There's currently 3781008 ways of doing ML

It takes time for a Canonical Stack to develop in any new field. Before it develops and unleashes a tidal wave of new innovation, it’s often super hard for anyone to see where it’s all going.

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Machine Learning Lifecycle

  • Highly Developed
  • Moderately Developed
  • Still Developing
  • Lightly Developed
  • 0. Resource Management, DL Enablement
  • 1. Data Gathering, Transformation
  • 2. Experimenting, Training, Tuning, Testing
  • 3. Productionization, Deployment, Inference
  • 4. Monitoring, Auditing, Management, Retraining
0.1.2.3.4.
Acceldata
Airbytes
Algorithmia
Arize AI
Arthur
Auger.AI
Bodywork
ClearML
cnvrg.io
Coiled
Comet
DAGsHub
Determined AI
Fiddler
Gretel.ai
Hopsworks
Iguazio
Inferrd
Iterative
ixpantia
Kubeflow
LatticeFlow
Maiot
MindsDB
MLFlow
ModelOp
Molecula
Neptune
Neu.ro Inc.
New Relic
OctoML
Pachyderm
PrimeHub
Sama
Seldon
Snitch AI
Spell
Superb AI
Superwise.ai
Tecton
Terminus DB
Toloka
TruEra
UbiOps
Valohai
WhyLabs
YData
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End-to-end ML platforms typically take 15–36 months to catch up on feature parity, but the products will launch much sooner than that to create thought leadership and get product feedback.

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