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Deepnote: AI-powered platform for professional data analytics and scientific team collaboration

General Introduction

Deepnote is a collaborative notebook platform designed for data analytics and data science teams. Combining Python, SQL, and no-code analytics with the ability to connect to over 50 data sources, Deepnote utilizes GPT-4 to provide code generation, editing, interpretation, and autocompletion capabilities, and instantly transforms notebooks into dynamic data applications and dashboards. Whether you're exploring data, building machine learning models, or collaborating with teams, Deepnote provides powerful support and an easy-to-use experience.

Deepnote AI is part of the Deepnote platform, designed to make working with data more efficient by generating, editing, and interpreting code. Whether you're a Python expert or a programming novice, Deepnote AI helps you get the insights you need at every step of your data analysis. It utilizes generative AI technology (powered by OpenAI and Codeium (providing support), generating code based on your instructions and providing code completion recommendations.Deepnote AI is available for team and enterprise programs designed to make working with data more efficient and productive.

  • Generate Code: Enter natural language commands into your laptop and Deepnote AI will generate the appropriate code based on the commands.
  • Edit Code: Optimize the code using the suggested changes provided by AI.
  • Interpretation Code: Select the code segment to be explained and Deepnote AI will provide a detailed explanation.
  • Code autocompletion: When writing code, the AI will provide complementary suggestions based on the context.

 


Deepnote: AI-powered collaborative workspace for professional data analytics and science teams-1

 

Function List

  • Multi-language support: Support for Python, SQL, R, and many other programming languages.
  • AI Assist: Utilizes GPT-4 to provide code generation, interpretation, and auto-completion.
  • data visualization: Instantly visualize data frames as code-free configurable charts.
  • collaboration function: Team members can comment, review and version control.
  • data integration: Built-in integration with popular data warehouses, databases and data lakes.
  • Safety Compliance: Complies with HIPAA, SOC 2, GDPR and other security standards.
  • Customized Deployment: Provides secure, compliant, fully managed instances.
  • Interactive Dashboard: Transform rapid data exploration into impressive reports and applications.
  • API Deployment: Deploy notebooks as APIs to serve models directly.
  • GPU Support: Select hardware from CPUs to massive GPUs.

 

Using Help

Create and manage projects

  1. Create a project: After logging in, click on the "New Project" button and select the desired programming language (e.g. Python, SQL).
  2. Import data: Import data by dragging and dropping a CSV file or connecting to a data source (e.g. Snowflake, BigQuery).
  3. Write code: Write code in a notebook and utilize the code generation and auto-completion features provided by GPT-4 to increase efficiency.
  4. data visualization: Convert data frames into charts using built-in visualization tools.
  5. coordination: Invite team members to join the project, share notebooks, comment and review.
  6. version control: Use version control features to track and manage code changes.

Advanced Features

  1. AI Assist: Describe the task you want to accomplish, and Deepnote will automatically generate, analyze, and interpret the data without the need to write code.
  2. Interactive Dashboard: Create interactive dashboards, add input blocks and buttons, and schedule notebooks to run at regular intervals.
  3. API Deployment: Deploy the laptop as an API, direct service model, and select the required hardware (e.g., GPU).
  4. Security settings: Configure RBAC, SSO, and directory synchronization to ensure access control to data and code.

sample application

  1. Sales Report: Create visual reports of sales data to analyze sales trends.
  2. Client 360: Integrate customer data to provide a comprehensive view of the customer.
  3. Churn prediction model: Building and deploying customer churn prediction models.
  4. A/B test evaluation: Analyze A/B test results to optimize business decisions.
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