Building a Reproducible Model Workflow
- Offered byUDACITY
Building a Reproducible Model Workflow at UDACITY Overview
Building a Reproducible Model Workflow
at UDACITY
Learn to be more productive through ML projects that require reproducible workflow best practices.
Duration | 1 month |
Mode of learning | Online |
Credential | Certificate |
Building a Reproducible Model Workflow at UDACITY Highlights
Building a Reproducible Model Workflow
at UDACITY
- Flexible learning program
- Technical mentor support
- Practical tips and industry best practices
- Unlimited submissions and feedback loops
- Additional suggested resources to improve
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Building a Reproducible Model Workflow at UDACITY Course details
Building a Reproducible Model Workflow
at UDACITY
Skills you will learn
What are the course deliverables?
- Learn the fundamentals of MLOps and how to create a clean, organized, reproducible, end-to-end machine learning pipeline from scratch using MLflow. Clean and validate data using pytest and tracking experiments, code, and results using GitHub and Weights & Biases. Plus, learn to select the best-performing model for production and deploy a model using MLflow.
More about this course
- Take Udacity's Building a Reproducible Model Workflow course to get an introduction on machine learning operations and learn how to create a clean, end-to-end machine learning pipeline MLflow.
Building a Reproducible Model Workflow at UDACITY Curriculum
Building a Reproducible Model Workflow
at UDACITY
Machine Learning Pipeline
Data Exploration & Preparation
Data Validation
Training, Validation & Experiment Tracking
Release & Deploy
Course Project: Build an ML Pipeline for Short-Term Rental Prices in NYC
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Student Forum
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Data ScienceMachine LearningMachine Learning and Pattern RecognitionBuilding a Reproducible Model Workflow