{"id":48997,"date":"2025-04-05T16:58:50","date_gmt":"2025-04-05T16:58:50","guid":{"rendered":"https:\/\/www.devopsschool.com\/blog\/?p=48997"},"modified":"2026-02-21T07:27:31","modified_gmt":"2026-02-21T07:27:31","slug":"mlops-lifecycle-phases-and-the-best-tools-for-each-stage","status":"publish","type":"post","link":"https:\/\/www.devopsschool.com\/blog\/mlops-lifecycle-phases-and-the-best-tools-for-each-stage\/","title":{"rendered":"MLOps Lifecycle Phases and the Best Tools for Each Stage"},"content":{"rendered":"\n

MLOps<\/strong> stands for Machine Learning Operations<\/strong>.
It\u2019s like DevOps<\/strong> for machine learning \u2014 but instead of just managing software code, you manage data, models, and ML workflows<\/strong>.<\/p>\n\n\n\n

\ud83e\udde0 Simple Definition:<\/strong><\/h3>\n\n\n\n
\n

MLOps is a way to build, train, deploy, and monitor machine learning models in a reliable, automated, and repeatable way.<\/strong><\/p>\n<\/blockquote>\n\n\n\n

\n\n\n\n

\ud83e\udde9 Why Do We Need MLOps?<\/h2>\n\n\n\n

Imagine a data scientist builds a great model on their laptop. That\u2019s great, but…<\/strong><\/p>\n\n\n\n

    \n
  • How do we get it into production<\/strong>?<\/li>\n\n\n\n
  • How do we track versions<\/strong> of the model and the data it used?<\/li>\n\n\n\n
  • What happens when the model\u2019s performance drops over time<\/strong>?<\/li>\n\n\n\n
  • How do we automate retraining<\/strong> with new data?<\/li>\n<\/ul>\n\n\n\n

    \ud83d\udca1 That\u2019s where MLOps comes in \u2014 it\u2019s the bridge between building a model and running it in the real world<\/strong>.<\/p>\n\n\n\n

    Below is a clean tabular format<\/strong> of each MLOps phase<\/strong>, the best tools<\/strong> used in each phase (2026-ready), and a third column<\/strong> to highlight tools reused across multiple stages<\/strong>.<\/p>\n\n\n\n

