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Fix list formatting in README #411

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merged 3 commits into from
Oct 2, 2023
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Fix list formatting in README #411

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52be597
Fix list formatting in README
Scorpil Oct 1, 2023
43d9bcf
Replace trailing spaces with <br/> to avoid linting error
Scorpil Oct 1, 2023
e7ced9d
Fixes typo in README
skrawcz Oct 2, 2023
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10 changes: 5 additions & 5 deletions README.md
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Expand Up @@ -27,14 +27,14 @@ Hamilton is a novel paradigm for specifying a flow of delayed execution in pytho
This paradigm makes modifications easy to build and track, ensures code is self-documenting, and makes it natural to unit test your data transformations. When connected together, these functions form a [Directed Acyclic Graph](https://en.wikipedia.org/wiki/Directed_acyclic_graph) (DAG), which the Hamilton framework can execute, optimize, and report on.

## Problems Hamilton Solves
✅ Model a dataflow -- If you can model your problem as a DAG in python, Hamilton is the cleanest way to build it.
✅ Unmaintainable spaghetti code -- Hamilton dataflows are unit testable, self-documenting, and provide lineage.
✅ Long iteration/experimentation cycles -- Hamilton provides a clear, quick, and methodical path to debugging/modifying/extending your code.
✅ Model a dataflow -- If you can model your problem as a DAG in python, Hamilton is the cleanest way to build it.<br/>
✅ Unmaintainable spaghetti code -- Hamilton dataflows are unit testable, self-documenting, and provide lineage.<br/>
✅ Long iteration/experimentation cycles -- Hamilton provides a clear, quick, and methodical path to debugging/modifying/extending your code.<br/>
✅ Reusing code across contexts -- Hamilton encourages code that is independent of infrastructure and can run regardless of execution setting.

## Problems Hamilton Does not Solve
❌ Provisioning infrastructure -- you want a macro-orchestration system (see airflow, kubeflow, sagemaker, etc...).
❌ Doing your ML for you -- we organize youir code, BYOL (bring your own libraries).
❌ Provisioning infrastructure -- you want a macro-orchestration system (see airflow, kubeflow, sagemaker, etc...).<br/>
❌ Doing your ML for you -- we organize youir code, BYOL (bring your own libraries).<br/>
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❌ Tracking execution + associated artifacts -- Hamilton is lightweight, but if this is important to you see the [DAGWorks product](www.dagworks.io).

See the table below for more specifics/how it compares to other common tooling.
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