What tools are essential for managing deadlines in machine learning projects?

For managing deadlines in machine learning projects these project management software tools are Really helpful: Jira: Flexible, customizable, integrates with ML tools. Asana: Versatile, collaborative, task tracking. Trello: Visual, card-based, simple task management. GitLab: Integrated version control, CI/CD pipelines. Redmine: Open-source, customizable, issue tracking. Microsoft Project: Powerful, Gantt charts, resource management. Confluence: Collaboration, integrates with Jira, documentation. Airtable: Flexible, combines spreadsheet with a database, integrates with various apps.

1) Agile Project Management: Trello, Asana, Jira: Break down tasks, track progress visually, manage dependencies. 2) Version Control: Git, Mercurial: Track code changes, revert errors, collaborate seamlessly. 3) Data Management: MLflow, Kubeflow: Organize datasets, track experiments, monitor model performance. 4) Continuous Integration/Continuous Delivery (CI/CD): Automate testing, deployment, and rollbacks for faster iterations. 5) Alerting and Monitoring: Prometheus, Grafana: Stay informed about model performance, infrastructure issues, and potential delays. 6) Project Communication: Slack, Discord: Facilitate team communication, share updates, discuss progress and roadblocks.

Choose the one you are comfortable with. Doesn't matter if it Azure DevOps, GitHub, Jira, Kanban board or whatever. Not having to learn a new tool will reduce project noise so that you can focus on your tasks.

Stay focused, chose the framwork you are familiar with and stay close to your team members, you can't work in isolartion, make sure you communicate and share your thoughts.

It depends a bit on your role and task. I’ve seen ML engineers using tools other software engineers would use. Since it’s good practice to Version control your code anyways you can start with using tools like GitHub. More professional teams would use tools like Asana and Jira. If these are too complex you can also use tools like linear. In the end there is no ?best tool“ as it depends on the stage of the company and the constellation as well as size of the team. If you’re working alone use whatever keeps you organised. If you are the only ML person use the tools the rest of the software engineers use.

In machine learning projects, managing deadlines is streamlined with a robust version control system. Tools like Git, GitHub, Bitbucket, and GitLab are indispensable for tracking and managing changes in code, data, and models. They facilitate seamless collaboration, enabling teams to explore various features and parameters while maintaining the ability to revert to stable versions when necessary. This not only ensures progress tracking and efficient teamwork but also provides a safety net for experimental endeavors, crucial for meeting project timelines effectively in the dynamic field of machine learning.

In Machine Learning Projects Deadline Managing is a very crucial task as the work is time-intensive and consuming at the same time. and Version control is required to be followed as a best practice for which we can use Git which is open-source and we can also look for some better enterprise-grade products on top of it like gitkraken, Jenkins, etc.

Use Git. Enable Large File Storage if necessary. Be careful not to store your training sets in Git as raw datasets. Compress it as much as possible. Don't store voluminous data sources like audio or video files in Git. Set up a data lake of sorts. Don't forget to keep your data safe from accidental (or deliberate) deletions!

For version control of code use got. It’s industry standard. If you want to version data there are alternatives such as DVC as git is not optimised for binary files but code. But keep in mind that data versioning tools are not as mature as code versioning. I’m still waiting for the git for data. Be mindful when working with kupptet notebooks. Changes can get messy. I tend to avoid updating them often or only checking in empty notebooks (not after running with the output) to make it easier to digest the changes in a pull request. If you have larger files such as models you can upload them as attachments to tags on GitHub. Or to dedicated model hosting sites such as hugging face.

Data Pipeline frameworks are crucial to be considered very seriously as CI/CD pipelines are the only gate barriers if not set up properly. If you are building on a Cloud Choose Wisely according to your hands-on experience and knowledge about the same be it Azure, AWS CodePipeline, Google's CI/CD, etc. The Same Can also be done with GitHub Actions

Choose the framework you know. GitHub Actions, Azure DevOps, AWS CodePipeline, Google CI/CD, roll you own, it doesn't really matter. Stick to the one you know unless there is a compelling reason to switch.

Use pytest for testing with python. Use mypy for static typing and make sure you type the code. It’s not a must for researchers and in academia nobody cares but in Industry it’s a must. Better learn it as early as possible! Use Google style guide for python to keep the code structured and consistent. Use black for formatting. Use isort for sorting your imports. It might be a little work to set off all up but it will make you much more productive in the long term.

Choose the testing, debugging and formatting tools according to your chosen language of implementation In my View pytest, lambdatest, and a few more alike tools are good in case of python. use proper formatting tools like google style and also utilize IDE extensions like stylelint, etc.

I am afraid that I will start to sound like a broken record. Choose the one(s) you know. There are testing, formatting, linting and stylistic tools for Python, Julia, Elixir, Rust or whatever your chosen implementation language of choice is. Don't overdo it. Aim for Just Enough Tooling and add what you need when necessary.

In my view Documentation and Representation are the parts of a project in which we simplify all the complex wor done in a well organised manner which is easily understandable by others. Confluence Serves as one of the best tool for this specific work: 1. Confluence: Purpose: Confluence, developed by Atlassian, is a collaboration tool that integrates with Jira. It is suitable for creating and organizing project documentation. 2. Features: Wiki-style documentation. Integration with Jira for seamless project tracking. Customizable templates for documentation.

The tool is not so important here. It doesn't matter if is SharePoint or Confluence or Miro or Coda or Loop or whatever. What is important that the team understand what the documentation structure is. Knowing where to capture or locate stuff is more important than the tool you do it with.

Documentation and presentation tools serve as the storytellers of your machine learning journey, transforming complex algorithms into narratives that resonate. From Google Docs to Jupyter Notebook, these tools offer a canvas to articulate your project's process, results, and insights. In my experience, employing such tools enhances clarity, fosters collaboration, and facilitates effective communication with diverse audiences. They are not just utilities; they are the brushes that paint a vivid picture of your machine learning endeavors, making your work accessible and impactful.

Models used in high-cost error areas, e.g. credit scoring, requires documents: 1. Development report -issues by model developer, describing development cycle, factor calculations et. Approve by product owner and sign by CRO. 2. Explanatory note - by the product owner, describing the processes in which model will be applied and decisions that will be made based on it. Approve by model developer and sign CRO. 3. Prod testing memo - results of model's test, comparison with benchmark development sample, prepared by tester. Sign by CRO, product owner and model developer. 4. Implementation order - the exact date of model deployment, as well as the old one expires. Approve by all relevant departments and sign by CRO.

If you are using python, you can you pdoc to create automated documentation. Moreover, use type hints from typing module to represent inputs and output data types for the functions used. You can create streamlit applications with the prototypes and use plotly for creating interactive charts to visualize and compare results.