PyLint
SonarQube
PyCharm
Coverity Scan
Checkmarx
ReSharper
Veracode
Micro Focus Fortify On Demand
Metaflow
Apache Airflow
Luigi
Azkaban
DepHell
Activeeon
Kubernetes
StackStorm
PyLint MetaflowMetaflow might be a bit more popular than PyLint. We know about 14 links to it since March 2021 and only 11 links to PyLint. 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 used Pylint to perform basic test on the code and for the security bit I used snyk SCM to check for vulnerabilities within my code and it's dependencies. - Source: dev.to / over 2 years ago
Pylint - https://pylint.pycqa.org/en/latest/ Black - https://black.readthedocs.io/en/stable/. Source: over 2 years ago
Your code isn't PEP-8 compliant. Use black or autopep8 on your code to auto-format your code, or at least use pylint to check for issues, before asking anyone else to read your code. Source: almost 3 years ago
Here's the pylint user manual if you're curious. Source: about 3 years ago
Use code linters. Code linters provide immediate feedback for your programs. The online W3C Markup Validation Service checks web documents for validity. ESlint helps you find and fix problems in JavaScript code. Pylint is a linter for Python code. Linters are available as plugins for IDEs like Visual Studio Code. Linters force you to learn by flagging errors and suggesting changes. - Source: dev.to / over 3 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
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.
Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
PyCharm - Python & Django IDE with intelligent code completion, on-the-fly error checking, quick-fixes, and much more...
Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.
Coverity Scan - Find and fix defects in your Java, C/C++ or C# open source project for free
Azkaban - Azkaban is a batch workflow job scheduler created at LinkedIn to run Hadoop jobs.
