In a world where AI is becoming increasingly ubiquitous, ensuring the reliability, transparency, and ethical use of these systems is paramount. This is where AI observability plays a crucial role.
Artificial Intelligence Observability is a holistic and comprehensive approach to obtain information about the behavior, data and performance of the model during its life cycle.
AI Observability enables precise analysis of the origins of the predictions made by ML models and helps to build performing and responsible models. AI Observability introduces a proactive approach to identify problems in ML pipelines and take the necessary actions to avoid further losses. It helps promote people’s trust in ML systems.
Although ML monitoring and observability seem similar, observability denotes a broader picture with testing, validation, explainability and preparation for unpredictable failure modes.
The Need for AI Observability
As the use of AI continues to expand across industries, there is a growing demand for AI Observability. This need arises from the inherent complexity and opacity of AI systems. Unlike traditional software, where developers can easily track the flow of data and code, AI models involve intricate algorithms and vast amounts of data that can be challenging to comprehend. Without proper observability, it becomes challenging to pinpoint the root cause of issues and understand why a particular decision was made by an AI system.
Another crucial factor driving the need for AI observability is the increasing concern over ethical implications of AI. With AI systems making decisions that impact people’s lives, it is imperative to ensure that these decisions are fair, unbiased, and aligned with ethical principles. AI observability provides the necessary visibility into these decisions, enabling stakeholders to identify and address any biases or unintended consequences.
A third element in play is the variables to which the algorithm is exposed once it is brought into production. An algorithm runs on data coming from the real world, which is by definition an ever changing environment: changes in input data structures, anomalies in flows can have significant impacts on results. Again, adopting practices to quickly detect these kinds of changes is part of the concept of AI Observability.
How AI Observability works
To better understand what is meant by AI observability and why it is crucial to ensure reliable and transparent performance of AI systems. Here is an overview of the AI Observability process and its key components:
- Data Collection: AI Observability begins with the collection of data from AI systems in production. This data includes inputs, outputs, performance metrics and other relevant indicators.
- Real-Time Monitoring: Collected data is continuously monitored in real time to identify anomalies, errors or unexpected behavior in AI systems and responses promptly.
- Data Analysis: This analysis provides insight into the performance of AI models and factors that may influence their behavior.
- Data visualization: The results of the data analysis are visualized through interactive dashboards and reports that provide a clear overview of the performance of AI systems and any issues detected.
- Diagnostics and Troubleshooting: Using the information obtained from data analysis, operators can diagnose problems and take steps to solve them. This may include optimizing AI models, correcting input data errors or updating system configurations.
- Continuous Optimisation: Based on data analysis and corrective actions taken, the AI Observability process feeds a continuous optimisation cycle. This cycle enables continuous improvement in the performance and reliability of AI systems over time.
- Reporting and Documentation: The results of monitoring, analysis and corrective actions are documented through detailed reports. These reports can be used for compliance, governance and continuous improvement purposes.
- Feedback and Iteration: User and operator feedback is used to guide the iteration and improvement of AI systems. This feedback and iteration cycle helps keep AI systems aligned with user needs and expectations over time.
- Security and Compliance: AI Observability also includes security and compliance audits to ensure that AI systems meet applicable security and privacy standards and comply with relevant regulations and regulatory requirements.
Why is AI observability essential?
Artificial intelligence Observability is essential for organizations wishing to leverage artificial intelligence and machine learning technologies, ensuring that they can efficiently manage, monitor and gain insights from their generative and predictive artificial intelligence models, thereby facilitating better decision-making and customer experience.
Observability becomes increasingly important as generative AI is introduced into the business ecosystem, due to the risks associated with the possibility of returning incorrect answers, ‘hallucinations’.
AI Observability helps gain insights into ML and LLM models and entire pipelines. It provides visibility into how the model works and helps to make decisions about the models.
AI observability is crucial for companies in all industries for several main reasons:
- Transparency and trust: AI systems often operate like black boxes, making it difficult to understand how they make decisions. Observability provides transparency into the inner workings of AI models, allowing companies to understand the reasoning behind the decisions made by AI. This transparency fosters trust among stakeholders, including customers, regulators and internal teams.
- Performance monitoring: Observability allows companies to monitor the performance of AI systems in real time. By tracking key performance metrics and detecting anomalies, companies can identify and resolve issues in a timely manner, ensuring the reliability and effectiveness of AI-driven processes. This capability is essential for maintaining high-quality service levels and achieving business objectives.
- Root cause analysis: When AI systems encounter errors or underperformance, observability allows companies to efficiently conduct root cause analysis. By tracking the inputs, outputs and internal states of AI models, companies can identify factors contributing to problems and take timely corrective action. This proactive approach minimizes downtime, mitigates risks and increases the resilience of AI-based operations.
- Compliance and governance: In regulated industries such as Finance, Healthcare and Transport, observability is key to ensuring compliance with legal and ethical standards. By monitoring Artificial Intelligence systems and documenting their behavior, companies can demonstrate regulatory compliance, reduce legal risks and uphold ethical standards related to privacy, fairness and accountability.
- Optimisation and iteration: Observability facilitates continuous optimisation and iteration of AI models. By analyzing performance data and user feedback, companies can identify opportunities to improve AI algorithms, refine training datasets and improve overall system performance. This iterative process enables companies to remain competitive in dynamic markets and adapt effectively to evolving user needs.
- Cost efficiency: Observability helps companies optimize the use of AI infrastructure resources. By monitoring resource consumption, identifying inefficiencies and optimizing configurations, companies can reduce the operational costs associated with AI deployment. This cost efficiency is particularly important for companies that operate at scale, where small improvements in resource utilization can lead to significant cost savings.
Observability tools
AI Observability is strategically crucial for companies in all industries due to its role in improving transparency, performance monitoring, root cause analysis, compliance, optimisation and cost efficiency.
By investing in observability capabilities, companies can indeed maximize the value of AI investments, mitigate risks and maintain a competitive advantage in today’s data-driven economy.
Observability tools such as Radicalbit permit to proactively identify and solve problems, optimize model performance, and ensure the reliability of AI-based applications.
Radicalbit’s observability and monitoring capabilities empower you to proactively identify and resolve issues, optimize model performance, and ensure the reliability of your AI-driven decisions across diverse domains.
It also turns out to be central to detecting and mitigating data and concept drift, i.e., “deviations” in data distribution, theoretical assumptions, or context that can undermine a model’s predictive capabilities.
How Bitrock can help
Since data is only as important as our ability to access and derive value from it, the processes of collecting, handling, and using data have become critical to organizational success. Moreover, it is now clear that AI is a crucial part of the equation and challenges such as model operationalization, model maintenance are the key to success.
Bitrock provides turnkey technologies and architectures for extracting value from very large quantities of data in a cost-effective manner by allowing high-speed data collection, discovery, and analysis.
We help companies with the definition of their AI strategy, assessing the maturity of their data infrastructure, identifying business opportunities related to data and AI. And we can help them execute their data strategy.
More specifically, we support our Clients in exploiting all the potential of their data, by helping them:
- Exchange data within the same business: Data should not be kept in silos
- Connect data: Make individual pieces of data readily available, so that they can communicate with one another
- Make data self-sufficient: Using automation techniques, data can generate value on its own
How can we do all this? Thanks to our in-house team of highly specialized Data Engineers, Data Scientists and MLOps Engineers, our expertise with top-tier Clients in relevant industries (such as Fintech, Banking, and Retail), and our deep knowledge of all major enabling technologies.
