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Blog Summary:
MLOps is the practice of streamlining the lifecycle of AI models, from development to deployment and monitoring. It’s crucial for businesses leveraging AI to scale, including tech companies, financial institutions, and healthcare providers, to ensure efficient and reliable model performance. This blog explores MLOps’ use cases and strategies to enhance model deployment.
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From the moment humans first dreamed of automating manual processes, the connection between man and machine has been inseparable. It can be traced back to the earliest glimpses of artificial intelligence (AI) with ancient automata—mechanical devices designed to mimic human actions.
Fast-forward to 1956, and AI technology established itself as a field formally starting the journey of “thinking machines.” These early efforts laid the foundation for what would eventually evolve into modern-day MLops Use Cases.
Today, machine learning operations (MLOps) stand at the intersection of innovation, automation, and scalability, changing the way businesses approach AI. With the global MLOps market size expected to reach USD 34.7 billion by 2032, it’s clear that the demand for operationalizing machine learning has grown exponentially.
From crafting AI models to deploying and monitoring them at scale, intelligent systems have become more reliable, efficient, and impactful than ever before. In this blog, we’ll explore how MLOps enables seamless collaboration between humans and machines across different industries.
Beginning the machine learning (ML) lifecycle requires your organization to prepare data by fetching it from different sources and performing aggregation, cleaning, and feature engineering. The extracted data is then used to train and validate the model so that it can serve as a prediction platform for other applications accessible through the APIs.
The ML lifecycle is a comprehensive process where ML models are developed and deployed in a structured manner. Each phase is critical for the success of the entire model, helping your business solve complex problems by getting data-driven insights.
Machine Learning Operations (MLOps) ensure that this lifecycle is smooth, reusable, and scalable. MLOps build models by feeding them data to help them recognize patterns, move the model from a testing environment to the real world, and monitor its performance for retraining.
Without MLOps in place to guide and direct the ML projects, your business can face many challenges:
While ML focuses on all the technical aspects of refining a training model, MLOps focuses on how an ML model should be implemented and managed in real-world settings. MLOps, hence, ensure that the learning models meet the application requirements and keep adapting to the relevance of the changing world.
Building advanced AI is like launching a rocket. The first challenge is to maximize acceleration, but once it starts picking up speed, you also need to focus on steering.
A number of organizations worldwide have implemented MLOps to standardize their operational processes. Data pipelines are extremely complex and require proper maintenance to ensure they are adaptable for scalability.
Hence, businesses must learn to implement MLOps effectively. To help them improve their processes, we have listed the top MLOps use cases:
By helping organizations stop fraud in real-time, MLOps provide a safe environment for financial institutions and their customers. It analyzes huge volumes of transactional data to detect some patterns and locations of transactions.
Common Applications:
For example, Payoneer from the United States utilized the Iguazio MLOps model to build a fraud prediction and prevention model that can adapt to new threats.
Patient outcomes are now powered by the real-time insights gained from AI-based medical imaging. The predictive models help healthcare professionals anticipate patients’ needs and provide suggestions on how to improve their care and treatment. MLOps ensures that the models are up-to-date with fresh data, accurate, and adaptable to new techniques.
Common Applications:
For example, Philips, located in the Netherlands, uses the ClearML model to save hours by implementing the MLOps best practice of streamlining experiment tracking and automating the documentation process. Steward Healthcare is another example of using predictive models for real-time patient insights.
Today’s utility inspection companies use computer vision techniques, drone cameras, and data analysis algorithms to identify defects and threats and reduce personnel access to hazardous areas. With MLOps, they can automate the deployment of such models that analyze data received from inspections with improved accuracy and speed.
Common Applications:
For example, EasyFlow offers AI technology solutions for power line inspections with object detection and segmentation. Sharper Shape implements the MLOps platform Valohai to identify broken insulators and encroachments.
Intelligent asset maintenance in manufacturing facilities utilizes ML operations for equipment data analysis, predicting machine failures before they occur. With MLOps, organizations can minimize equipment downtime and improve efficiency.
Common Applications:
For example, Oyak Cement in Turkey implemented the DataRobot MLOps model, which helped them increase alternative fuel usage by sevenfold and reduce costs by USD 39 million.
