AI Observability
 minutes read

Initiation To AI Observability - Leading You Towards Reliable, Robust Models

Our take on why you should incorporate AI observability in ML Dev practices and how to get started on that track.

Initiation To AI Observability - Leading You Towards Reliable, Robust Models
In this post:

It was the opening day of the inter-school soccer tournament. The coach stood at the bylines, watching his team culminate months of practice on the field. They had trained hard, yet he was aware that their performance in the tournament would be a different story altogether. They would face players possessing different styles, different fields, and perhaps unfriendly audiences. 

Our coach is a smart man, though. Not only did he train his team to overcome training issues, but also read the dynamics. The players were taught to detect any problems that could throw off their game and resolve them quickly.

Wait, this is not a sports blog! But a little analogy doesn’t hurt. Now let us leave that imaginary soccer tournament and return to the world of ML development.

The soccer team is the model you have trained and tested and will be heading to the playground called the target environment. You or your team are the coaches. Collecting statistics and metrics throughout the lifecycle will give you the advantage of reading the dynamics and improving on the model. This incorporation of AI observability would surely win the cup called a well-performing, reliable model.

Did We Mean Model Monitoring?

The short answer is: No.

Here is the long answer:

AI Observability is an approach that gauges a model’s performance across the entire ML development lifecycle, from conception to post-deployment. It helps teams to pre-empt possible issues and implement a timely resolution. Monitoring the model’s performance is a way to collect logs and metrics to support AI observability. It is, therefore, one of the means to achieve AI observability. 

Since AI observability is much more than simple model monitoring, it has been fondly christened “monitoring-on-steroids”. These are some points that can further differentiate between the two processes.

A comparison table that compares model monitoring in the left column to AI observability in the right column
A comparison table that compares model monitoring in the left column to AI observability in the right column

The Pillars of AI Observability 

The practice of AI observability is supported by four major activities, namely,

  • performance monitoring
  • watching the drift
  • monitoring data quality
  • utilizing explainability

The said methods provide many functions individually, but their use focused on AI observability is the gamechanger.

The four pillars of AI observability. Source: The author
The four pillars of AI observability. Source: The author

Performance monitoring

Analogous to the soccer team, the performance of a model needs continual monitoring. Some actions such as deployment to the target environment may severely affect the performance. While there are a large variety of AI models and use-cases, some universal metrics like accuracy, precision, recall, F1 score, and RMSE are indicators of performance. You can read further about the key metrics in model monitoring and how to measure them in one of our blogs. 

The metrics put numbers to the behavior of the data and the model over the duration of time. Thereby, statistical analysis can be performed to gain deeper insight into current and anticipated behavior. Deciding on specific metrics will let you track the performance variations at different scales of time, such as daily, weekly or monthly intervals. 

It is also true that the ground truth might vary for different use-cases or be initially absent. Therefore the definition of expected model behavior is conditional. The collection of logs and metrics can signal changes in the long-term performance and help cement the ground truth too. Additionally, changes in measured metrics can reveal a shift in prediction performance even in the absence of ground truth. Such instances are signs of drifts.

Catching the drift

The concept of drift is the boogeyman necessary to keep the elements in check. It measures the statistical differences between distributions of model inputs and outputs and its generalization capabilities, among others. Based on the underlying elements, drift could be concerned with concept, data, predictions, or upstream the data pipeline.

  • Catching concept drift lets you handle the dynamic relationship between the input and output. Therefore, decisions can be made to re-train or enhance the model, tune hyperparameters or reconsider data preparation strategies.
  • While data drift is unavoidable, it makes for a sensible strategy to monitor changes in distributions and handle them gracefully.
  • Prediction drift guards against degradation in prediction performance and ensures a good customer experience.
  • Watching out for upstream drift guards against unexpected issues after the model is deployed.

Monitoring data quality

A model is as good as the data it trains and tests on. It is the simple mantra of ‘garbage in, garbage out.’ An ML monitoring system should ensure data quality through practices of pre-processing, integrity checks, and checks for shifts in cardinality.

Utilizing explainability

The realization that post hoc analysis could further enhance a model led to the need for AI explainability. AI explainability is a very popular domain of algorithms that seek to explain decisions taken by a model. It can be executed at different granularities, seek feature attributions, and help teams make better choices.

I’m Sold on AI Observability! But how to Start with it?

Now that we have waxed eloquently about AI observability, it is our duty to help you get started with it. This is the part where we at Censius excel at bringing up the ML development experience of your team.

AI observability in action for a classification model
AI observability in action for a classification model. Source: Censius AI

The Censius AI observability platform offers a three-pronged approach to more reliable ML projects.

  • Firstly, automation of model monitoring minimizes the efforts and can timely identify potential issues.
  • Secondly, the generated reports assist in root-cause analysis of detected anomalies.
  • Thirdly, the Censius AI observability platform makes your model future-ready by incorporating best MLOps practices and tools.

The features offered by Censius are manifold, yet it is effortless to get started. Don’t worry! We do not want you to take coding classes or go through boring tutorials. Your model can be up and running on the Censius AI observability platform in three easy peasy steps:

  1. Integrate the SDK: This is where you register the model and configure logging options through an easy-to-use interface.
  2. Wire in the monitors: Browse through the available monitoring configurations that cater to an entire ML pipeline. Make your choice and wait for the magic to happen.
  3. Start to observe: Ka-ching! You can view all the information needed to track the project and anomalies, if any. No, we do not have little minions working in the backend. It is all thanks to the power of AI and MLOps.

AI Observability for All

During the course of this blog, you must have identified requirements relevant to your role in the team. The good news is that AI observability serves all of the stakeholders in the ML development domain.

The operations involved in ML development and deployment involve teams from different operations. The feedback given by AI observability fosters collaboration between the stakeholders who oversee model performance and consumer experience. Additionally, an automated AI observability platform can lower the potential of human error and time costs.

  • If you are an ML engineer, then collected logs and metrics provide insight into the development strategy.
  • The product team concerned with the performance in the target environment consumes the performance metrics and provides input to the ML scientist.
  • The ML or a data scientist, in turn, would use the information provided by the product team to decide on enhancement for the modeling and pipelining strategies. These functions are well supported by performance monitoring and explainability aspects of AI  observability.
  • The traditional DevOps practices followed the AI observability of conventional software. Similarly, an MLOps engineer and their team can ensure the smooth functioning of pipelines that can scale according to data volume and cardinality.
  • An organization that invests in an end-to-end AI observability platform will never be caught off-guard. The automated ability to detect potential issues and timely insights can help decision-makers build a product aimed at happy consumers.

Wrapping up

AI Observability is much more than model monitoring. It is supported by performance monitoring, drift detection, data quality provisions, and the use of explainability to ensure a reliable model. Since AI observability practices are critical to the quality of the ML lifecycle, automation of the process can significantly lower the efforts and errors. In this blog, we introduced the Censius AI observability platform for your end-to-end needs. Lastly, we saw that different stakeholders stand to gain from this golden practice.

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