    \n\n\n\n
    \n\n\n\n

    \ud83d\udcca MLOps Phases & Best Tools (with Multi-Phase Tools Highlighted)<\/strong><\/h2>\n\n\n\n
    MLOps Phase<\/strong><\/th>Best Tools<\/strong><\/th>Common \/ Reusable Tools Across Phases<\/strong><\/th><\/tr><\/thead>
    1. Data Ingestion<\/strong><\/td>Apache NiFi, Airbyte, Azure Data Factory, AWS Glue<\/td>Apache NiFi (used in preprocessing too)<\/td><\/tr>
    2. Data Versioning<\/strong><\/td>DVC, LakeFS, Delta Lake, Git LFS<\/td>DVC (used in model training & pipelines)<\/td><\/tr>
    3. Data Validation & Quality<\/strong><\/td>Great Expectations, TensorFlow Data Validation, Deequ<\/td>Great Expectations (used during training too)<\/td><\/tr>
    4. Data Preprocessing<\/strong><\/td>Pandas, PySpark, Scikit-learn, AWS Glue<\/td>Pandas, PySpark (used in training as well)<\/td><\/tr>
    5. Experiment Tracking<\/strong><\/td>MLflow<\/strong>, Weights & Biases (W&B), Neptune.ai<\/td>MLflow<\/strong> (also used in model registry & deployment)<\/td><\/tr>
    6. Model Training<\/strong><\/td>PyTorch, TensorFlow, Scikit-learn, XGBoost<\/td>MLflow, DVC (used for reproducibility and tracking)<\/td><\/tr>
    7. Hyperparameter Tuning<\/strong><\/td>Optuna, Ray Tune, Hyperopt, SageMaker Autopilot<\/td>Optuna (integrates with MLflow & KFP)<\/td><\/tr>
    8. Model Evaluation<\/strong><\/td>MLflow, SciKit-learn metrics, TensorBoard<\/td>MLflow (for logging results and comparisons)<\/td><\/tr>
    9. Model Registry<\/strong><\/td>MLflow Model Registry<\/strong>, Seldon Core, BentoML<\/td>MLflow<\/strong><\/td><\/tr>
    10. Model Packaging<\/strong><\/td>Docker, ONNX, BentoML, FastAPI<\/td>BentoML, Docker (used in deployment phase)<\/td><\/tr>
    11. Model Deployment<\/strong><\/td>FastAPI, MLflow Serving<\/strong>, KFServing, Seldon, SageMaker<\/td>MLflow, BentoML, Docker<\/td><\/tr>
    12. Monitoring & Drift<\/strong><\/td>Prometheus, Grafana, Evidently AI<\/strong>, WhyLabs<\/td>Evidently AI (used with pipelines and dashboards)<\/td><\/tr>
    13. Retraining Triggering<\/strong><\/td>Apache Airflow, Kubeflow Pipelines, Dagster, Metaflow<\/td>Airflow\/Kubeflow (also used for orchestration)<\/td><\/tr>
    14. CI\/CD Automation<\/strong><\/td>GitHub Actions, Jenkins, GitLab CI, Argo Workflows<\/td>GitHub Actions (used in retraining & serving)<\/td><\/tr>
    15. Documentation & Auditing<\/strong><\/td>MLflow UI, Pachyderm, Azure Purview, DataHub<\/td>MLflow (central audit and logs)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    \n\n\n\n

    \ud83d\udd01 Most Common Tools Used in Multiple MLOps Phases<\/strong><\/h2>\n\n\n\n
    Tool<\/strong><\/th>Used In Phases<\/strong><\/th><\/tr><\/thead>
    MLflow<\/strong><\/td>Experiment Tracking, Model Evaluation, Model Registry, Deployment, Audit<\/td><\/tr>
    DVC<\/strong><\/td>Data Versioning, Model Training, Pipelines<\/td><\/tr>
    Airflow<\/strong><\/td>Retraining, Data Ingestion, CI\/CD Pipelines<\/td><\/tr>
    BentoML<\/strong><\/td>Model Packaging, Deployment<\/td><\/tr>
    Docker<\/strong><\/td>Packaging, Serving, CI\/CD<\/td><\/tr>
    Evidently AI<\/strong><\/td>Evaluation Monitoring, Drift Detection, Model Monitoring<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    \n\n\n\n

    Here’s a simple and practical explanation<\/strong> of each MLOps phase<\/strong>, designed to help anyone (even beginners) understand the end-to-end machine learning lifecycle<\/strong>:<\/p>\n\n\n\n

    \n\n\n\n

    \ud83e\udde9 MLOps Lifecycle Explained Simply (Phase-by-Phase)<\/strong><\/h2>\n\n\n\n
    \n\n\n\n

    1. Data Ingestion (Getting the Data)<\/strong><\/h3>\n\n\n\n
      \n
    • What happens:<\/strong> Collect data from various sources \u2014 databases, files, APIs, etc.<\/li>\n\n\n\n
    • Why it matters:<\/strong> Your model is only as good as the data you feed it.<\/li>\n\n\n\n
    • Real-life example:<\/strong> Pulling sales data from an online store and customer reviews from Twitter.<\/li>\n<\/ul>\n\n\n\n
      \n\n\n\n

      2. Data Versioning (Tracking the Data Changes)<\/strong><\/h3>\n\n\n\n
        \n
      • What happens:<\/strong> Save different versions of your data as it changes over time.<\/li>\n\n\n\n
      • Why it matters:<\/strong> So you can re-train your model with the exact same data<\/strong> if needed.<\/li>\n\n\n\n
      • Real-life example:<\/strong> You store the dataset used in a model built in Jan 2024, even if it\u2019s updated later.<\/li>\n<\/ul>\n\n\n\n
        \n\n\n\n

