Introduction
Azure AI-ML studio simplifies the creation of time-series forecasting models with minimal coding. In this blog I am going to show you using Azure Machine Learning Studio to predict ebike rental demand for future dates.
It can provide forecast demand based on the historical data that we have provided and it chooses the best model and suitable Algorithms to run the ML job and predicts the most effective results but this prediction might not be 100% accurate unless we provide all keen achieve data with all dependencies for model to fully analyze along with best compute engines, algorithms etc.
In recent years, AI-driven demand forecasting is widely adopted across various industries, Such as
- Retail: Retailers like Walmart and Amazon use AI to predict product demand, optimize inventory levels, and manage supply chains efficiently.
- Manufacturing: Companies such as Siemens leverage AI for forecasting demand, reducing production downtime, and minimizing waste.
- Healthcare: Hospitals and clinics use AI to forecast patient admissions, ensuring adequate staffing and resource allocation.
- Energy: Utilities like National Grid use AI for demand forecasting to balance supply and demand, optimizing energy distribution.
- Transportation: Logistics companies like UPS use AI to predict demand for shipping services, optimizing routes and delivery times.
AI demand forecasting helps businesses improve operational efficiency, reduce costs, and enhance customer satisfaction by anticipating needs more accurately.
Another application could be if HR departments predict future hiring needs for an IT company. This not only boosts the company's business but also reduces the workload on HR teams by allowing them to plan more effectively.
Prerequisites
- Azure AI-ML workspace
- Dataset (of your own)
Overview
Implementing demand forecasting using Azure Machine Learning Studio involves a series of structured steps, each critical for building an accurate and deployable predictive model. The process begins with signing in to Azure ML Studio, where you access and configure your workspace to set the stage for your project. This workspace acts as the central hub for all your machine learning activities, ensuring that you have the necessary resources and settings tailored to your specific needs.
The next step involves creating and loading your dataset. This typically means uploading your data as a tabular Azure ML dataset. For instance, if you are working with ebike rental data, In your case you can upload your historical data of your business or employee hiring or trades etc. Ensuring that your data is correctly formatted and imported is crucial because the quality of your input data directly affects the model’s performance.
Once your dataset is ready, you proceed to configure the job. This entails setting up your experiment, where you define your objectives and parameters. You select the target column, which in this case could be ‘cnt’ representing the count of ebike rentals. Additionally, you configure the compute cluster, which determines the computational resources allocated for running your experiment. This step is pivotal as it sets the groundwork for your machine learning task, ensuring that the experiment runs efficiently.
After configuring the job, you move on to selecting the forecast settings. Here, you define the machine learning task explicitly as time series forecasting. You also set the forecast horizon, which specifies how far into the future you want to make predictions. Adjusting additional settings might include selecting specific algorithms or tuning hyperparameters to enhance the model's accuracy.
With all settings in place, you run the experiment. Azure ML Studio facilitates monitoring the progress of your experiment through notifications and real-time updates. This allows you to track the model's training process and make necessary adjustments on the fly. Running the experiment is an iterative process where you might need to refine parameters based on initial outcomes to achieve optimal results.
Upon completion of the experiment, you explore the models generated. Azure ML Studio provides a suite of performance metrics to evaluate these models, such as accuracy, precision, and mean absolute error. By comparing these metrics, you can identify the best-performing model that meets your forecasting needs. This step is crucial as it involves analyzing and selecting the model that will provide the most reliable predictions.
Finally, you deploy the selected model as a web service for real-time predictions. Deployment involves configuring the deployment settings and initiating the process to make the model accessible via API. This step transforms your model from a theoretical construct into a practical tool that can be integrated into business applications, allowing for real-time demand forecasting.
Well, here a simple recording of mine will walk you through the discussion that we had till now. It will showcase you how it predicts the future demand for ebike rentals.
In summary, deploying the final model—enables you to leverage advanced machine learning techniques for accurate demand forecasting. This workflow not only enhances predictive accuracy but also streamlines the implementation of AI solutions in real-world business scenarios.
Hope you enjoyed this blog. Happy Learning!!
