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Microsoft Fabric Copilot for Data Science and Data Engineering began its public preview journey in November 2023, during the Microsoft Ignite Conference. This tool, built to act as a collaborative assistant for data professionals, has already started to transform data exploration, preparation, and machine learning tasks, boosting productivity of data scientists and data engineers in Fabric. This week we are also happy to announce the worldwide availability of Copilot for Data Science and Data Engineering.
The value of Microsoft Fabric Copilot lies in its ability to adapt to the user’s workflow, providing assistance tailored to the context of your data and project. Here are examples of the value it provides:
If you are a data professional who uses Fabric notebooks, you can now leverage the power of Microsoft Fabric Copilot. Through natural language processing, Copilot allows you to generate code, receive guidance, and gain insights from data more quickly than ever before! This is achieved via two main features: the Copilot Chat panel and Copilot inside notebook cells (Chat Magics).
The Copilot Chat Panel serves as an interactive AI assistant within Microsoft Fabric's notebook environment. Here’s how it adds detailed value to various stages of a data professional’s workflow:
Interactive Q&A for Instant Insights: You can query your datasets interactively. For example, by asking, "What is the distribution of sales across different regions?" Copilot can generate the code for you to complete the relevant analysis, providing a visual distribution or summary statistics. This natural language interface simplifies the data exploration process and enables you to have a conversational experience with your data.
Context-Aware Code Suggestions: The chat panel understands the state of the user's notebook. If a data frame is loaded with sales data, you might ask, "Can you clean this data for analysis?" Copilot would generate a code snippet that applies filters, handle missing values, or remove duplicates based on the specific characteristics of the data in context.
Seamless Progression of Analysis: While working through a notebook, you might reach a point where the next steps are unclear. The user can leverage the chat panel to ask, "What should I do next after visualizing this data?" Copilot could suggest various statistical tests to validate hypotheses or recommend machine learning models that suit the data’s features.
We have introduced a developer friendly and non-intrusive way of interacting with Copilot directly in code-cells. Here's a closer look at how it works:
Natural Language answers as output: With magics, you can embed Copilot's AI capabilities directly into your code cells. For instance, %%chat magic could be used to initiate a conversation about the user's data within a cell. You may write, %%chat Analyze the pandas dataframe and recommend a few machine learning models, and Copilot would provide a natural language explanation and set of insights. Copilot may also provide sample code snippets that you may be able build upon in a new cell.
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Code Generation in-cell: The %%code magic command allows you to convert a natural language instruction into executable code right inside a cell. For instance, if you type %%code Can you provide me with linear regression model code on my dataframe , Copilot would understand the context, generate the code to create a linear regression model, and present the code snippet ready for execution.
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Microsoft Fabric Copilot for Data Science and Data Engineering has the potential to revolutionize the way data professionals complete their tasks, providing a layer of intelligence that augments skills and streamlines workflows. Its integration into Fabric notebooks is a testament to the future of collaborative, AI-enhanced data analysis and model building. This tool does not just automate tasks; it acts as a partner that brings data professionals closer to goals of efficient data management, insightful analysis, and innovative machine learning solutions. To learn more, please visit our documentation here.
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