We are producing data faster then ever. Every business wants to leverage data to create intelligent applications that can predict future trends and optimize day-to-day processes. Artificial intelligence (AI) has advanced significantly in recent years, and businesses are starting to implement AI solutions on a wider scale than ever before. As AI adoption increases, businesses are realizing the potential of AI to streamline operations and generate value in new and interesting ways. However, many companies struggle with implementing AI due to its complexity. There are many different components involved in an AI solution, each with its own unique needs and requirements. Some of the most challenging aspects of implementing AI involve making sure that the technology is always working optimally, monitoring performance levels throughout all stages of implementation, and identifying where improvements can be made. Fortunately, there are several best practices for the effective implementation of AI software that will help you succeed where others have struggled previously.
What is AI observability?
AI observability, also known as “real-time AI monitoring,” is the idea of creating a system that allows you to see what all aspects of your AI solution are doing in real-time. This includes everything from data ingestion to model training and prediction. This sort of visibility into every aspect of your AI solution will allow you to troubleshoot issues and identify areas for improvement. AI observability is immensely important for maximizing the value of AI and preventing issues that would otherwise be hidden from view. Any system that is critical for your business will have issues from time to time, and AI is no different. Having a clear understanding of what is happening at every single moment in your AI solution can help you identify issues quickly and take the necessary steps to resolve them. Furthermore, it will give you the opportunity to track the impact of your proposed solutions and make sure your AI is working as effectively as possible.
Why is AI Observability Important?
The importance of AI observability cannot be understated. An AI solution without proper tracking is like a car without a speedometer or a GPS — you have no way of knowing how well you are doing, or where you are headed. Having clear visibility into every aspect of your AI implementation will allow you to quickly identify and resolve issues that might otherwise have gone unnoticed. You will also be able to track the true impact of your AI solution and make necessary improvements to maximize the value of your investment. Additionally, proper AI observability allows you to keep your team informed about the progress of your AI implementation. This will help you avoid any bottlenecks or communication breakdowns that could otherwise cause delays in your project. With proper AI observability, you will have easy access to data that will allow you to stay informed of your AI solution’s progress and take action if any issues arise.
3 Pillars of AI Observability
- Data Ingestion – Data Ingestion refers to the implementation of the technologies and tools necessary to get data into your AI solution. This includes anything from scraping website data to get content for your model to uploading customer data to create customer profiles.
- Model Training – Model Training refers to the process of creating the model within your AI solution. This can be extremely challenging since you are creating a system that can learn and predict future trends based on past data. Many businesses struggle with creating a model that works effectively and can deliver the desired results.
- Prediction – Prediction is the point at which your AI solution is operational. Now that you have a model inside your AI solution, you will be able to get insights from the data that you have ingested. You can use this information to make intelligent business decisions, optimize day-to-day operations, and much more.
Important AI Observability Strategies
- Data Normalization – Data Normalization refers to the process of ensuring that all of your data is consistent and high quality. Having high-quality data is essential in creating an effective AI solution, and normalizing your data will help you make sure that the data you have is of the best possible quality. This will save you time, money, and effort as you create and implement your AI solution, allowing you to focus on making your AI solution as effective as possible.
- Automated Testing – Automated testing refers to the practice of using automated tools and simulations to test the performance of your AI solution. This can help you identify issues and make adjustments to your AI solution before it has even gone live. This can save you significant time and effort since you don’t have to wait until your AI solution is operational to identify and resolve issues.
Now that you understand the importance of AI observability, you can start to implement effective monitoring systems throughout your entire AI solution. From data ingestion to model training and prediction, every aspect of your AI solution should be visible and easily understood. Having clear and consistent visibility into the inner workings of your AI solution will allow you to quickly identify and resolve issues and make improvements where necessary. This will allow you to maximize the value of your AI solution, and make implementation as efficient as possible.