Who should use the Train 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
A streamlined workflow to prepare data, train models, evaluate performance, and deploy the final model for real-world use.
Deliverable outcome
An up-to-date model that adapts to changing data patterns.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
An up-to-date model that adapts to changing data patterns.
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 Activeloop Deep Lake to a clear problem statement and a raw dataset ready for exploration. Then, you pass the output to Dataiku to a clean, well-understood dataset with no obvious quality issues. Then, you pass the output to Dataiku to a feature matrix and target vector, split into three sets for model development. Then, you pass the output to Optuna to a set of trained models with tuned hyperparameters, ready for final evaluation. Then, you pass the output to Dataiku to a single champion model with validated performance on unseen data. Then, you pass the output to Hugging Face Spaces to a live, monitored model serving predictions in a production environment. Finally, ZenML is used to an up-to-date model that adapts to changing data patterns.
Define problem and collect raw data
A clear problem statement and a raw dataset ready for exploration.
Explore and clean the data
A clean, well-understood dataset with no obvious quality issues.
Engineer features and split data
A feature matrix and target vector, split into three sets for model development.
Train candidate models
A set of trained models with tuned hyperparameters, ready for final evaluation.
Evaluate and select best model
A single champion model with validated performance on unseen data.
Deploy final model to production
A live, monitored model serving predictions in a production environment.
Iterate and retrain (optional)
An up-to-date model that adapts to changing data patterns.
Start by clearly defining the business problem and the target variable you want to predict. Then gather raw data from relevant sources such as databases, APIs, or flat files, ensuring you have enough volume and variety to train a robust model.
Why Activeloop Deep Lake: Activeloop Deep Lake provides multimodal AI data storage with version control, directly supporting raw data collection and cloud storage needs (AWS S3, Google Cloud Storage).
Perform exploratory data analysis (EDA) to understand distributions, missing values, and outliers. Clean the data by handling missing entries, correcting data types, and removing duplicates to ensure quality input for modeling.
Why Dataiku: Dataiku provides data wrangling and cleaning capabilities, aligning with the need to explore and clean data using Python libraries like pandas and matplotlib.
Transform raw data into meaningful features through encoding, scaling, and creation of new variables. Then split the dataset into training, validation, and test sets to enable unbiased evaluation.
Why Dataiku: Dataiku supports data wrangling and cleaning, which includes feature engineering and data splitting, matching the needs for scikit-learn and pandas workflows.
Select a range of candidate algorithms (e.g., linear models, tree-based, neural networks) and train them on the training set. Use the validation set to tune hyperparameters and compare initial performance.
Why Optuna: Optuna specializes in hyperparameter search and optimization, directly supporting the need for hyperparameter tuning libraries like Optuna and GridSearchCV.
Assess all candidate models on the held-out test set using the predefined success metrics. Compare results, check for overfitting, and select the best-performing model for deployment.
Why Dataiku: Dataiku provides model deployment and monitoring, which includes evaluation capabilities that align with using scikit-learn and interpretation tools like SHAP/LIME.
Package the selected model (e.g., as a pickle file or ONNX format) and integrate it into a production environment via an API or batch pipeline. Monitor performance and retrain as needed.
Why Hugging Face Spaces: Hugging Face Spaces allows deploying ML models as web apps with scalable inference, fitting the need for Docker, FastAPI, and cloud deployment.
Periodically collect new data and retrain the model to maintain accuracy. This step is optional but recommended for long-running models in dynamic environments.
Why ZenML: ZenML orchestrates ML pipelines and versions artifacts, aligning with automated pipeline tools like Kubeflow/Airflow and version control with DVC/MLflow.
§ 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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