Imagine working for a supermarket, facing the challenge of determining the optimal bread stock levels each week to meet customer demands while minimizing food waste. Or perhaps you’re looking to understand your customers better, enabling you to create personalized offers that resonate with them.
Whenever your organization seeks to make informed decisions, data science steps in to extract valuable insights from your data. Data science is a powerful blend of mathematics, statistics, and computer engineering.
By applying data science techniques, you can analyze your data to uncover complex patterns that yield meaningful insights for your organization. You can even use data science to create artificial intelligence (AI) models that encapsulate these intricate patterns. A prevalent approach involves training machine learning models using libraries like scikit-learn in Python to achieve AI capabilities.
The Data Science Journey
Taking a data science project from inception to completion can be a daunting task. Microsoft Fabric offers a unified workspace to streamline the management of end-to-end data science projects.
Let’s embark on this journey to uncover the power of data science within Microsoft Fabric!
Understand the data science process
Visualizing Data: The First Step to Insights
A common approach to extracting insights from data is through visualization. When dealing with complex datasets, you might want to delve deeper and uncover intricate patterns hidden within the data.
The Role of Machine Learning
As a data scientist, you can train machine learning models to identify these patterns in your data. These patterns can then be utilized to generate new insights or make predictions. For instance, you could predict the expected number of products you anticipate selling in the coming week.
While training the model is crucial, it’s just one piece of the puzzle in a data science project. Before we explore the typical data science process, let’s briefly examine some common machine learning models.
Common Machine Learning Models
The essence of machine learning lies in training models that can discern patterns within vast datasets. These patterns can then be used to make predictions, leading to novel insights and informed actions.
Let’s categorize the four common types of machine learning models:
- Classification: Predicts a categorical value, such as whether a customer might churn.
- Regression: Predicts a numerical value, like the price of a product.
- Clustering: Groups similar data points into clusters or groups.
- Forecasting: Predicts future numerical values based on time-series data, such as expected sales for the next month.
Choosing the right machine learning model hinges on understanding the business problem at hand and the data available to you.
The Data Science Process
Training a machine learning model typically involves the following steps:
- Define the Problem: Collaborate with business users and analysts to determine what the model should predict and define the criteria for its success.
- Get the Data: Identify data sources and gain access by storing your data in a Lakehouse.
- Prepare the Data: Explore the data by reading it from the Lakehouse into a notebook. Clean and transform the data according to the model’s requirements.
- Train the Model: Select an algorithm and hyperparameter values through trial and error, meticulously tracking your experiments with MLflow.
- Generate Insights: Utilize model batch scoring to produce the desired predictions.
As a data scientist, a significant portion of your time is dedicated to data preparation and model training. The way you prepare the data and the algorithm you choose can significantly impact the success of your model.
Tools and Techniques
You can prepare and train models using open-source libraries available for your preferred language. For example, in Python, you can leverage Pandas and NumPy for data preparation and libraries like Scikit-Learn, PyTorch, or SynapseML for model training.
Experiment Tracking with MLflow
During experimentation, it’s vital to maintain an overview of all the models you’ve trained and understand how your choices influence their performance. By tracking your experiments with MLflow in Microsoft Fabric, you can efficiently manage and deploy your trained models.
In the following sections, we’ll explore how Microsoft Fabric facilitates each step of the data science process, empowering you to build and deploy impactful machine learning models.
Data: The Fuel for AI
Data forms the bedrock of data science, especially when the goal is to train machine learning models to achieve artificial intelligence. Typically, models improve their performance with an increase in the size of the training dataset. However, not only quantity but also the quality of the data plays a crucial role.
Leverage Fabric’s Data Ingestion and Processing
To ensure both quality and quantity of data, harnessing Microsoft Fabric’s robust data ingestion and processing engines is key. You have the freedom to choose between a low-code or code-first approach when establishing the essential pipelines for data ingestion, exploration, and transformation.
Ingest Data into Microsoft Fabric
The first step is to bring your data into Microsoft Fabric. You can ingest data from a variety of sources, both local (like CSV files on your machine) and cloud-based (like Azure Data Lake Storage Gen2).
Tip: Learn more about how to ingest and orchestrate data from various sources with Microsoft Fabric.
https://learn.microsoft.com/en-us/training/paths/ingest-data-with-microsoft-fabric/
Once you’ve connected to a data source, store the data in a Microsoft Fabric lakehouse. The lakehouse acts as a centralized repository for structured, semi-structured, and unstructured files, providing easy access whenever you need to explore or transform your data.
