Data science is a complex field demanding numerous packages, installations and highly configured local machines to analyse, visualise data and draw conclusions. Data science necessitates time-consuming processes such as dataset preparation, selecting appropriate model, model training, testing, and model evaluation. Typically, data scientists would repeat these steps hundreds of times between features, algorithms, and model requirements before determining the best model for the business challenge at hand. It may take a long time for these cycles to be completed. For data science teams, managing dependencies and technology stacks across different use cases, local machines, and development environments is one of the most challenging tasks. In the course of their routine tasks, data scientists are required to maintain the relevant packages to keep the application up and running. Time-to-value for businesses can be further delayed pertaining to bottlenecks in the deployment, cooperation, and administration of infrastructure and environments.

Data scientists and analysts use python with Jupyter Notebook as their beloved go-to tool. With Jupyter notebook one can ​​access text editor, terminal, and directory viewer all under one view. Jupyter Notebook is an open-source web application that provides interactive computational capabilities. Working on the local machines comes with a lot of disadvantages. It is very common to run into issues such as:

  1. Software or package version differences between development and production environments resulting in delays;
  2. Operating system dependencies ;
  3. Data Logistics;
  4. Hardware issues;
  5. Loss of code and many more

On the other hand, cloud platforms get the data science teams up and running faster, providing greater long-term advantages such as unlimited compute and storage, easier collaboration, faster time to ML in production. The fastest and most cost-effective way to get started with data science and machine learning is to use a data science and machine learning platform such as ours.

We are thrilled to announce the release of Jupyter Lab in the cloud on our RunCode Remote Development Environment. Now, you no longer need to install anaconda packages or configure your local machines to get started. Our cloud based Jupyter lab will streamline your workflow, make communication and sharing easier, and help you get results faster. Collaboration, sharing, visualisations are all available at just the click of a button. It’s all in the cloud!

Unleash the full potential of our platform with our latest release and stay tuned for upcoming interesting updates.

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