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True magic happens when you deploy an AI/ML model into the real world, where it can make predictions, optimize processes and drive insightful decisions. It’s where theory meets reality and where algorithms start delivering tangible value. Whether you’re predicting customer behaviors, optimizing supply chains or diagnosing medical conditions, the deployment phase is where these innovations step out of the lab and into the heart of your operations. 

The models operate in the dynamic realm of real-world data, where shifts and changes are constant. This is where the importance of monitoring comes into play. Just as pilots use instruments to navigate through changing weather, monitoring your AI and ML models ensures they stay on course even as conditions evolve.

In this final article of our AI blog series, we’ll cover the last two phases of the Columbus AI Innovation Lab - Deploy and Support. If you missed the other blogs, you can find them here:

The Columbus AI Innovation Lab: Make artificial intelligence work for your business

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Unleashing the power of machine learning: From deployment to dynamic monitoring

To get the most value out of machine learning models, it is important to seamlessly deploy them into production so a business can start using them to make practical decisions. 

Data scientists, IT teams, software developers and business professionals must collaborate to guarantee that the model operates effectively. This is a significant challenge because there is frequently a language barrier between the programming language used to create a machine learning model and the languages that the production system can understand. 

When deploying and maintaining machine learning models in a production environment, consider the following:

  • Data quality: The data quality that machine learning models are trained on has a significant impact on the accuracy and dependability of the models. Businesses could require assistance to make sure the data is accurate and complete, and reflects the issue they are attempting to solve.
  • Model drift: Due to changes in the underlying data or the deployed environment, machine learning models may perform worse over time. The predictions of the model may not be as dependable, losing accuracy, or relevance. To address this issue, build monitoring and alert systems to spot model drift and routinely retrain the model using fresh data.
  • Bias: If the data that machine learning models are trained on is biased or if the model was built with biased assumptions, the conclusions that the models produce may also be prejudiced. This problem may lead to unjust or discriminatory outcomes, which would have serious moral and legal ramifications. To prevent this, make investments in data and model transparency, frequently audit models for bias, and put mitigation mechanisms in place for any biases that are found.

Model deployment 

Machine learning models must be integrated into a system or application that can make predictions in real-time to be deployed in a production setting. The following are the key moments that can be considered:

  • Scalability: Design the machine learning model to scale and manage vast volumes of data, support numerous users and work with various applications. The model architecture needs to be made to manage the workload and data volume anticipated. In addition, some larger solutions may need to be distributed across multiple servers or nodes.
  • Version control: Manage modifications to the machine learning model using version control, and make sure the right version is being utilized in production. Maintain a change record, tag the model versions and save various models in a version control system.

A development environment must be set up, the model must be tested and validated in a staging environment, and the model must be sent to production utilizing an automated workflow.

The data engineering pipelines need to be created to automate the deployment process and ensure the machine learning model is deployed quickly and efficiently for both categories mentioned below.

  • Batch predictions: Using historical data, the deployed machine learning model generates a collection of predictions. When real-time predictions are not required as an output or when the data is not time-dependent, this is frequently adequate. For instance, when deciding which customers to target for a product ad campaign, you would first obtain prediction scores for all customers, sort your model’s predictions to determine which customers are most likely to make a purchase, and then focus on perhaps the top 5% of those customers.
  • Real-time predictions (also known as on-demand predictions): These predictions are made using the input data that is available at the moment of the request. For example, when a user opens the Uber Eats app, they get a list of suggested restaurants and an estimated delivery time. What appears to be incredibly straightforward and simple in the app, however, does not accurately reflect the complexity of the process.

Businesses should make sure that its models are scalable, safe, dependable and can provide real-time predictions to consumers in a production setting by adhering to these machine learning model deployment principles.

Deployment with containerization

There is currently no open, standardized method for deploying machine learning models. Containerization has become the de facto norm. Containerization is the process of packaging code and dependencies together into a unit that can run across different environments.

One widely used method is to put the code for the machine learning model forecast and its tech stack (dependencies) into a Docker container, which is a standalone executable package of software that has everything you need to run the application.

The execution is then carried out by a system that automates deployment such as Kubernetes or a substitute (for example, Azure Container Instances or AWS). Then, a REST API, which connects applications, makes the model’s features, such as forecasts, accessible.

Deploying machine learning models as serverless functions

Serverless computing is an event-driven compute service that runs code for almost any application. Since developers do not need to manage infrastructure, serverless implementation of code can increase productivity as developers can spend more time writing code. Ultimately, serverless functions are stateless and are only executed when you need them. This makes them highly cost-effective solutions for many applications.

With serverless functions, data scientists can move beyond Jupyter notebooks and just training models to building fully fledged prediction services that use ML models.

For example, serverless computing works well for single-function applications such as chatbots that receive many requests of various complexity. Since chatbot user demands are unexpected, allocating a static server to them may result in underused bandwidth or a capacity crisis/issues. Major cloud services like Azure Functions, AWS Lambda or Google Cloud Functions support this function. 

Support and monitor via AI Control Tower: How to optimize and maintain AI/ML models

In the world of machine learning, building a powerful model is the first important step of the journey. The next challenge is maintaining its performance, ensuring accuracy and adapting it to changing data dynamics. Maintenance of an AI model must be responsible, compliant and effective. This is where the art of monitoring machine learning models comes into play, ensuring that your models continue delivering results you can rely on.

