Full Stack Deep Learning with Python Introduction | Introducing ML Ops | Introducing ML flow
Introduction
Full-stack deep learning, MLOps, and MLflow
Prerequisites
1. An Overview of Full-Stack Deep Learning
Introducing full-stack deep learning
Introducing MLOps
Introducing MLflow
Setting up the environment on Google Colab
Running MLflow and using ngrok to access the MLflow UI
MLFlow Tutorial | ML Ops Tutorial
How to work with google Colab |Environment Setup|
2. Model Training and Evaluation Using MLflow
Loading and exploring the EMNIST dataset(In progress)
Logging metrics, parameters, and artifacts in MLflow
Set up the dataset and data loader
Configuring the image classification DNN model
Training a model within an MLflow run
Exploring parameters and metrics in MLflow
Making predictions using MLflow artifacts
End to end Deep Learning Project Implementation using MLOps Tool MLflow & DVC with CICD Deployment |
If you seek a more in-depth understanding of deep learning and Python, this hands-on course can help you. In this course, certified Google cloud architect and data engineer Janani Ravi guides you through the intricacies of full-stack deep learning with Python. After a review of full stack deep learning, MLOps, and MLflow, dive into setting up your environment on Google Colab and running MLflow. Learn how to load and explore a dataset, as well as how to log metrics, parameters, and artifacts. Explore model training, evaluation, and hyperparameter tuning. Plus, go over model deployment and predictions.
Colaboratory, or “Colab” for short, is a product from Google Research. Colab allows anybody to write and execute arbitrary python code through the browser, and is especially well suited to machine learning, data analysis and education
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Deep Learning Project
MLflow
DVC (Data Version Control)
CI/CD Deployment
MLOps Tutorial
Machine Learning Lifecycle
Model Deployment
Data Versioning
Experiment Tracking
MLOps Practices
Deep Learning Workflow
Model Management
Continuous Integration
Continuous Deployment
Reproducibility in ML
AI Project Implementation
Automating ML Pipelines
Version Control in ML
Deep Learning Best Practices
MLOps Tools and Techniques
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MLFlow Tutorial | ML Ops Tutorial | Introduction To MLflow-An Open Source Platform for the Machine Learning Lifecycle | What Is MLOps | Introduction to MLOps | MLOps Tutorial | DevOps Tutorial For Beginners | Edureka | Introduction to MLOps
What is MLOps | MLOps Explained in just 3-minutes | Introduction to MLOps | Intellipaat
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