Turning your AI aspirations into reality: MLOps best practices for your industry

Enterprise leaders are investing in artificial intelligence (AI) and machine learning (ML) technologies at an unprecedented pace, unlocking business insights from their treasure troves of data. But managing the lifecycle of these solutions can prove complex, time-consuming, and expensive without a proper framework. That's when machine learning operations (MLOps) comes in.

MLOps is a set of principles that ensures enterprises maximize the value of their digital investments, helping technology teams develop, deploy, monitor, and scale AI models. By embracing MLOps, organizations can replicate successful applications while mitigating the potential risks of not having a foundation for continuous innovation.

Here, we show you some of the challenges and opportunities in three industries, revealing how MLOps accelerates AI and ML adoption.

Challenges

As healthcare and life science companies invest in new technologies to boost efficiencies and reduce costs, the complexity of healthcare data means AI solutions are difficult to scale. Some of the most common hurdles include:

• Data security and regulation: Extracting the most value from AI and ML is often intricate because of insufficient data
• Disconnected data sources: Disjointed data is the most significant barrier to interoperability in healthcare
• Distrust in new technologies: A lack of transparency and understanding of AI models is the primary source of user mistrust

Best practices

With MLOps, healthcare and life science leaders can seamlessly integrate AI into business operations. Some of the guidelines we suggest are:

• Implement a responsible AI framework as part of the MLOps pipeline to improve transparency and, in turn, mitigate end users' distrust
• Ensure data aligns with the compliance policies of the country where you operate
• Monitor latency caused by anomalies in data and alert teams when a model breaches a critical threshold

The accounting team at a global healthcare equipment manufacturer struggled to manage high volumes of accounts receivable, resulting in payment delays, tied-up working capital, and other inefficiencies. To solve these pain points, we adopted an MLOps strategy to create an autonomous accounts receivable solution.

First, our team gathered data containing more than 3.6 million invoices. Then, we built a customer-segmentation model to find variables influencing customer payment behaviors.

Through orchestrated pipelines, design patterns, and an MLOps framework, our AI solution could process more than 20,000 transactions in less than five minutes, identifying those customers at risk of missing a payment with 87% accuracy. Equipped with this information, teams at this Fortune 500 company could make decisions at speed, freeing up cash and strengthening working capital.

Challenges

Artificial intelligence is changing how insurance companies operate in the digital age – from distribution, underwriting, and pricing to claims. Yet, the industry continues to struggle with issues such as:

• Anti-selection and fraud management: Limited access to sensitive and predictive data hinders decision-making
• Lack of data consistency: Fragmented and disconnected data makes ML deployments complicated and costly

Best practices

Evaluating new models should not be restricted to metrics such as accuracy, precision, or recall. Business KPIs collected through online experimentation should be considered, too.

Here are a few other best practices:

• Use robust anomaly detection algorithms to identify high-risk customers accurately
• Apply data anonymization techniques to achieve actuarial fairness
• Evaluate all models for ethics and compliance

Facultative reinsurance is a highly competitive market. Our client, a global reinsurer, is at the forefront of underwriting and claims handling. However, with the arrival of digitally savvy newcomers, the traditional insurance model is under siege.

To effectively compete in this new environment, our client set out to automate the underwriting process, driving efficiencies to get an edge over the competition. So, we jumped into action, following an MLOps approach and using AI and advanced analytics to objectively assess and predict metrics such as credit risk, liquidity risk, and market risk.

The result? Underwriters across the organization gained access to a single source of truth to review all supporting data, significantly expediting decision-making. Through this initiative, we estimate our client could boost top-line growth by 12–15% annually.

Challenges

Apart from facing fierce competition and tight revenue margins, enterprise leaders in the consumer goods and retail industry commonly deal with:

• Unreliable database performance: In the digital age, any hiccups in database performance translate to missed sales and poor customer experiences – these issues also impact ML models
• Evolving customer behavior: Purchasing habits continue to change, forcing engineers to redeploy AI algorithms constantly

Best practices

To address these challenges and stay at the forefront of innovation, MLOps teams operating in the retail industry should:

• Embrace data harmonization to map key features contributing to the model outcome
• Consistently check for seasonality, time dependency, and imbalance within target data distributions
• Use automated reporting, detailed model performance, and drift detection to confirm model accuracy

For many retailers, supply chain disruptions can wreak havoc on their business. From natural disasters to political unrest, supply chain operations are at constant risk of interruption. However, by harnessing the power of AI and ML, organizations can detect, verify, analyze, and forecast potential outcomes from these threats, providing early warning signals and potential solutions.

For example, we worked with a leading food company to develop an analytics solution to generate short-term forecasts and augment human capabilities. By embracing an MLOps framework and using big data – such as 3.6 million historical records on orders and shipments – our solution dynamically accounts for any potential risks within an eight-week framework.

With this information, employees at this food company could quickly gather insights to navigate any potential issues on the horizon.

As organizations ramp up artificial intelligence investments to build resilience, increase efficiencies, and drive growth, enterprise leaders increasingly recognize the importance of MLOps – especially considering that less than half of pilot projects ever make it to production.

By incorporating machine learning operations from the earliest design phases of any AI project, teams gain the full benefits of ML – without the headaches. These ML solutions can then be effectively scaled and replicated across the enterprise for a greater return on investment and competitive advantage.