The pharmaceutical industry utilizes MLOps models to analyze large volumes of chemical compounds to discover new drugs. By identifying potential combinations, these companies can streamline the drug development processes and reduce the time and money needed to introduce new treatments.
Common Applications:
For example, Pfizer uses MLOps to study drugs’ molecular details, determine their effectiveness, and enhance the efficiency of clinical trials.
Agricultural companies use MLOps to scale their operations by automating the collection, processing, and analysis of data from sensors, drones, and IoT devices. MLOps enables seamless deployment and monitoring of models, ensuring they adapt to changing weather or soil conditions.
Common Applications:
For example, AgroScout in the United States implemented the ClearML MLOps model, which helped them increase the experiment volume by 50 times and decrease crop production time by half.
E-commerce companies implement MLOps to scale their AI technology models and deliver hyper-relevant personalized experiences. They can analyze vast amounts of browsing behavior, purchase history, and preferences—to create tailored product recommendations, personalized discounts, and dynamic website content.
Common Applications:
Amazon is the prime example of using MLOps to analyze user behavior, dynamically pricing millions of daily users, and managing an extensive product catalog.
In construction, companies utilize the power of AI technology to streamline their workflows, designs, and materials by tracking experiments. Using MLOps tools in ML models, they can analyze simulations, site sensors, and historical projects to test material strength, energy efficiency, or project timelines and refine models to build the most effective strategies.
Common Applications:
For example, Constru, a construction technology company, utilizes an AI computer vision platform to analyze construction processes. By implementing ClearML’s open-source MLOps platform, Constru effectively enhances its ability to process and interpret vast amounts of visual data from construction sites.
AI technology models for forest management help optimize forest health monitoring by predicting wildfire risks and managing deforestation. With MLOps, all the processes are automated, from data processing to model deployment, to speed up real-time data analysis using satellite imagery, IoT, and weather data.
Common Applications:
For example, CollectiveCrunch utilized MLOps to build and deploy an AI detection tool for biodiversity and carbon changes, reducing the development time by 90%.
In chemical manufacturing, companies utilize MLOps for faster AI model deployment for process optimization, quality control, and predictive maintenance. MLOps allows AI technology models to quickly analyze sensor data from reactors or production lines, identify inefficiencies, suggest adjustments in real-time, and adapt to raw material variations.
Common Applications:
For example, Ecolab, a chemical manufacturer in the United States, implemented the Iguazo MLOps model and decreased the deployment time of its AI model from 365 days to up to 90 days.
Kickstart your MLOps journey and transform your ML workflows to scale your AI models with data-driven insights.
MLOps effectively manages and implements the entire ML lifecycle using a combination of both technical and soft skills, tools, and practices. These include ML frameworks like TensorFlow and PyTorch for developing the model, tools like Git for version control, Jenkins for CI/CD pipelines, cloud computing with Azure and AWS, and platforms like Kubeflow.
MLOps oversees all the phases of the process with all these tools to streamline the entire data pipeline, which is comprised of different components. Here’s how MLOps use cases can be implemented effectively:
MLOps extracts data from sensors, APIs, and databases, cleaning and preparing it for analysis with normalization, handling missing values, and creating useful features. Then, data versioning takes place, making tracing, replicating, tracking, and managing easier using MLOps tools like Luigi, Airflow, and Data Version Control (DVC).
Best Practice: For example, you want to prepare data for building an e-commerce recommendation model that predicts the products a user might like based on their purchase and browsing history.
You can set up triggers for automated model retraining whenever a new dataset, such as purchases or clicks, is added to the database.
This phase focuses on building and refining models using the prepared data and selecting algorithms. It keeps detailed records of different versions, compares them, and suggests the most effective approaches to optimize performance. To log experiments, it uses tools like TensorBoard and Weights & Biases to standardize environments.
Best Practice: For example, you have built a healthcare prediction model that predicts the likelihood of diseases based on patients’ health records. You can integrate the ML model with the existing electronic health records (EHR) system that provides real-time risk scores during patient visits.
