Who should use the Build and Deploy an AI Model workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Development
A streamlined workflow to train a baseline machine learning model, build it into a final AI model, evaluate its performance, and deploy it for real-world use.
Deliverable outcome
An automated retraining pipeline that keeps the model current and reliable.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
An automated retraining pipeline that keeps the model current and reliable.
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 clear problem definition and a clean, split dataset ready for modeling. Then, you pass the output to scikit-learn to a working baseline model with documented performance metrics. Then, you pass the output to TensorFlow Hub to a refined model with validated performance exceeding the baseline. Then, you pass the output to scikit-learn to a validated model with documented test performance and error analysis. Then, you pass the output to Red Hat OpenShift AI to a deployed, integrated model serving real-time predictions with monitoring. Finally, Red Hat OpenShift AI is used to an automated retraining pipeline that keeps the model current and reliable.
Define Problem and Collect Data
A clear problem definition and a clean, split dataset ready for modeling.
Train Baseline Model
A working baseline model with documented performance metrics.
Build and Refine Final Model
A refined model with validated performance exceeding the baseline.
Evaluate and Validate Model Performance
A validated model with documented test performance and error analysis.
Deploy and Integrate the Model
A deployed, integrated model serving real-time predictions with monitoring.
Set Up Continuous Retraining Pipeline (Optional)
An automated retraining pipeline that keeps the model current and reliable.
Start by clearly defining the business problem and the target metric (e.g., accuracy, latency). Then gather a labeled dataset that represents the real-world distribution, ensuring it is clean and properly split into training, validation, and test sets.
Why scikit-learn: scikit-learn is a core library for data preprocessing, feature engineering, and initial model exploration, directly supporting the Python/pandas/scikit-learn needs of this step.
Select a simple, fast algorithm (e.g., logistic regression, decision tree) and train it on the training set. Use the validation set to tune hyperparameters minimally, establishing a performance baseline against which more complex models will be compared.
Why scikit-learn: scikit-learn provides the essential algorithms (classification, regression, clustering) needed to train a baseline model, and integrates well with pandas and matplotlib for data handling and visualization.
Iterate on the baseline by experimenting with more advanced algorithms (e.g., XGBoost, neural networks) and feature engineering. Use cross-validation and hyperparameter tuning (grid search or Bayesian optimization) to maximize performance on the validation set, then retrain on the full training set.
Why TensorFlow Hub: TensorFlow Hub offers pre-trained models that can be fine-tuned with TensorFlow/PyTorch, supporting the deep learning and model refinement needs of this step.
Assess the final model on the held-out test set to estimate real-world performance. Analyze confusion matrices, ROC curves, and error distributions. Check for overfitting by comparing train vs. test metrics, and ensure the model meets the success criteria defined in step 1.
Why scikit-learn: scikit-learn offers comprehensive metrics for classification, regression, and clustering evaluation, directly supporting the model validation needs of this step.
Package the trained model (e.g., as a pickle file, ONNX, or TensorFlow SavedModel) and create a lightweight API using a framework like Flask or FastAPI. Containerize with Docker, deploy to a cloud platform (AWS, GCP, Azure), and integrate with the existing application via REST endpoints. Set up monitoring for latency and drift.
Why Red Hat OpenShift AI: Red Hat OpenShift AI provides a full platform for deploying AI models at scale, managing the model lifecycle, and integrating with cloud infrastructure, aligning with the deployment and integration needs.
Automate the retraining process by creating a pipeline that triggers on new data or performance degradation. Use tools like Apache Airflow or Kubeflow to schedule periodic retraining, re-evaluation, and redeployment, ensuring the model stays accurate over time.
Why Red Hat OpenShift AI: Red Hat OpenShift AI manages the AI model lifecycle, including retraining pipelines and deployment, which aligns with the continuous retraining and orchestration needs.
§ 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.
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