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The Best Software for Collaborating on Machine Learning Projects

Collaborating on machine learning projects is challenging. It requires focus, attention to detail, and strong analytical skills. But it also requires tools.

When you’re working on a project solo, you have full flexibility when it comes to the work style. So what to do when you’re collaborating with your team? It’s possible to maintain flexibility but it’s impossible to do it without the right software.

There are many tools for collaboration on machine learning projects but not all will enhance work. The secret lies in features.

Here are 8 of the best software that will make the life and work of people working on ML projects easier. They can easily be incorporated into the process of agile project management with their adjustable features. Dig into our little inventory!


EXPLORE MORE TOOLS
Best Tools to Manage Machine Learning Projects


1. Neptune – ML metadata tracking

 

Team workspace

Neptune is a metadata store for MLOps. It’s an excellent metadata tracking platform for any data science team. The software easily integrates with your workflow and offers an extensive range of metadata tracking features.

It gives you a central place to log, store, display, organize, compare, and query all metadata generated during the machine learning lifecycle. Additionally, Neptune is very flexible, works with many other frameworks, and thanks to its stable user interface, it enables great scalability (to millions of runs). 

Neptune – summary:

  • Provides user management and organization with different organization, projects, and user roles
  • Fast and beautiful UI with a lot of capabilities to organize runs in groups, save custom dashboard views and share them with the team
  • You can use a hosted app to avoid all the hassle of maintaining yet another tool (or have it deployed on your on-prem infrastructure)
  • Your team can track experiments that are executed in scripts (Python, R, other), notebooks (local, Google Colab, AWS SageMaker) and do that on any infrastructure (cloud, laptop, cluster)
  • Enjoy extensive experiment tracking and visualization capabilities (resource consumption, scrolling through lists of images)

 2. Comet – tracking ML projects

Comet app

Comet is a meta machine learning platform for tracking, comparing, explaining, and optimizing experiments and models.

Just like many other tools – for example, Neptune (neptune-client specifically) or WandB – Comet proposes open-source Python library to allow data scientists to integrate their code with Comet and start tracking work in the application.

As it’s offered both cloud-hosted and self-hosted, users can have team projects and save a backup of experimentation history.

Comet is converging towards more automated approaches to ML, by predictive early stopping (not available with the free version of the software) and Neural architecture search (in the future).

Comet.ml – summary:

  • Deals with user management
  • Sharing work in a team: multiple features for sharing in a team
  • Integrations with other tools: should be developed by the user manually
  • SaaS/Local instance available: Yes/Yes
  • Bonus: Display parallel plots to check patterns in the relationships between parameters and metrics

👉 See the comparison between Comet.ml and Neptune.

3. GitHub – software development platform

GitHub project management

GitHub is the most popular platform built for developers. It’s used by millions of teams around the globe as it allows for easy and painless collaboration. With GitHub, you can host and review code, manage projects, and build software.

It’s a great platform for teams collaborating on machine learning projects who want to simplify workflow and share ideas conveniently. GitHub lets teams manage ideas, coordinate work, and stay aligned with the entire team to seamlessly collaborate on machine learning projects.

Here are some of the main features you and your team will find helpful:

  • Build, test, deploy, and run CI/CD the way you want in the same place you manage code
  • Use Actions to automatically publish new package versions to GitHub Packages. Install packages and images hosted on GitHub Packages or your preferred registry of record in your CI/CD workflows
  • The software lets you secure your work with vulnerability alerts so you can remediate risks and learn how CVEs affect you
  • The build-in review tools make it easy and convenient to review code – a team can propose changes, compare versions, and give feedback
  • GitHub easily integrates with other tools for smooth work, or you can create your own tools with GitHubGraphQL API

GitHub is a platform where all the documentation is easily accessible, and all the features make it a unified system for flexibly developing software.


READ ALSO
How to Set Up Continuous Integration for Machine Learning with Github Actions and Neptune: Step by Step Guide


4. Jira – project management for agile teams

Jira - tracking ML projects

Jira is a fantastic software for agile teams as it allows for fully-encompassed project management. It’s an issue and project tracking tool so teams can plan, track, and release their product or software as a perfectly developed ‘organism’.

Jira allows for flexible workflow automation. You can freely manage a project by assigning certain tasks to people, bugs to programmers, create milestones, or plan to carry certain tasks within a  specific timeframe.

Products and apps built on top of the Jira platform help teams plan, assign, track, report, and manage work. Four products are built on the Jira platform: Jira Software, Jira Service Desk, Jira Ops, and Jira Core. Each product comes with built-in templates for different use cases and integrates seamlessly, so teams across organizations can work better together.

Jira is a great solution for collaboration for programmers, analysts, and software architects and all the team of people developing software. It helps to simplify, organize, and structure workflow.

5. Slack – online chat compatible with other apps

Slack dashboard

Slack is one of the most popular apps for communication. It can also enhance the work of people working on machine learning projects.

Slack is the e-mail turned into messages with people, information, and tools in one place. 

