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Today no less than 60% of companies are either exploring the possibilities of adopting artificial intelligence or trying to realize its potential to transform the way they do business. The problem is that a significant portion of them (one-third) struggle to produce substantial change with AI.

The lifecycle of an AI solution usually consists of problem definition, data collection, model building, model fine-tuning, and applying the solution to solve a specific problem. Various experts build the solution to solve business problems. Still, a problem solved by a data scientist does not automatically translate into a constant stream of actual value for the business. Once deployed to production, the AI solution cannot be left as-is. Like any other system, it requires continuous maintenance. However, any AI solution’s maintenance differs significantly from the maintenance of other systems (e.g., microservice-based applications). The performance of any AI solution can be affected by many factors, and if the maintenance work is not done, the solution will cause problems instead of solving them.

In this article, I will look at some factors that can affect the performance of your AI solutions, explain what Managed AI is, and provide a few scenarios for AI/ML maintenance with Managed AI. Finally, I will clarify why Managed AI (as a service) should be your long-term strategy for scaling and keeping your AI solutions in shape.

AI Solutions Are More Complicated Than They Seem

The lifecycle of an AI solution usually starts with defining a business problem. Then, you gather data, build and train a model, fine-tune the model, and integrate the model as a part of your solution.  

The gist is that any AI solution should solve business problems. You should engage with various subject matter experts to ensure it does. Only after that can you expect the solution to bring your business some actual value. But the story does not end there.

A typical application operates as a static system that changes once updates are introduced. By contrast, AI solutions are all about models and, more specifically, about the data that these models consume. Because data is constantly changing, AI solutions should be designed, implemented, and maintained as flexible systems.

Here are some risk factors that can affect AI solutions: 

  • Concept drift — target variables that the model is trying to predict change over time in unforeseen ways

  • Data drift — variations in production data from the test and validation of the model before production deployment

  • Model drift — the degradation of model performance due to changes in data and relationships between input and output variables

  • Bias — the algorithm produces systemically prejudiced results due to erroneous assumptions in the machine learning process.

  • Unexpected inputs — inputs that significantly differ from the training data and cause erratic and flawed predictions

  • New data source(s) — introducing new data, especially “dirty” data, can significantly affect the model’s performance.

  • Model performance degradation — because the data changes, the model that initially performed well might degrade over time.

A few of the areas essential for the productionalization of AI solutions:

  • Inference performance metrics — inference behaves differently in production; not only the model but the entire pipeline needs to be monitored.

  • Dependencies updates — as the input data for features changes, so will the model; sometimes, those changes are undesirable.

  • Input data monitoring — input data quality is critical for any ML system; an immediate feedback loop is needed here.

  • Model monitoring — performance shifts must be tracked to determine how well the model performs; we must evaluate performance on real-world data.

  • Inference from an Ops standpoint — the model is a system that needs to be monitored from the lower level (CPU, memory, disk consumption, etc.)

  • Engineering complexity of an ML-specific high load — massive data files for ML are challenging and require extra engineering effort and ML-specific application servers.

If you do not address any of these issues in time, your AI solution can start to cause more damage than it generates in actual business value. Incorrect data, models that deliver inaccurate results, and wrong predictions and insights can all be detrimental to your business. Here is when Managed AI can help!

What Is Managed AI?

Managed AI is an end-to-end service for managing AI workloads and a new approach to handling complicated, resource-intensive AI/ML tasks by third-party managed service providers.

Managed AI services are critical for companies looking to gain a competitive edge in the AI/ML field. By partnering with third-party vendors, they access a pool of highly specialized professionals (e.g., Data Scientists, Data and Machine Learning Engineers, DevOps, MLOps, etc.). Those professionals can manage their AI solutions without the need to hire all of them, structure new organizations, or even understand the specifics of the field. This shortcut to successful results in reduced costs, higher quality pipelines, and greater efficiencies in operations.

Today, Managed AI is not a fringe service but a core business component, helping companies to develop and deploy AI solutions faster and at scale and to deliver on their ROI.

Scenarios for AI/ML Maintenance With Managed AI 

Let’s review some examples from my practice and see how the Managed AI approach has helped our customers.

Model Monitoring

One of our clients wanted to implement an AI-powered fraud detection solution as an integral part of its document management workflow.

The first release was successful. We observed a significant improvement in the quality of the workflow’s outputs. However, as time elapsed, we noticed that the inference data distribution had changed. The solution worked fine, but something was wrong.

If we had not had proper controls to monitor the model end-to-end, we would never have discovered something was amiss. Little did we know that the human factor was to blame. It turned out that the employees whose job was to process the documents found a workaround, a way to fool the fraud detection solution. 

Imagine that the customer had opted to use the solution without continuous model monitoring. It would have worked perfectly while missing actual fraud cases and reporting non-existing ones. In this case, AI would have generated more harm than value.

Data Quality Assurance

Another client was running a business analytics business. Their BI platform was hosted in the cloud and, like any analytics-focused tool, depending on clean, accurate, valid, relevant, timely, and consistent data.

When the client’s cloud provider rolled out several updates of the services they used, the existing working solution “readjusted” and started to cause bugs. The client no longer had a clear understanding of its data.

The good news is that the client never stopped maintaining its solution. Provectus owned the entire data quality assurance and data governance lifecycle, which helped us to identify and fix the buggy component quickly, to stop the flow of dirty data downstream.

Complex High-Load Inference

As demonstrated by another client, high-quality data and highly accurate models, even when everything is adequately monitored, tracked, and maintained, are not enough to guarantee success.

When the client developed the client’s AI-powered solution for detecting fraud in bank transactions, they did not consider scalability from an infrastructure standpoint. The historical data was perfect, and the initial model was accurate, but the infrastructure was not ready to handle massive loads of transactions in production. In other words, what looked perfect in notebooks was a mess when deployed to production. Data scientists were too eager to field-test their theories but did not evaluate and fine-tune the model over time, failing to make the solution more robust and resilient.

AI solutions that require complex, high-load inference can be a challenge. Understanding the specifics of the ML inference process and how it differs from classic applications or microservices is essential. Sometimes, you even need completely different hardware to serve your models properly at scale. 

In the Managed AI approach, any AI work combines development, enhancement, and maintenance that entails data science, machine learning engineering, and DevOps (MLOps). In the case of our client, we looked at its solution holistically from the start of our engagement, which helped us to deliver a system capable of handling complex, high-load inferences on top of a robust ML infrastructure.

Summing It Up

Trying to overhaul an entire organization with AI without factoring in Managed AI is sometimes just too complicated to be practical. Even if you start your AI journey with a single AI use case, plan for its maintenance with external service providers or an internal team of trained professionals.

To work for your business, AI solutions, including their data, models, and ML infrastructure, have to be constantly kept in shape. They need to be scaled and enhanced to produce meaningful change. Maintenance is necessary if you want to enable your business with AI and not disable it.

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