mypy
PyLint
flake8
PyFlakes
SonarQube
PyCharm
PyChecker
PEP8
Metaflow
Apache Airflow
Luigi
Azkaban
Apache Oozie
Activeeon
DepHell
StackStorm
mypy MetaflowBased on our record, mypy should be more popular than Metaflow. It has been mentiond 48 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Mypy is "an optional static type checker for Python that aims to combine the benefits of dynamic (or "duck") typing and static typing". As Python is dynamically typed, Mypy adds an extra layer of safety by checking types at compile time (based on type annotations conforming to PEP 484), catching potential errors before runtime. - Source: dev.to / 5 months ago
Mypy stands as an essential static type-checking tool. Its primary function is to verify the correctness of types in your codebase. However, manually annotating types in legacy code can be laborious and time-consuming. - Source: dev.to / 6 months ago
Lua is a great language for embedding, but one thing I wish it had was some form of optional type annotations that could be checked by a linter. Something like mypy for Lua would be super-useful. Source: 11 months ago
Python is a dynamically typed language (unlike C or java which are statically typed) meaning that there's no enforcement on the type. This var ; type syntax is called Type Hints, and they are just that, merely hints. So they serve as a reminder to developers of what types of variables a function should receive and output, but they implement no real restrictions. So if you try to pass a string to collatz for... Source: 12 months ago
Mypy (https://mypy-lang.org/), the static type checker for python, so quite an important project in the python ecosystem. Source: about 1 year ago
I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home. Source: 12 months ago
1) I've been looking into [Metaflow](https://metaflow.org/), which connects nicely to AWS, does a lot of heavy lifting for you, including scheduling. Source: about 1 year ago
Even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf. Source: about 1 year ago
They had to figure out video compression that worked at the volume that they wanted to deliver. They had to build and maintain their own CDN to be able to have a always available and consistent viewing experience. Don’t even get me started on the resiliency tools like hystrix that they were kind enough to open source. I mean, they have their own fucking data science framework and they’re looking into using neural... Source: over 1 year ago
Github Actions, Metaflow and AWS SageMaker are awesome technologies by themselves however they are seldom used together in the same sentence, even less so in the same Machine Learning project. - Source: dev.to / over 1 year ago
PyLint - Pylint is a Python source code analyzer which looks for programming errors.
Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
flake8 - A wrapper around Python tools to check the style and quality of Python code.
Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.
PyFlakes - A simple program which checks Python source files for errors.
Azkaban - Azkaban is a batch workflow job scheduler created at LinkedIn to run Hadoop jobs.
