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Introducing Build: A Playground For Data Scientists

Naman Maheshwari
Aug 23, 2022
3 min read

Experimentation in data science has typically been a cumbersome long-drawn-out process.

While building ML models for production, data scientists regularly face a myriad of challenges that only seem to be growing:

  1. Managing code and dependencies
  2. Replicating those environments
  3. Monitoring your system usage while training
  4. Tracking the total cost of experimentation
  5. And doing all of this while keeping your team in sync

What's more, accessing suitable machines that are powerful enough to handle your increasing machine learning workload is challenging. It may sometimes even require powerful GPUs like the NVIDIA V100 or P100, access to terabytes of storage, and more!

As your team scales, their workload scales across projects, datasets, and applications, and these problems only get exponentially more complex. There's also the issue of abstraction while onboarding users to a team - for instance, an intern or a temporary team member doesn't need access to sensitive data and secret keys. So how do you give them access to exactly what they need without giving them access to everything?

Introducing Build by Nimblebox.ai

Build by NimbleBox.ai is a plug-n-play playground for data scientists to build and train their models without worrying about the underlying architecture, making your experimentation fast and easy.

Having been used by over 15,000 developers across leading firms worldwide, Build by NimbleBox.ai is our most battle-tested product.


Here's a sneak peek at what Build has to offer:

Building machine learning models has never been easier

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Build by NimbleBox.ai allows you to install custom packages through Docker or by uploading a pip file, pre-installed with significant machine learning packages and libraries you regularly use.

Having sufficient storage space is paramount in a data-driven field like machine learning

Increase your drive size. Upgrade your machine's hardware by switching between CPU and GPU machines (choosing dedicated or preemptible machines), saving you valuable time and money.

Host any custom application on your instance

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Hosting applications like VS Code and Grafana? Expose ports publicly so you can run custom applications like Tensorboard, Streamlit, Grafana, MLFlow, GitLab, and MinIO, among others. You can also share these applications over the internet or within your team.

Create checkpoints to revert to in your model lifecycle

Developed by experienced machine learning researchers, Build by NimbleBox.ai allows you to backup your environment, so restoring and resetting your environment is a breeze. In addition, templating your code and environment together and the ability to create clones of your instance makes handoffs across teams a breeze.

That's not all.

Save on cloud costs while maintaining accountability in your team

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The auto-shutdown timer on your instances saves you from racking up vast amounts of cloud costs because instances run overnight. Scaling your team also means inculcating accountability. Build by NimbleBox.ai empowers accountability in your team by providing insights into your hardware and storage usage, viewing project runtime, and checking your team members' activity in the workspace.

NimbleBox.ai fits in your tech stack like a puzzle piece

Readily-available integrations with your team's tools and services, like Slack, GitHub, Kubernetes, Cloud buckets like AWS S3, Google Cloud Storage, and more.

Being cloud-agnostic is essential for your success

Coming this fall, you will be able to use Build by NimbleBox.ai and its features on your cloud account.

Build your own machine learning model on app.nimblebox.ai

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