Data

Tools

Indexes

Applications

Experiments

Open Source Science

https://github.com/awslabs/mlmax

Example templates for the delivery of custom ML solutions to production so you can get started quickly without having to make too many design choices.

https://github.com/awslabs/mlmax

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (12.0%) to scientific vocabulary

Keywords

inference machine-learning training
Last synced: 5 months ago · JSON representation

Repository

Example templates for the delivery of custom ML solutions to production so you can get started quickly without having to make too many design choices.

Basic Info
Statistics
  • Stars: 71
  • Watchers: 6
  • Forks: 18
  • Open Issues: 4
  • Releases: 2
Topics
inference machine-learning training
Created over 5 years ago · Last pushed over 1 year ago
Metadata Files
Readme Contributing License Code of conduct

README.md

ML Max

ML Max is a set of example templates to accelerate the delivery of custom ML solutions to production so you can get started quickly without having to make too many design choices.

Quick Start

  1. ML Training Pipeline: This is the process to set up standard training pipelines for machine learning models enabling both immediate experimentation, as well as tracking and retraining models over time.
  2. ML Inference Pipeline: Deploys a model to be used by the business in production. Currently this is coupled quite closely to the ML training pipeline as there is a lot of overlap.
  3. Development environment: This module manages the provisioning of resources and manages networking and security, providing the environment for data scientists and engineers to develop solutions.
  4. Data Management and ETL: This module determines how the machine learning operations interacts with the data stores, both to ingest data for processing, managing feature stores, and for processing and use of output data. A common pattern is to take an extract, or mirror, of the data into S3 on a project basis.
  5. CICD Pipeline: This module provides the guidance to setting up a continuous integration (CI) and continuous deployment (CD) pipeline, and automate the delivery of the ML pipelines (e.g., training and inference pipelines) to production using multiple AWS accounts (i.e., devops account, staging account, and production account.).

Help and Support

Owner

  • Name: Amazon Web Services - Labs
  • Login: awslabs
  • Kind: organization
  • Location: Seattle, WA

AWS Labs

GitHub Events

Total
  • Watch event: 4
Last Year
  • Watch event: 4

Issues and Pull Requests

Last synced: almost 2 years ago

All Time
  • Total issues: 45
  • Total pull requests: 55
  • Average time to close issues: 3 months
  • Average time to close pull requests: 14 days
  • Total issue authors: 10
  • Total pull request authors: 10
  • Average comments per issue: 1.56
  • Average comments per pull request: 1.02
  • Merged pull requests: 50
  • Bot issues: 0
  • Bot pull requests: 1
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • josiahdavis (16)
  • verdimrc (14)
  • yinsong1986 (5)
  • yapweiyih (3)
  • chenwuperth (2)
  • Ale0x78 (1)
  • shawnbrown (1)
  • RichardScottOZ (1)
  • edenduthie (1)
  • yihyap (1)
Pull Request Authors
  • josiahdavis (19)
  • verdimrc (10)
  • yihyap (6)
  • yinsong1986 (6)
  • anneehuu7 (4)
  • RichardScottOZ (3)
  • dependabot[bot] (3)
  • kianho (2)
  • goyder (2)
  • yapweiyih (2)
Top Labels
Issue Labels
enhancement (24) environment (15) stale (14) stale-issue (14) ml pipeline (8) bug (8) docs (7) good first issue (4) help wanted (4) testing (3) data-module (2) question (1)
Pull Request Labels
enhancement (4) stale-pr (4) dependencies (3) environment (2) bug (2) ml pipeline (2)

Dependencies

contrib/interpretation/requirements.txt pypi
  • sagemaker ==2.19.0
  • sagemaker-pyspark ==1.4.1
modules/data/requirements.txt pypi
  • boto3 >=1.9.213
  • fsspec *
  • matplotlib *
  • pandas *
  • pyyaml *
  • s3fs *
  • sagemaker >=2.22.0
  • scikit-learn ==0.20.0
  • stepfunctions >=2.0.0
modules/environment/util/docker/requirements-cdk.txt pypi
  • aws-cdk.aws-cloudwatch-actions *
  • aws-cdk.aws-events *
  • aws-cdk.aws-events-targets *
  • aws-cdk.aws-lambda *
  • aws-cdk.aws-sns *
  • aws-cdk.aws_apigateway *
  • aws-cdk.core *
  • aws_cdk.aws_cloudwatch *
  • aws_cdk.aws_ec2 *
  • aws_cdk.aws_elasticloadbalancingv2 *
  • aws_cdk.aws_iam *
  • aws_cdk.aws_logs *
  • aws_cdk.aws_secretsmanager *
  • aws_cdk.aws_ssm *
  • aws_cdk.custom_resources *
modules/environment/util/docker/requirements.txt pypi
  • awswrangler *
  • black *
  • boto3 *
  • flake8 *
  • isort *
  • matplotlib *
  • mypy *
  • mysql-connector-python *
  • numpy *
  • pandas *
  • plit *
  • pre-commit *
  • pytest *
  • sagemaker *
  • scikit-learn *
  • seaborn *
  • sphinx *
modules/environment/util/screening/requirements.txt pypi
  • smallmatter main
modules/pipeline/requirements.txt pypi
  • boto3 >=1.9.213
  • fsspec *
  • matplotlib *
  • pandas *
  • pytest *
  • pyyaml *
  • s3fs *
  • sagemaker >=2.22.0
  • scikit-learn ==0.20.0
  • stepfunctions >=2.0.0
requirements-dev.txt pypi
  • black ==19.10b0 development
  • flake8 * development
  • isort ==5.7.0 development
  • pre-commit * development
  • pydocstyle * development
  • recommonmark * development
  • sphinx * development
  • sphinx-markdown-tables * development
  • sphinx-rtd-theme * development
  • tox * development
requirements.txt pypi
  • boto3 >=1.9.213
  • datatest ==0.11.1
  • fsspec ==2021.8.1
  • loguru ==0.5.3
  • matplotlib ==3.4.3
  • numpy >=1.20.0
  • pandas ==1.3.2
  • pytest ==6.2.5
  • pytest-cov ==2.12.1
  • pyyaml ==5.4.1
  • s3fs ==2021.8.1
  • sagemaker >=2.22.0
  • scikit-learn ==0.20.0
  • stepfunctions >=2.0.0
.github/workflows/branch.yaml actions
  • actions/checkout v1 composite
  • actions/setup-python v1 composite
.github/workflows/project.yml actions
  • srggrs/assign-one-project-github-action 1.2.1 composite
.github/workflows/stale.yml actions
  • actions/stale v3 composite
modules/environment/util/docker/Dockerfile docker
  • amazonlinux 2 build
modules/monitoring/docker/Dockerfile docker
  • python 3.7-slim-buster build
pyproject.toml pypi
setup.py pypi