While generative AI dominates today's headlines, traditional predictive machine learning models continue to drive critical business decisions across industries. To ensure predictive models achieve a solid ROI, well after models are initially deployed,  establishing a Machine Learning Operations (MLOps) plan is essential. MLOps is the practice of streamlining the entire lifecycle of machine learning models—from development and training to deployment, monitoring, and maintenance—in a repeatable, scalable, and governable way. Think of it as bringing software engineering discipline to machine learning, ensuring that your AI investments don't remain theoretical exercises but become dependable business assets that continue to deliver value over time. Without robust MLOps practices, models often degrade in production as data shifts over time. What begins as an impressive prototype can quickly become unreliable, leading to poor decision quality with real financial consequences. Poor model accuracy directly impacts business outcomes, diminishing your ML investment's ROI and potentially creating compliance risks.  Implementing MLOps can seem daunting, but with the right platform and processes, organizations can establish systems that maximize their ROI. The first step is to understand the critical steps and phases of the machine learning life cycle. Then identifying the framework and tools required to handle these phases. Cloudera AI offers an integrated environment designed to address each critical stage of the machine learning lifecycle. The Machine Learning Lifecycle with Cloudera AI: Machine Learning Operations with Cloudera Business Inputs & Data Engineering Leverage Cloudera's data connections to seamlessly access data from diverse sources Integrate business requirements directly into the ML pipeline through Cloudera's Feature Store Data Science Work in customizable Sessions with pre-configured runtimes for Python, R, and Spark and use integrated JupyterLab and Workbench environments for collaborative development Apply secure data access controls through Cloudera SDX Model Security framework Model Training Track experiments through native MLflow integration within Cloudera's Model Catalog Scale training with distributed computing resources via Kubernetes Machine Learning Operations Packaging: Containerize models with dependencies automatically managed through Cloudera SDX Deployment & Serving: Deploy models as REST APIs with a few clicks through Cloudera's Model Governance system Monitoring: Track model performance and detect drift through dedicated monitoring dashboards Closed Loop ML Implement automated retraining pipelines when monitoring triggers performance thresholds Ensure continuous model improvement with feedback loops from production to training Enterprise Governance Implement comprehensive model governance through Cloudera SDX (Shared Data Experience) providing unified security and governance Leverage the Cloudera Data Catalog to track model assets, metadata, and maintain governance across the ML lifecycle This end-to-end MLOps framework ensures organizations can efficiently operationalize machine learning while maintaining security, governance, and scalability throughout the entire lifecycle. Hands-On MLOps: The Banking Marketing Campaign Example To see these capabilities in action, let's explore the banking marketing campaign example available in the cml-banking-mlop-marketing-campaign repository. This project implements a complete MLOps workflow for a common banking use case: predicting which customers are likely to subscribe to a term deposit during a marketing campaign. The repository provides a step-by-step guide through the entire process: Data acquisition and storage using Cloudera's data connections to ingest the UCI Bank Marketing dataset and store it in a data lake with Apache Iceberg format, ensuring version control and proper governance. Exploratory data analysis with JupyterLab to understand customer characteristics and their correlation with campaign outcomes, demonstrating Cloudera AI's interactive analysis capabilities. Model training with MLflow to systematically experiment with different XGBoost configurations, tracking all parameters, metrics, and artifacts. This showcases how Cloudera AI's integrated experiment tracking simplifies model development. Model deployment as a REST API using Cloudera AI's Models functionality, making predictions available to other applications through a standardized interface with proper authentication and monitoring. Automated retraining and updating through a sequence of Jobs that simulate new data arrival, retrain models, and update deployments—demonstrating Cloudera AI's automation capabilities. Performance monitoring with a dashboard that tracks model accuracy over time, alerting when performance degrades and triggering the retraining workflow. This example showcases Cloudera AI's ability to orchestrate the entire MLOps lifecycle without requiring complex integration of disparate tools. Each component—from data connections to experiment tracking to model deployment—works together seamlessly, allowing data scientists and ML engineers to focus on creating value rather than managing infrastructure. The Banking Marketing MLOps lab demonstrates a practical example of managing a machine learning model throughout its lifecycle. The use case focuses on a common challenge in banking: predicting which customers are likely to subscribe to a term deposit during a marketing campaign. The lab begins with real customer data from the UCI Bank Marketing dataset, which contains information about customer demographics, previous interactions, and whether they subscribed to term deposits. This historical data serves as the foundation for training our initial classification model using XGBoost and tracking experiments with MLflow. This lab simulates the passage of time – a critical element often overlooked in ML examples. After deploying the initial model as a REST API endpoint, the lab uses Cloudera's data generation capabilities to create synthetic customer data that represents new interactions over time. This mimics the real-world scenario where models must process fresh data that may differ from their training distribution. ... View more

According to a survey conducted by Kaggle in 2021, Python is still the most commonly used programming language for data science with over 80% of respondents choosing it as their preferred language. However, R continues to be a popular language among data scientists, with over 15% of respondents choosing it as their primary language.   One of the reasons for R's continued popularity is its strong statistical analysis capabilities. R was designed specifically for statistical computing and provides a rich ecosystem of packages for data analysis and visualization. This makes R a powerful tool for data scientists who need to analyze large datasets and perform complex statistical modeling.   In this article, we'll delve into how to deploy R models in CML, highlighting the steps and key considerations to keep in mind when building and deploying models in this environment.   CMLs Model Framework   As a refresher, let's revisit the key concepts of a model in CML. CML's framework allows for maximum flexibility when it comes to deploying models. Here is the reference diagram showing the fundamental concepts of a model.  Models - Concepts and Terminology   The model artifacts are actually called from within a Python or R script file. Regardless of the runtime used, you will need to embed your prediction logic within a function. The input arguments sent to the CML model are in JSON format. By the time these parameters are ingested by the function within the R script file, it becomes an R list object. This is important to note because this will determine what, if any transformations, need to occur before getting to the prediction step in your code.    Simple Add Model in R Let’s start by looking at a deployed model below for a CML model that adds two numbers. In this case, we take the two elements from the function arguments and add them. R wrapper script The CML model parameters, or in this case the named list elements are defined when the CML model is deployed. Deploying the add 'model' Working with actual prediction models The example above helps us get started with using an R model in CML. Now let’s look at two model examples with a focus on the R script file and how parameters are ultimately passed to the model object. For the two models, we deploy below. We’ll be using the Cars93 dataset.    Simple Linear Regression In the example below we are using the Cylinders and Weight as features (or independent variables) to predict our dependent variable - MPG.City. You can follow the details here to see how the model was built R-CML in github   In this example, you will note that no further transformation is required. The input parameters were passed directly into the prediction step.   Decision Tree Model  In our final model, we’ve gotten slightly more sophisticated, included more features and now using a decision tree model. We trained our model so that it takes R data frame objects as inputs for predictions. Therefore we need the appropriate step to transform our list into a data frame.     Below we can see how we define the json input format for the model.    I hope this has given you enough information to go and build your own R models in CML! Happy model building! ... View more