We are producing data faster than 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.
Creating reliable and dependable AI solutions requires a comprehensive understanding of the entire development process. To achieve this, companies must have complete visibility into every stage from gathering labeled training datasets to fine-tuning hyperparameters and testing real-world applications. This approach ensures that the end product aligns with people’s expectations, while mitigating potential risks associated with deploying unverifiable systems. Trustworthy AI is at the heart of any development philosophy, and we should strive to deliver solutions that meet the highest standards of reliability and accuracy.
When it comes to implementing an AI solution, there are several components to consider, each with their own specific needs and requirements. However, one of the most challenging aspects is ensuring that the technology is always performing at its best, which involves monitoring performance levels throughout all stages of implementation and identifying areas where improvements can be made. But don’t worry – there are effective best practices you can follow to ensure successful AI software implementation. In this post, I will delve into the concept of observability and how it can help you achieve your goals to deliver reliable AI solution.
What is AI observability?
To achieve optimal performance and desired results, it is essential to comprehend the exact meaning of “observability” in the context of AI applications. The observability feature plays a critical role in ensuring the efficacy and triumphant implementation of the technology. Essentially, observability in AI pertains to the capacity to observe, debug, and comprehend the behaviour of ML models in production settings. It enables precise monitoring of model inputs over an extended period, enabling prompt identification of any drifting parameters or anomalies without interfering with the user experience or unnecessarily compromising model performance metrics such as precision and recall scores.
Why is AI Observability Important?
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. Now that we know what observability means and what it is about. Let us explore the impact it can create if implemented in AI systems.
Below are the two major properties that need to be monitored in the AI system.
- Data Drift: One of the primary challenges is the data distribution shift, which occurs when the data used to train an ML model differs from the data it encounters in production. As a result, the model’s performance may degrade, leading to incorrect or biased predictions. Data drift typically results from seasonal fluctuations or evolving customer preferences. For example, there is a noticeable difference in demand for Face masks in covid and post covid era. Hence, models created using past data are meaningless. The model becomes muddled as a result of the changing input data since the distribution of the variables changes. With observability, data scientists can detect and mitigate data distribution shifts by monitoring and comparing the input data to the training data.
- Concept Drift: Concept drift occurs when the underlying relationship between features and the target variable changes over time. This can lead to inaccurate predictions and compromised performance. For example, the season, which affects temperature data but is not explicitly expressed in temperature data, can be one factor in meteorological data. Another example would be the evolution of consumer spending trends, which could be affected by economic strength even though it isn’t explicitly stated in the report. Concept Drift can be further divided into four categories :
- Sudden Drift
- Gradual Drift
- Incremental Drift
- Recurring Concepts
By monitoring the output of Machine Learning models in production over time, you can detect concept drift and enable data scientists to retrain the model or adjust its parameters. With the help of ML Observability, teams are able to assess and analyze the impact of underlying causes on performance degradation by comparing shifts with baselines from training, validation, or previous production periods.
Implementing AI Observability
Implementing AI observability requires a combination of technical and organizational practices. On the technical side, data scientists and developers need to design ML models with observability in mind by:
- Incorporating logging
- Capturing metrics
- Monitoring capabilities.
Organizations also need to establish alerting mechanisms to notify them when issues arise and create dashboards to visualize the performance of ML models.
A few of the most prominent AI observability tools/platforms are Neptune.ai, Grafana + Prometheus, Whylabs, and Arize AI.
Free and Open source Whylogs allows users to generate data profiles, which are statistical summaries of their data. These statistical summaries include crucial information about the data, such as its distribution, the number of missing values, the kind of data, etc. These profiles allow users to understand the data being put into their models, the predictions they are making, and the model’s performance. They can quickly discover problems like data drift, poor data quality, and more.
Along with the use of monitoring tools, practitioners recommend using “human in the loop” observability. By automating these procedures, machine learning experts may concentrate on putting more models into use because they won’t have to spend as much time diagnosing and repairing problems with their already-deployed ML systems.
Conclusion
AI observability is a critical practice for ensuring the reliability and effectiveness of ML models in production environments. By providing real-time visibility into ML operations, observability enables data scientists and developers to detect and diagnose issues quickly, assess the impact of changes, and optimize ML models’ performance. Implementing observability requires a combination of technical and organisational practices, and it is crucial for promoting transparency and accountability in the use of AI technologies. Observability is the key difference between a team that flies blind after deploying a model and a team that can iterate and improve their models quickly.
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