The next phase starts by deploying the ML model in production and creating an automated pipeline. Support from various deployment strategies and tools, such as KServe and Seldon Core, ensures the models are smoothly integrated into applications. Once the model is deployed, it starts serving to deliver API outputs.
Best Practice: For example, you have deployed a fraud detection model to detect fraudulent credit card transactions based on user behavior. An effective implementation strategy could be using machine learning development services to deploy the model in real-time transaction processing systems to set up immediate alerts.
Monitoring and vigilance are essential to identifying emerging issues like drifts in data accuracy, biases, or concerns about fairness. Hence, setting up a notification system is essential in this step, which is helpful for flagging any performance degradation signs.
Best Practice: For example, you want to monitor a demand forecasting model for retail processes to predict future demand based on external factors like a vacation. You can track the accuracy of forecasts based on unexpected events to optimize stock levels.
Once the model has been monitored, the insights gained are fed again into the system to retrain it. It begins the improvement process with newly structured algorithms to boost performance. Since this isn’t a one-time process, it should be done recurringly to reflect changes in underlying patterns.
Best Practice: For example, you want to retrain an existing predictive maintenance model for transportation to detect which vehicle needs maintenance based on sensor data. You can utilize edge computing to get real-time alerts and retrain the model using new sensor data on vehicle usage patterns.
From detecting rare diseases in healthcare to optimizing supply chains in manufacturing, MLOps powers industries with precise, scalable AI technology solutions. Let’s explore how MLOps is being used in different sectors with real-world stories of how it turns data into actionable insights, reshaping industries:
MLOps streamlines the development and deployment of AI models in healthcare for real-time insights, from predicting patient outcomes to diagnosing diseases.
For instance, the Mayo Clinic uses MLOps to refine AI models that detect early signs of breast cancer, ensuring better patient outcomes with scalable, accurate solutions while adhering to stringent regulatory standards.
In finance, MLOps enhances fraud detection, risk modeling, and personalized financial services by automating model deployment and monitoring.
For example, JP Morgan Chase uses MLOps to scale AI models that predict fraudulent transactions, reduce false positives, improve response times to threats, and ensure security and trust for millions of customers.
Retailers harness MLOps for personalized recommendations, inventory optimization, and demand forecasting.
For example, Amazon employs MLOps to run recommendation engines that process billions of customer interactions daily. These engines deliver tailored product suggestions and improve the shopping experience while ensuring scalability during peak sales events like Amazon Prime Day.
MLOps in manufacturing enables predictive maintenance and process optimization by managing complex AI technology workflows.
General Electric leverages MLOps to monitor turbines and engines, using real-time data and predictive analytics to reduce downtime and maintenance costs and drive operational efficiency on a global scale.
MLOps supports reservoir modeling, drilling optimization, and predictive maintenance in oil and gas.
Shell integrates MLOps to optimize drilling operations, combining historical and real-time data to improve safety and efficiency while reducing environmental impact—an essential capability in high-stakes energy exploration.
Machine learning requires dealing with large sets of sensitive data, especially when scaling an organization. As the quantity of data grows, maintaining the quality can become challenging.
While this requires a cultural shift, it also needs to bridge the gap between skills and capabilities. However, deploying an ML model is only as good as its ability to adapt and perform.
Moon Technolabs is your ideal partner for MLOps consulting services to enhance your model deployment processes. From automating data pipelines to deploying and monitoring ML models, we ensure a scalable, efficient, and reliable MLOps infrastructure tailored to your needs.
Contact our MLOps experts today.
Without an effective MLOps process, organizations struggle with unscalable workflows, model decay, and a lack of monitoring, leading to underperformance and missed opportunities. Successful implementation of MLOps use cases can only be achieved when organizations prioritize a transparent ML model deployment process.
Implementing MLOps ensures that your ML lifecycle is consistently automated, optimizing it for scalability while deploying, monitoring, and retraining for the best performance.
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Jayanti Katariya is the CEO of Moon Technolabs, a fast-growing IT solutions provider, with 18+ years of experience in the industry. Passionate about developing creative apps from a young age, he pursued an engineering degree to further this interest. Under his leadership, Moon Technolabs has helped numerous brands establish their online presence and he has also launched an invoicing software that assists businesses to streamline their financial operations.
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