The latest articles on DEV Community by Jennifer Kwentoh (@chiazor). https://dev.to/chiazor https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F323302%2F41e89578-fb49-4d35-85bb-5c2027ed287f.jpeg https://dev.to/chiazor en Jennifer Kwentoh Thu, 19 Aug 2021 08:25:45 +0000 https://dev.to/chiazor/machine-learning-in-production-phases-3kd5 https://dev.to/chiazor/machine-learning-in-production-phases-3kd5 <p><a href="https://res.cloudinary.com/practicaldev/image/fetch/s--MSzJiqZQ--/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880/https://jenniferkwentoh.com/wp-content/uploads/2021/07/introduction-to-mlops_ml-in-production-1-1024x576.png" class="article-body-image-wrapper"><img src="https://res.cloudinary.com/practicaldev/image/fetch/s--MSzJiqZQ--/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880/https://jenniferkwentoh.com/wp-content/uploads/2021/07/introduction-to-mlops_ml-in-production-1-1024x576.png" alt="deploying machine learning models in production"></a><br> In deploying <a href="https://jenniferkwentoh.com/what-is-machine-learning-fundamentals/">machine learning</a> models to the production environment, It is important to consider performance in the real world. <a href="https://jenniferkwentoh.com/category/machine_learning_in_production_mlops/">Machine learning</a> operation involves model testing, versioning, continuous deployment (CI/CD), availability, and monitoring.</p> <h2> These phases below are essential in building an excellent end-to-end machine learning system. </h2> <ol> <li><p><strong>Project Scope</strong>: In a typical MLE project, it is crucial first to define the scope of the project. An excellent project scope will define the outcome of the project.</p></li> <li><p><strong>Data Engineering</strong>: this phase defines the methods and techniques used to collect, organize and store big data. Some other ways to clean and preprocess data.</p></li> <li><p><strong>Build Machine Learning Models</strong>. At this phase, you already have the correct data defined in your scope documentation. Different machine learning algorithms are applied in training and testing a good model. </p></li> <li><p><strong>Model Deployment</strong>. Deploy models to connect with new or existing applications either natively or through application interfaces (API). </p></li> <li><p><strong>Model Monitoring and Maintenance</strong>. It is crucial to monitor models in production. Monitor its performance over time and if there is a need to retrain based on new information.</p></li> </ol> machinelearning ai mlops