mypy
PyLint
flake8
SonarQube
PyFlakes
Pyright
PEP8
PyChecker
Metaflow
Apache Airflow
Luigi
Azkaban
DepHell
Activeeon
Kubernetes
StackStorm
mypy MetaflowBased on our record, mypy should be more popular than Metaflow. It has been mentiond 50 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.
I've always admired many of Java's features, but let's not act like the reason for using Java for scripting is the pitfalls of Python. It's just because of an underlying preference for Java. 1. https://mypy-lang.org/. - Source: Hacker News / 5 months ago
I’m not here to tell people which languages they should love. But if you do find yourself writing production code in a dynamically typed language like Python, Ruby, or JavaScript, I would give serious consideration to opting into the type-checking tools that have become available in those ecosystems. In Python, consider requiring type hints and adding mypy checks to your CI to move your type safety bugs forward... - Source: dev.to / 12 months ago
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 / over 1 year 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 / over 1 year 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: almost 2 years ago
Metaflow is an open source framework developed at Netflix for building and managing ML, AI, and data science projects. This tool addresses the issue of deploying large data science applications in production by allowing developers to build workflows using their Python API, explore with notebooks, test, and quickly scale out to the cloud. ML experiments and workflows can also be tracked and stored on the platform. - Source: dev.to / 6 months ago
As a data scientist/ML practitioner, how would you feel if you can independently iterate on your data science projects without ever worrying about operational overheads like deployment or containerization? Let’s find out by walking you through a sample project that helps you do so! We’ll combine Python, AWS, Metaflow and BentoML into a template/scaffolding project with sample code to train, serve, and deploy ML... - Source: dev.to / 9 months 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: about 2 years 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 2 years 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 2 years 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.
SonarQube - SonarQube, a core component of the Sonar solution, is an open source, self-managed tool that systematically helps developers and organizations deliver Clean Code.
Azkaban - Azkaban is a batch workflow job scheduler created at LinkedIn to run Hadoop jobs.