        3. Data Validation & Quality (Checking the Data)<\/strong><\/h3>\n\n\n\n
          \n
        • What happens:<\/strong> Check if data has missing values, unexpected formats, or wrong labels.<\/li>\n\n\n\n
        • Why it matters:<\/strong> Dirty data = broken models.<\/li>\n\n\n\n
        • Real-life example:<\/strong> Ensuring “age” field isn\u2019t negative or missing for any record.<\/li>\n<\/ul>\n\n\n\n
          \n\n\n\n

          4. Data Preprocessing (Cleaning the Data)<\/strong><\/h3>\n\n\n\n
            \n
          • What happens:<\/strong> Clean, normalize, and transform the data to make it model-ready.<\/li>\n\n\n\n
          • Why it matters:<\/strong> Raw data needs polishing before training.<\/li>\n\n\n\n
          • Real-life example:<\/strong> Converting text to numbers, filling in missing values.<\/li>\n<\/ul>\n\n\n\n
            \n\n\n\n

            5. Experiment Tracking (Logging Your Experiments)<\/strong><\/h3>\n\n\n\n
              \n
            • What happens:<\/strong> Track each training run \u2014 parameters, results, model files.<\/li>\n\n\n\n
            • Why it matters:<\/strong> Helps compare versions and know what worked best.<\/li>\n\n\n\n
            • Real-life example:<\/strong> You train 10 models with different learning rates and track all their results.<\/li>\n<\/ul>\n\n\n\n
              \n\n\n\n

              6. Model Training (Teaching the Model)<\/strong><\/h3>\n\n\n\n
                \n
              • What happens:<\/strong> Use the prepared data to train a machine learning model.<\/li>\n\n\n\n
              • Why it matters:<\/strong> This is where the model \u201clearns\u201d from patterns in your data.<\/li>\n\n\n\n
              • Real-life example:<\/strong> Training a model to predict customer churn based on past behavior.<\/li>\n<\/ul>\n\n\n\n
                \n\n\n\n

                7. Hyperparameter Tuning (Optimizing the Training)<\/strong><\/h3>\n\n\n\n
                  \n
                • What happens:<\/strong> Automatically try different combinations of model settings to find the best one.<\/li>\n\n\n\n
                • Why it matters:<\/strong> Fine-tuning can drastically improve model accuracy.<\/li>\n\n\n\n
                • Real-life example:<\/strong> Trying different learning rates, batch sizes, and tree depths.<\/li>\n<\/ul>\n\n\n\n
                  \n\n\n\n

                  8. Model Evaluation (Testing the Model)<\/strong><\/h3>\n\n\n\n
                    \n
                  • What happens:<\/strong> Measure how well the model performs using test data.<\/li>\n\n\n\n
                  • Why it matters:<\/strong> You need to know how reliable the model is before using it.<\/li>\n\n\n\n
                  • Real-life example:<\/strong> Checking model accuracy or error rate on unseen data.<\/li>\n<\/ul>\n\n\n\n
                    \n\n\n\n

                    9. Model Registry (Saving & Versioning the Model)<\/strong><\/h3>\n\n\n\n
                      \n
                    • What happens:<\/strong> Store models with names, versions, and stages like \u201cStaging\u201d, \u201cProduction\u201d.<\/li>\n\n\n\n
                    • Why it matters:<\/strong> Keeps your models organized and production-ready.<\/li>\n\n\n\n
                    • Real-life example:<\/strong> V1 of a model is in staging, V2 is in production.<\/li>\n<\/ul>\n\n\n\n
                      \n\n\n\n

