What Is An Artifact In Machine Learning?

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H2O Wiki

A

• AI Engineer
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• AI Risk Management
• AI Transformation
• AI Winter
• AI in Cloud Computing
• Activation Function
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• Artificial General Intelligence
• Automated Machine Learning

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• Generalized Linear Models
• Generative AI
• Generative Adversarial Network
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• Logistic Regression

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• Multiclass Classification
• Multilayer Perceptron

N

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• Neural Networking and Deep Learning
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O

• Operationalizing AI
• Optical Character Recognition
• Overfitting in Machine Learning | H2O.ai Wiki

P

• Prediction
• Python AutoML
• Pytorch

R

• Random Forest
• Recommendation System
• Recurrent Network
• Regression
• Regression Trees
• Regularization Parameter Lambda
• Reinforcement Learning
• Risk Governance Framework
• RuleFit

S

• Sentiment Analysis
• Shapley Values
• Speech-to-Text
• Stack Ensemble
• Structured vs Unstructured Data
• Supervised Machine Learning
• Support vector machine

T

• Target Leakage
• Target Variable
• Time Series Data
• Training Sets
• Transfer Learning

U

• Underfitting
• Unsupervised Machine Learning

V

• Validation Sets
• Vector

W

• Weights and Biases
• Word2Vec

X

• XGBoost

Z

• Zero-Shot Learning

Wiki Topics

-Select-

H2O Wiki

Algorithms

• Activation Function
• Confusion Matrix
• Convolutional Neural Networks
• Forward Propagation
• Generative Adversarial Network
• Gradient Descent
• Linear Regression
• Logistic Regression
• Machine Learning Algorithms
• Multilayer Perceptron
• Naive Bayes
• Neural Networking and Deep Learning
• RuleFit
• Stack Ensemble
• Word2Vec
• XGBoost

Artificial Intelligence

• AI Engineer
• AI Ethics
• AI Governance
• AI Models
• AI Risk Management
• AI Transformation
• AI Winter
• AI in Cloud Computing
• Artificial General Intelligence
• Document AI
• Explainable AI
• Generative AI
• NTrees
• Prediction
• Validation Sets

Data

• Big Data
• Citizen Data Scientist
• Data Profiling
• Data Science
• Shapley Values
• Structured vs Unstructured Data
• Time Series Data

Deep Learning

• BERT
• Deep Learning Cloud
• Deep Learning Use Cases
• Differentiable Programming
• Large Language Models
• Reinforcement Learning
• Zero-Shot Learning

General

• Feature Engineering
• Feature Selection
• Machine Learning Operations

Machine Learning

• Automated Machine Learning
• Grid Search
• Hyperparameter Optimization
• Machine Learning
• Machine Learning Lifecycle
• Machine Learning Models
• Multiclass Classification
• Overfitting in Machine Learning | H2O.ai Wiki
• Python AutoML
• Regularization Parameter Lambda
• Supervised Machine Learning
• Support vector machine
• Training Sets
• Unsupervised Machine Learning
• Vector

Modeling

• Back Propagation
• Classification
• Clustering
• Decision Tree
• Generalized Linear Models
• Model Fitting
• Neural Network
• Neural Network Architecture
• Operationalizing AI
• Random Forest
• Recurrent Network
• Regression
• Regression Trees
• Risk Governance Framework
• Underfitting
• cross-validation

Predictions

• Recommendation System
• Target Leakage
• Target Variable

Tools

• Containers
• Natural Language Processing
• NumPy
• Optical Character Recognition
• Pytorch
• Sentiment Analysis
• Speech-to-Text
• Weights and Biases

Training

• Artifacts
• Transfer Learning

Artifacts

What are Artifacts?

An artifact is a machine learning term that is used to describe the output created by the training process. Output could be a fully trained model, a model checkpoint, or a file created during the training process.

AI Artifacts describe all digital products that are used in an AI Tool. They can be the input, the output, or an intermediate result that is processed by tools. We mainly specify six types of artifacts(corresponding to the steps in the pipeline): Data, Knowledge, Model, Application, Algorithm, and Benchmark.

What is an artifact in software development?

An artifact is a byproduct of software development that helps to describe the architecture, design, and function of the software. Artifacts act like roadmaps that software developers can use to trace the entire software development process. These artifacts may be databases, data models, printed documents, or scripts.

Artifacts also describe all the digital assets used in an artificial intelligence tool. Examples include the input, the output, or an intermediate result that is processed by tools.

Artifacts and H2O AI Cloud

H2O Driverless AI provides several configuration options/environment variables that enable exporting of artifacts instead of downloading. Artifacts can be exported to a file system directory, an Amazon S3 bucket, a Bitbucket repository, or Azure Blob storage.

Read more

Artifacts Resources

Exporting Artifacts

Climbing the AI and ML Maturity Model Curve

H2O Driverless Demo

Unwrap Deep Neural Networks Using H2O Wave and Aletheia for Interpretability and Diagnostics