_template.yml

# Name of the system
name: My AutoML System
# Short description
description: An awesome AutoML system.
# The URL of the main website or documentation
website: https://example.com/...
# Whether the system is open-source
open_source: yes
# List of businesses or academic institutions that directly support the
# development of the system, and/or hold intellectual property over it
institutions:
  - University of Klingon
  - Star Fleet Ltd.
# If it's open-source, link of a public source code repository, otherwise `null`
repository: https://github.com/...
# If it's open-source, a license key, otherwise `null`
license: MIT

# List of links to relevant papers, preferably DOIs or other universal handlers,
# but can also be links to arxiv.org or other repositories
# sorted by most relevant papers, not date
references:
  - https://doi.org/...
  - https://arxiv.org/abs/...

# Whether the system has a command line interface
cli: yes
# Whether the system has a graphic user interface
gui: no
# Whether the system can used from an HTTP RESTful API
http: no
# Whether the system can be linked as a code library
library: yes
# Programming languages in which the system can be used, i.e.,
# it is either natively coded in that language or there are maintained bindings
# (as opposed to using language X's standard way to call code from language Y)
programming_languages:
  - python
  - r

# Domains in which the system can be deployed
domains:
  - images
  - nlp
  - tabular
  - time_series

# Whether the system supports multiple domains for a single workflow, e.g.,
# by allowing multiple inputs of different types simultaneously
multi_domain: yes

# List of high-level techniques that are available in the systems, broadly classified
# according to model families
techniques:
  - linear_models
  - trees
  - bayesian
  - kernel_machines
  - graphical_models
  - mlp
  - cnn
  - rnn
  - pretrained
  - ensembles
  - ad_hoc # indicates non-ML algorithms, e.g., tokenizers...

# Whether the system supports model distillation
distillation: yes

# List of ML libraries that support the system, i.e., where the techniques
# are actually implemented, if any
ml_libraries:
  - scikit-learn
  - keras
  - pytorch
  - nltk
  - spacy
  - transformers

# List of high-level tasks the system can perform automatically
tasks:
  - classification
  - structured_prediction
  - structured_generation
  - unstructured_generation
  - regression
  - clustering
  - imputation
  - segmentation
  - feature_preprocessing
  - feature_selection
  - data_augmentation
  - dimensionality_reduction
  # domain-agonostic data preprocessing such as normalization and scaling
  - data_preprocessing
  # refers to domain-specific preprocessing, e.g., stemming, as opposed to
  - domain_preprocessing

# Whether the system supports multiple tasks in a single workflow, e.g.,
# by allowing multiple output heads from the same neural network
multi_task: no

# List of high-level search strategies that are available in the system
search_strategies:
  - random
  - evolutionary
  - gradient_descent
  - hill_climbing
  - bayesian
  - grid
  - hyperband
  - reinforcement_learning
  - constructive
  - monte_carlo

# If the system includes meta-learning, list of broadly classified techniques used
meta_learning:
  - portfolio
  - warm_start

# High-level characteristics of the hyperparameter search space
search_space:
  # If there are hyperparameters that only make sense conditioned to others
  hierarchical: yes
  # If the hyperparameter space has an associated probabilistic model
  probabilistic: yes
  # If the hyperameter space can be used for gradient descent
  differentiable: no
  # If the global structure of the hyperparameter space is inferred automatically from,
  # e.g., type annotations or model's documentation, as opposed to explicitely
  # defined by the developers or the user
  automatic: yes

  # Types of hyperparameters that can be optimized
  hyperparameters:
    - continuous
    - discrete
    - categorical
    - conditional

  # Types of pipelines that can be discovered by the AutoML process
  pipelines:
    # A single estimator (or model in general)
    - single
    # A fixed pipeline with several, but predefined, steps
    - fixed
    # A variable-length pipeline where each step feeds on the immediately previous' output
    - linear
    # An arbitrarily graph-shaped pipeline where each step can feed on any of the previous'
    - graph

  # Whether the seach space contains potentially invalid pipelines that are only discovered
  # when evaluated, e.g., allowing a dense-only estimator to precede a sparse transformer
  robust: no

# Whether the system is designed to be extensible, in the sense that a user can add
# a single new type of model, or search algorithm, etc., in an easy manner,
# not needing to modify any part of the system
extensible: yes
# Whether the models obtained from the AutoML process can be freely inspected by the user
# up to the level of individual parameters (e.g., neural network weights)
accessible: yes
# Whether the models obtained can be exported out of the AutoML system, either on a
# standard format, or, at least, in a format native of the underlying ML library,
# such that they can be deployed on another platform without depending on the AutoML system itself.
portable: no

# Computational resources that, if available, can be leveraged by the system
computational_resources:
  gpu: yes
  tpu: no
  cluster: no