Who should use the Deploy machine learning models workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Development
Train a machine learning model using TensorFlow or Kaggle, then deploy it to production with Seldon Core or Baseten for real-time inference via API endpoints.
Deliverable outcome
Observability into model performance and system health.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
Observability into model performance and system health.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use scikit-learn to a clean, versioned dataset ready for model training. Then, you pass the output to Weights & Biases to a trained model with documented performance metrics and a serialized artifact. Then, you pass the output to MLEM to a portable docker image containing the model and inference runtime. Then, you pass the output to Seldon Core to a live api endpoint serving real-time predictions from the deployed model. Finally, Escher is used to observability into model performance and system health.
Prepare and version the dataset
A clean, versioned dataset ready for model training.
Train and evaluate the model
A trained model with documented performance metrics and a serialized artifact.
Containerize the model with dependencies
A portable Docker image containing the model and inference runtime.
Deploy with Seldon Core or Baseten
A live API endpoint serving real-time predictions from the deployed model.
Set up monitoring and logging (optional)
Observability into model performance and system health.
Collect or select a labeled dataset relevant to your problem. Clean the data, handle missing values, and split into training, validation, and test sets. Use DVC or Git LFS to version the dataset so that experiments are reproducible.
Why scikit-learn: scikit-learn directly provides the core Python ML tools needed for dataset preparation and versioning, including pandas integration and preprocessing utilities.
Build a model architecture using TensorFlow/Keras or a Kaggle notebook. Train on the prepared dataset, tune hyperparameters, and evaluate on the validation set. Save the best model checkpoint and log metrics (accuracy, loss, etc.).
Why Weights & Biases: Weights & Biases directly supports model training, experiment tracking, and inference, matching the needs for training and evaluation with TensorFlow/Keras.
Create a Dockerfile that includes the model artifact, inference code, and required libraries (TensorFlow Serving or custom Python server). Build and tag the image, then push it to a container registry (Docker Hub, ECR, GCR).
Why MLEM: MLEM directly supports model packaging and saving, which is essential for containerizing models with dependencies.
Configure a SeldonDeployment YAML (for Kubernetes) or use Baseten's CLI to deploy the container. Set resource limits, scaling policies, and expose an API endpoint. Verify the endpoint responds correctly with a test request.
Why Seldon Core: Seldon Core is explicitly listed in the menu and directly matches the deployment need with model deployment, monitoring, and explainability.
Integrate Prometheus metrics (request latency, error rate) and structured logging (e.g., ELK stack). Configure alerts for model drift or endpoint downtime. This step is optional for initial deployment but recommended for production.
Why Escher: Escher directly supports monitoring machine learning models, which aligns with setting up monitoring and logging.
§ Before you start
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
§ Related
Ship features faster by delegating architecture, implementation, testing, and deployment to specialized AI coding agents.
Rapidly prototype and deploy a functional application using AI-assisted coding and design systems — from idea to live product in days.
From logic definition to production-ready code with automated testing and deployment — a repeatable pipeline for shipping software features.