Monitoring is detecting and measuring any issues that might come up with the machine learning models. Machine learning model monitoring gives data scientists visibility into model performance, like monitoring applications for performance, reliability and error circumstances. When ML is used for datasets with high volatility or when predictions are made using models, ML monitoring is particularly crucial.

Unexpected outliers must be reported to data scientists. AI models are frequently probabilistic, meaning they can produce a variety of outcomes. An outlier, or result that is far beyond the typical range, can occasionally be produced by models. When they go unreported, outliers frequently have serious negative effects that can disrupt business outcomes. ML teams should also keep an eye on trends and changes in product and company indicators that are directly affected by AI to ensure that models have a meaningful impact on the real world.

Let’s take predicting the daily price of a stock as an example. Long short-term memory (LSTM) algorithms can offer basic forecasts when market volatility is minimal, while more thorough deep learning algorithms can increase accuracy. However, when markets are extremely volatile, most models will find it difficult to generate accurate predictions. Model monitoring will alert users to these circumstances. 

A distinct type of ML model often performs classification tasks like rating a product’s quality as best or defective, and performance may be checked using precision and recall measures. In contrast to recall, which measures a model’s sensitivity, accuracy contrasts real positive results with those the model predicted. ML model drift, such as concept drift or data drift, which happens when the input data changes or when the underlying statistics of what is being projected change, can also be foreseen.

Another issue is explainable machine learning, which places pressure on models to identify the input qualities that have the greatest impact on the outcomes. This problem is related to model bias, in which the training set contains statistical errors that skew the model’s predictions.

These challenges can destroy confidence and lead to serious business issues. Throughout the phases of development, training, deployment and monitoring, model performance management seeks to address them. Enterprises want solutions that provide context and visibility into model behaviors throughout the entire life cycle — from model training and validation to analysis and improvement — to ensure that ML models are not biased or inaccurate. 

Solution and model governance

Managing and regulating a machine learning model’s lifecycle, from creation to deployment and maintenance, is known as model governance. This ensures the model’s accuracy, dependability and trustworthiness, as well as its alignment with the organization’s goals and compliance requirements. We can create alerts and take steps to control the system when needed after monitoring and analyzing the production data.

For instance, when model performance deteriorates (for example, low accuracy, high bias and so on) below a defined threshold, it sends the product owner or quality assurance expert a warning. They then retrain and use a different model.

Effective model governance includes:

  • Setting access controls for all models in production
  • Versioning all models
  • Creating the right documentation
  • Monitoring models and their results
  • Implementing machine learning with existing IT policies

Monitoring and tracking indicators such as recall, precision and accuracy ensure that machine learning models perform at their peak and deliver accurate and actionable results.

It’s also important to monitor your infrastructure so it is set up correctly and operating without a hitch. Make sure that your models are working to their maximum potential and offering insightful data for your company by following a thorough monitoring checklist and periodically checking their performance.

Implementing machine learning is getting more important as more apps employ a rising amount of data. Machine learning algorithms are the brains behind online services, home appliances and smart devices. Machine learning’s success can help a variety of business areas, including high-performance computing, data management and safety-critical systems, all of which have a lot of potential.

Make AI work for your business

We developed the Columbus AI Innovation Lab to support businesses through their digital journey and to help you make artificial intelligence work for your business. Our AI blog series walked you through what you need to know at each step. Even with this resource, we understand that making AI work for your business can be overwhelming.

While we covered many operational and technical aspects of deploying and monitoring a new ML model here, it’s vital to consider the organizational considerations for success, such as organizational preparedness and change management. These are just as important if you want your organization to succeed with AI. 

You must get buy-in from all stakeholders and align them to the project goals by communicating the benefits of the AI use case, addressing concerns and highlighting how its deployment will help you meet your organizational goals and strategies. 

Deploying a ML model requires collaboration between different teams, including data scientists, engineers, domain experts, business analysts and legal/compliance teams. Effective communication and collaboration among these teams are essential for a smooth deployment. 

Introducing an ML model often requires changes in existing workflows, processes and decision-making. Proper change management practices should be in place to help employees adapt to these changes. Training sessions for employees who will interact with the model are crucial, but other change management practices include helping users understand the model’s benefits, capabilities and limitations as well as addressing users’ potential resistance. Employees must learn to interpret the model’s outputs effectively and make informed decisions based on its predictions. 

Organizations should also establish processes and procedures as part of the implementation process. This includes ethical guidelines for using the model and to help users understand potential biases, fairness and accountability. Developing a way to receive feedback from users and stakeholders after the model is deployed is essential to provide insights for further improvements. 

And how do you measure the success of the deployed solutions? Every organization should clearly define key performance indicators (KPIs) to measure the success of the models. This could include accuracy improvements, cost savings, user satisfaction or other relevant metrics. 

By following all phases of the Columbus AI Innovation Lab and change management best practices, organizations will be ready to reap the benefits of AI. 

Columbus AI Innovation Lab

Our experienced team is ready to guide you at any stage of your AI journey. With our deep technical expertise and solid business know-how, we can help you identify the potential of AI for your business and implement AI technologies to drive impactful outcomes.

Whether you are at the beginning of AI adoption, implementing an AI project, or would like to optimize AI projects within your organization, reach out to discuss how we can support you.

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