Explore and Transform Your Data
As a data scientist, you might be accustomed to working with notebooks. Microsoft Fabric offers a familiar notebook experience, powered by Spark compute.
Apache Spark is an open-source parallel processing framework that excels at large-scale data processing and analytics.
Notebooks are automatically linked to Spark compute. When you execute a cell in a notebook for the first time, a new Spark session is initiated, persisting as you run subsequent cells. To optimize costs, the Spark session will automatically terminate after a period of inactivity, or you can manually stop it.
Within a notebook, you have the freedom to choose your preferred language. For data science workloads, PySpark (Python) or SparkR (R) are likely choices.
Use your favorite library or the built-in visualization options within the notebook to explore your data. If needed, you can transform your data and save the processed results back to the lakehouse.
Prepare Your Data with the Data Wrangler
To expedite data exploration and transformation, Microsoft Fabric offers the user-friendly Data Wrangler.
Upon launching the Data Wrangler, you’ll gain a descriptive overview of your data. You can examine summary statistics to identify issues like missing values.
For data cleaning, choose from the available built-in operations. Selecting an operation automatically generates a preview of the result and the corresponding code. Once you’ve chosen all the necessary operations, export the transformations to code and execute them on your data.
In the following sections, we’ll dive deeper into training machine learning models and tracking experiments with MLflow in Microsoft Fabric. Let’s continue our data science journey!
Train and Score Models with Microsoft Fabric
Tracking Your Progress
Once you’ve ingested, explored, and preprocessed your data, it’s time to leverage it for training a model. Model training is an iterative process, and it’s essential to keep track of your work.
Microsoft Fabric seamlessly integrates with MLflow, enabling you to easily track and log your efforts. This allows you to revisit your work at any time and make informed decisions about the best approach for training the final model. By meticulously tracking your progress, you ensure that your results are easily reproducible.
Any work you want to monitor can be tracked as experiments within Microsoft Fabric.
Understanding Experiments
Whenever you train a model in a notebook that you wish to track, you create an experiment in Microsoft Fabric. An experiment can encompass multiple runs, each representing a task you executed in a notebook, such as training a specific machine learning model.
For example, consider training a machine learning model for sales forecasting. You might experiment with different training datasets while using the same algorithm. Each time you train a model with a different dataset, a new experiment run is created. This allows you to compare these runs to pinpoint the best-performing model.
Start Tracking Metrics
To facilitate comparison between experiment runs, you can track parameters, metrics, and artifacts for each run.
All tracked parameters, metrics, and artifacts are displayed in the experiment overview. You can view individual experiment runs in the Run details tab or compare them side-by-side in the Run list.
By leveraging MLflow’s tracking capabilities, you can effectively compare model training iterations and identify the configuration that yielded the most suitable model for your specific use case.
Understanding Models
After training a model, the next step is to utilize it for scoring. Scoring involves applying the model to new data to generate predictions or insights. When you train and track a model with MLflow, artifacts representing your model and its metadata are stored within the experiment run. You can save these artifacts in Microsoft Fabric as a model.
Saving your model artifacts as a registered model in Microsoft Fabric simplifies model management. Whenever you train a new model and save it under the same name, a new version is added to the model, ensuring a clear version history.
Generating Insights with Models
To harness a model for generating predictions, you can employ the PREDICT function in Microsoft Fabric. This function is designed for seamless integration with MLflow models and enables you to use the model for batch predictions.
Imagine receiving weekly sales data from multiple stores. You’ve trained a model on historical data to predict the upcoming week’s sales based on the sales of the past few weeks. By tracking this model with MLflow and saving it in Microsoft Fabric, you can readily use the PREDICT function to generate forecasts for the next week whenever new weekly sales data arrives. The forecasted sales data, stored as a table in a lakehouse, can then be visualized in a Power BI report for business users to consume.
In the following sections, we’ll explore how to deploy and manage your models within Microsoft Fabric. Stay tuned to learn how to operationalize your models and make them accessible for real-world applications!
Conclusion
Microsoft Fabric offers one central workspace to perform data science from beginning to end.
To perform data science, you need to first define the problem. Then, you can identify the data you need and ingest it into Microsoft Fabric. When you’ve ingested your data, you can explore and prepare your data, using notebooks or the Data Wrangler.
To train machine learning models as part of your data science project, you can track your work with experiments. To use a model to generate insights, you can use the built-in PREDICT function.
This blog post is based on information and concepts derived from the Microsoft Learn module titled “Get started with data science in Microsoft Fabric.” The original content can be found here:
https://learn.microsoft.com/en-us/training/modules/get-started-data-science-fabric/
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