivation & good quality education to students for learning and human development, and to become an expert in Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Natural language processing, Python programming, and so on.

#dswithbappy aims to change this education system of Bangladesh.
I believe that high-quality education is not just for the privileged few. It is the right of everyone who seeks it. My aim is to bring quality education to every single student. All I need from you is intent, a ray of passion to learn.

Thanks!
#dswithbappy

Connect with me here:

🙏🙏🙏🙏🙏🙏🙏🙏
YOU JUST NEED TO DO
3 THINGS to support my channel
LIKE
SHARE
&
SUBSCRIBE
TO MY YOUTUBE CHANNEL

Рекомендации по теме

Комментарии

Thank you. You're such a great teacher but also a mentor

paulntalo

1:17:00 When running dvc repro I deleted the stage_02 outs: command and also parsed deps: url without python keyword as shown below and everything runs just fine. stages:
stage_01:
cmd: python pipeline/stage_01.py
deps:
- pipeline/stage_01.py

stage_02:
cmd: python pipeline/stage_02.py
deps:
- pipeline/stage_02.py

stage_03:
cmd: python pipeline/stage_03.py
deps:
- pipeline/stage_03.py
- artifacts/output.txt

paulntalo

Thanks for the video.Issue with the dvc yaml deps is we had "python" keyword in rows #5, #11 and #19.

praveenmek

Day 2 - MLflow & DVC MLOps Tools Demo | MLOPs Production Ready Deep Learning Project

Day 2 - MLflow & DVC MLOps Tools Demo | MLOPs Production Ready Deep Learning Project

Machine Learning Model Deployment with Python (Streamlit + MLflow) | Part 1/2

Machine Learning Model Deployment with Python (Streamlit + MLflow) | Part 2/2

Day 2 Morning Keynote | Data + AI Summit 2022

Airflow и MLFlow автоматизаций пайплайнов Machine Learning / MLOps

Introducing MLflow for End-to-End Machine Learning on Databricks

Advancing Spark - MLFlow 2 0

Introduction To MLflow-An Open Source Platform for the Machine Learning Lifecycle

Building a MLOps Platform Around MLflow to Enable Model Productionalization in Just a Few Minutes

MLflow: An Open Platform to Simplify the Machine Learning Lifecycle

MLflow Integration with PyCaret and PyTorch

Combining Machine Learning and MLflow with your Lakehouse. Data Brew | Season 1 Episode 5

Simplifying Model Development and Management with MLflow | Keynote Spark + AI Summit 2020

Getting Started With MLFlow

Managing the Complete Machine Learning Lifecycle with MLflow continues

Tutorial: Managing the end-to-end machine learning lifecycle with MLFlow

What is MLflow?

Simplifying Model Management with MLflow - Matei Zaharia (Databricks) Corey Zumar (Databricks)

Gitlab 15.11 Model experiments + MlFlow Integration Demo

Advancing Spark - Getting Started with MLFlow Pipelines

Machine Learning Data Lineage with MLflow and Delta Lake

Deploying Machine Learning Models with mlflow and Amazon SageMaker

MLflow Announcement | Keynote Data + AI Summit NA 2021

Introducing MLflow for End-to-End Machine Learning on Databricks