By combining people, applications, and data, it effectively replaces e-mail and long, messy threads. Additionally, it lets people see a bigger picture so everyone can see what’s happening within a company and stay in the loop.

Slack makes it simple to follow conversations or find important information in an easily searchable archive. So when a team works on an ML project, they always know what’s happening and can streamline processes.

Summary of Slack main functionalities:

  • Workspaces – you can create multiple workspaces for different projects or teams
  • Channels – private or public, shared with clients let you quickly communicate with other people
  • Direct and group messages
  • Company’s key info – threads, mentions and reactions, saved items, and people are at your hand so you can quickly navigate in the app
  • Apps – an inventory of numerous tools lets you integrate Slack with other popular tools
  • Files – a space where you and your team and the entire company can store files and easily access them with a search option or upload a new file
    save drafts
  • History and search allow for quick and easy search for the things you are looking for
  • Video calls with screen sharing
  • Create workflows that automate routine actions and communication
  • Messages and files can be encrypted and you can contact the sales team for more security options

Slack is a powerful tool for communication and collaboration and its smallish features allow teams to work in the agile style.

6. Notion.so – all-in-one workspace

Notion is a collaboration tool that lets you write, plan, collaborate, and organize teamwork.

It comprises of four modules, each with different functionalities:

  1. Notes, Docs – text editor which serves as a space for files, notes of different formats; you can add images, bookmarks, videos, code, and many more
  2. Knowledge Base – in this module, teams can store knowledge about projects, tools, best practices, and other aspects that are necessary for developing machine learning projects
  3. Tasks, Projects – tasks and projects can be organized in a Kanban board, calendar, and list views
  4. Databases – this module can effectively replace spreadsheets and keep records of important data and unique workflows in a convenient way

Additionally, every team member can use Notion for personal use to keep a record of work-related activities and information, for example, weekly agenda, goal, task list, or personal notes.

Other smallish features include #markdown. /Slash commands, drag-and-drop feature, comments and discussions, and integrations with 50+ popular apps such as Google Docs, Github Gist, CodePen, and more

All modules create a coherent system that serves as a unified hub for work management and project planning. It’s a lightweight tool suitable for agile teams.

7. Google Meet – real-time meetings

Google Hangouts Meet

Google Meet is one of the most popular e-meeting platforms. It’s especially helpful for remote teams collaborating on machine learning projects. Google Meet lets teams meet for sprints, conferences, individual chats, or any other form that is preferred. 

Using your browser, you can share your video, desktop, and presentations with teammates and customers.

Google Meet integrates with G Suite’s Google Calendar and Gmail so you can see the complete list of participants and scheduled meetings.

You can easily and quickly schedule a meeting and set it to team members way ahead so they can join it later with an auto-generated link.

If in your company you use G Suite, you can access meetings with dialing in a phone number of a given meeting. And it can be used solely for audio meetings or video conferencing as it lets users turn off the camera or mute microphone so people can focus on a person who is speaking.

8. Google Docs – collaboration in real-time across organization

Google Docs is one of the best solutions for organizing files and collaborating on them in real-time, no matter how many users are working on the file.

Even I am writing this article using Google Docs, so other people taking part in its publication can easily collaborate on it.

Google Docs has useful features that facilitate the work of teams:

  • Create as many files as necessary, adjust them as you like
  • Use templates from the template gallery
  • Add-ons allow users to customize documents for more flexible and effective work
  • Smart editing and styling tools help easily format text and paragraphs – you can choose from hundreds of fonts, add links, images, and drawings
    Team members can access, create, and edit documents from a phone, tablet, or computer
  • Everyone can work together in the same document at the same time
    All changes are automatically saved – there is no need to save files locally on the computer or other device
  • You can use revision history to see old versions of the same document, sorted by date and who made the change.

Google Docs is a must-have tool for collaborating on machine learning projects.

To wrap it up

When choosing software for collaboration on ML projects, make sure it corresponds to your team’s preferences, style of work, and can be easily integrated with the apps you currently use.

It’s the best way to ensure coherency, seamless workflow, and security of projects.

Happy collaborating!


READ NEXT

How to Improve the Collaboration in the ML/DS Team?

As a run tracking hub, Neptune provides several features for enabling knowledge sharing and collaboration among members of your data science team.

They can:

  • have every piece of every run or notebook of every teammate in one place,
  • see and compare all the teams’ experiments and models,
  • see what everyone on the team is working on,
  • share a view on a project or any of its parts, by simply copying and pasting the URL to it,
  • collaborate with other team members on the results.

How does Neptune help different people on the ML/DS team?

Data Scientists benefit from:

  • Seeing all model training metadata in one place
  • Comparing model training runs
  • Seeing model training runs live
  • Being able to reproduce model training runs

Machine Learning Engineers find the most valuable that they can:

  • Have a central registry for the models, runs, and notebooks,
  • Check how the model was built,
  • Find and fetch information they need for putting model in production.
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