                      10. Model Packaging (Preparing for Deployment)<\/strong><\/h3>\n\n\n\n
                        \n
                      • What happens:<\/strong> Convert your model into a format that can run anywhere \u2014 like an API or a container.<\/li>\n\n\n\n
                      • Why it matters:<\/strong> Makes it easier to deploy models to websites, apps, or services.<\/li>\n\n\n\n
                      • Real-life example:<\/strong> Wrap your trained model in a FastAPI app with Docker.<\/li>\n<\/ul>\n\n\n\n
                        \n\n\n\n

                        11. Model Deployment (Launching the Model)<\/strong><\/h3>\n\n\n\n
                          \n
                        • What happens:<\/strong> Deploy the model to production \u2014 as a REST API, mobile app, or batch job.<\/li>\n\n\n\n
                        • Why it matters:<\/strong> It\u2019s how users or systems can actually use the model.<\/li>\n\n\n\n
                        • Real-life example:<\/strong> A chatbot uses your ML model in real-time to predict user intent.<\/li>\n<\/ul>\n\n\n\n
                          \n\n\n\n

                          12. Monitoring & Drift Detection (Watching the Model in Action)<\/strong><\/h3>\n\n\n\n
                            \n
                          • What happens:<\/strong> Keep an eye on how the model performs over time.<\/li>\n\n\n\n
                          • Why it matters:<\/strong> Models can get \u201cstale\u201d or inaccurate if data changes (concept drift).<\/li>\n\n\n\n
                          • Real-life example:<\/strong> The model was 90% accurate at launch but now it’s 70% \u2014 that\u2019s a red flag.<\/li>\n<\/ul>\n\n\n\n
                            \n\n\n\n

                            13. Retraining & Feedback Loops (Keeping the Model Fresh)<\/strong><\/h3>\n\n\n\n
                              \n
                            • What happens:<\/strong> If performance drops, automatically retrain with fresh data.<\/li>\n\n\n\n
                            • Why it matters:<\/strong> Keeps your model accurate as the world changes.<\/li>\n\n\n\n
                            • Real-life example:<\/strong> Retraining a fraud detection model monthly as new fraud patterns emerge.<\/li>\n<\/ul>\n\n\n\n
                              \n\n\n\n

                              14. CI\/CD for ML (Automating Everything)<\/strong><\/h3>\n\n\n\n
                                \n
                              • What happens:<\/strong> Automate the whole ML workflow \u2014 from code to retrain to deploy.<\/li>\n\n\n\n
                              • Why it matters:<\/strong> Saves time, reduces human error, and speeds up delivery.<\/li>\n\n\n\n
                              • Real-life example:<\/strong> Pushing code to GitHub automatically retrains and deploys the model.<\/li>\n<\/ul>\n\n\n\n
                                \n\n\n\n

                                15. Documentation & Audit Trail (Track Everything for Trust & Compliance)<\/strong><\/h3>\n\n\n\n
                                  \n
                                • What happens:<\/strong> Keep records of what model was used, by whom, on which data, and when.<\/li>\n\n\n\n
                                • Why it matters:<\/strong> Helps with team collaboration, debugging, and legal compliance (like GDPR).<\/li>\n\n\n\n
                                • Real-life example:<\/strong> You can trace exactly what model made a prediction 6 months ago.<\/li>\n<\/ul>\n\n\n\n
                                  \n\n\n\n

                                  <\/p>\n","protected":false},"excerpt":{"rendered":"

                                  MLOps stands for Machine Learning Operations.It\u2019s like DevOps for machine learning \u2014 but instead of just managing software code, you manage data, models, and ML workflows. \ud83e\udde0 Simple Definition: MLOps… <\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_joinchat":[],"footnotes":""},"categories":[2],"tags":[],"class_list":["post-48997","post","type-post","status-publish","format-standard","hentry","category-uncategorised"],"_links":{"self":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/48997","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/comments?post=48997"}],"version-history":[{"count":4,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/48997\/revisions"}],"predecessor-version":[{"id":58944,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/48997\/revisions\/58944"}],"wp:attachment":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/media?parent=48997"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/categories?post=48997"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/tags?post=48997"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}