How do companies deploy and monitor machine learning models without a dedicated tool?

Companies can deploy and monitor machine learning (ML) models without purchasing a dedicated MLOps platform by combining existing DevOps infrastructure, open-source tools, cloud services, and custom Python scripts. This approach is often called DIY MLOps (Do-It-Yourself MLOps). The key is to build a repeatable process for taking a trained model into production, tracking its behavior, detecting problems, and updating it when necessary. How companies deploy ML models without dedicated tools A…

1. AWS — Cloud VM, Kubernetes, AWS EC2, Azure VM.
2. GitHub — Use GitHub Actions to automate deployment.
3. Google Vertex AI
4. Prometheus Group — Prometheus and Grafana are common options for this type of monitoring.
5. Evidently AI
6. Apache Airflow
7. MLflow — For example, MLflow supports packaging models into Docker containers and serving them through REST APIs.
8. Microsoft Azure Machine Learning — Microsoft – Model monitoring in production.

Companies can deploy and monitor ML models without a dedicated MLOps platform by assembling familiar software-engineering components into a lightweight pipeline: Package the model. Save the model artifact, preprocessing code, dependencies, and configuration in Git or an artifact repository. A Docker image makes the runtime reproducible across development, staging, and production. Expose predictions. Run a small HTTP service, such as FastAPI or Flask, for online inference. Run scheduled scripts…

1. GitHub — Use GitHub Actions, GitLab CI, Jenkins, or a similar CI/CD system to run unit tests, data-validation checks, model-quality tests, and load tests before deploying.
2. Prometheus Group — Prometheus can collect metrics, while Grafana can display dashboards and trigger alerts.
3. Apache Airflow

1. MLflow — When companies lack dedicated MLOps platforms (like MLflow, Weights & Biases, or SageMaker), they fall back on standard software engineering practices, leveraging existing DevOps, cloud infrastructure, and logging tools.
2. Weights & Biases — When companies lack dedicated MLOps platforms (like MLflow, Weights & Biases, or SageMaker), they fall back on standard software engineering practices, leveraging existing DevOps, cloud infrastructure, and logging tools.
3. Amazon SageMaker AI — When companies lack dedicated MLOps platforms (like MLflow, Weights & Biases, or SageMaker), they fall back on standard software engineering practices, leveraging existing DevOps, cloud infrastructure, and logging tools.
4. AWS — Orchestration: Containers are deployed using standard orchestration tools like Kubernetes (K8s) or simpler cloud-native container runners (e.g., AWS ECS, Google Cloud Run , or Azure Container Instances) to handle…
5. Google Vertex AI — Orchestration: Containers are deployed using standard orchestration tools like Kubernetes (K8s) or simpler cloud-native container runners (e.g., AWS ECS, Google Cloud Run , or Azure Container Instances) to handle…
6. Prometheus Group — Application Performance Monitoring (APM): Tools like Datadog, New Relic , or Prometheus + Grafana are repurposed to track system-level metrics—CPU/GPU utilization, memory consumption, latency (p99/p50 response times),…
7. Snowflake — These are dumped into a data warehouse (like Snowflake, BigQuery , or PostgreSQL ) for periodic offline analysis.
8. Apache Airflow — Data Quality Checks: Simple cron jobs or data pipelines (using tools like Airflow or basic Python scripts) run periodic queries against recent logs to check for null values, sudden shifts in feature value distributions,…