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2. Power Up: Crafting an Intelligent Energy Forecasting Application Using Azure Kubernetes Service

· 6 min read
#60Days Of IA

At the forefront of recent technological innovation are intelligent apps: apps that use machine learning (ML), artificial intelligence (AI), and data analytics. These apps support smarter, data-driven decisions, making them particularly useful in sectors like energy management, where efficiency and long-term planning are critical.

Our upcoming series will guide you through creating an intelligent app that leverages Azure technologies, including Azure Kubernetes Service (AKS), to build an application that forecasts energy usage and pricing.

Your app will harness AKS for hosting and AI to analyze historical energy consumption data. Then, you’ll integrate the Kubernetes AI Toolchain Operator (KAITO) with with XGBoost and LLaMA 2 to build an intelligent app that underscores the importance of green energy practices and demonstrates the versatility and efficacy of Azure services.

We invite you to join us on this three-part educational series, where you’ll learn the skills needed to construct your own intelligent apps. But, this series is more than a technical walkthrough: It’s an opportunity to engage with cutting-edge technologies and contribute to meaningful advancements in energy management.

Whether you’re an experienced developer or new to the AI and ML sphere, this series will give you a glimpse into the future of application development and the strategic impact of Azure technologies in driving forward-thinking solutions.

The Synergy of Azure Kubernetes Service and Intelligent Apps

Using AKS as the backbone of intelligent apps has numerous benefits — especially when deploying your AI-driven application. AKS provides a managed, cloud-based container orchestration service that simplifies deploying, managing, and scaling AI-backed applications, making it ideal for a project like the one you’ll create in this series.

One of the primary advantages of AKS is its ability to handle distributed applications with evolving demands. For AI-driven apps, the ability to scale resources based on computational demands is crucial. Because AKS allows for automatic scaling, intelligent apps have the necessary resources during peak analysis times without wasting resources during quieter periods. But this dynamic scalability isn’t just about handling loads efficiently: It’s also cost-effective, ensuring that you pay only for the resources you use.

Integrating the KAITO operator with AKS further enhances the deployment of AI models like LLaMA 2 by simplifying the complexities of managing AI workloads. KAITO, designed specifically for Kubernetes environments, acts as a bridge between the advanced AI models and the scalable, managed infrastructure provided by AKS. It offers custom resource definitions (CRDs) tailored for AI applications, facilitating the deployment, updating, and management of AI models within the Kubernetes ecosystem.

This seamless integration enables developers to focus more on the application logic and less on the underlying infrastructure, accelerating the development cycle and reducing the time to market for innovative AI solutions.

AKS and KAITO create a robust, flexible, and efficient environment for developing and deploying intelligent applications. This combination not only leverages the cloud’s power and scalability but also optimizes the deployment of AI models, making it easier for developers to bring complex, data-driven applications to life.


Register for Intelligent Apps on AKS: Episode 2, a live hands-on training with an expert on how to use AKS to run your own AI models with KAITO.

Laying the Groundwork with Azure Kubernetes Service

In the first installment of this series, you’ll roll up your sleeves and set up an AKS environment. This step is foundational to the rest of the series, laying the groundwork for deploying and managing your application — and accessing the full scalability and flexibility that AKS offers.

The article starts with a straightforward step-by-step guide on establishing the AKS environment, ensuring you have a solid base for the exciting journey ahead. This tutorial is succinct to maintain clarity and speedy development, offering links to additional resources for well-documented steps.

Next, you’ll meet KAITO, a tool that streamlines deploying AI applications in Kubernetes environments. The core of this article is configuring the KAITO operator to work seamlessly with the LLaMA 2 model, providing hands-on instructions, code samples, and screenshots to guide you through each step.

Adding Intelligence to the App

The second part of this series dives into the more practical aspects of building the Intelligent App. You’ll leverage an open-source energy dataset alongside powerful tools like XGBoost and a custom Python API to craft a forecasting model that predicts future energy demands with speed and precision.

Integrating these tools with AKS and Azure Container Registry highlights the high-impact relationship between robust data processing capabilities and scalable cloud infrastructure. Hands-on examples and streamlined code will guide you through setting up the environment, processing the dataset, and deploying the forecasting model.

This practical application reinforces the theoretical foundations laid in Part 1 and sets the stage for advanced analytics and AI-driven predictions. As you progress through the tutorial, the focus will remain on simplicity and efficiency, ensuring that even complex AI-related processes become accessible.


Complete the Intelligent Apps Skills Challenge to compete for the leaderboard and earn a Microsoft Learn Badge.

Building a Web Interface

As the concluding installment of our series, part 3 assembles all the pieces by introducing a user-friendly web interface. Here, users can input or upload their energy usage data and parameters, after which the Intelligent App will generate future predictions on usage and pricing.

This web front end serves as the direct point of interaction with your AKS-hosted application, seamlessly displaying the reports and predictions the AI model produces.

After deploying this interface in the AKS environment established in part 1, you’ll experience the complete cycle of developing an intelligent, data-driven application and appreciate how straightforward it is to engineer intelligent apps that can deliver tangible, user-centric outcomes.

Ready to Get Started?

Together, these three articles guide you through creating an innovative, AI-driven energy forecasting app. Setting up a scalable AKS environment with integrated cutting-edge AI models, processing open-source energy data for insightful predictions, and deploying a user-friendly web interface will equip you with the tools you need to build your own Intelligent Apps.

Stay tuned for each part of the series and get ready to dive into the world of Azure, AI, and application development with us. Join us in this exciting venture and harness the power of technology to make a difference. Register for the Intelligent Apps on AKS: Episode 2  to experience live hands-on training with an expert on how to use AKS to run your own AI models with KAITO.