No-Code AI & ML: From Data to Deployment Without Coding

Explore how age, gender, class, and traveling with family relate to Titanic survival using the dataset. Build a no-code predictive model in Kaggle and Dataiku using the Titanic dataset.

Import datasets in dataiku, identify data types, and perform feature selection and data cleaning to prepare a Titanic dataset for modeling.

Demonstrates building a prediction model with auto ML in Dataiku, using survived as the target in a two-class classification, and training with an 80%/20% split while optimizing ROC AUC.

Explore modeling algorithms on the no-code ml platform by selecting random forest, logistic regression, gradient boosting, and xgboost. Use grid search with five-fold cross-validation, then train and compare results.

This demo covers model information and data preparation, showing linear combinations creating features from five inputs and a gradient boosted trees classifier trained on 712 rows with 100 boosting stages.

Choose the right model by problem type (classification, regression, or clustering) and data size. Balance complexity and interpretability, and use accuracy, precision, recall, and F1 to guide selection.

upload the test data, preprocess to mirror the training file, and run the model to score unseen records with predicted probabilities in the dataiku no-code ml workflow.

Export your trained model code to a Python file or a Jupyter Notebook from the visual ML flow, enabling familiar programmers to modify and run preprocessing, cross‑validation, and modeling steps.

Explore no-code machine learning tools that democratize AI by enabling data preparation, model training, deployment, and monitoring through drag-and-drop interfaces, pre-built algorithms, and templates.

Discover how to use no-code machine learning tools to build, train, test, and deploy models via a graphical user interface, enabling marketing, forecasting, and innovative applications without coding.

Learn how no-code ML platforms like Dataiku streamline importing, cleaning, transforming, and engineering features, then visualize, split data, and validate models for reliable machine learning.

Create a blank project and import data from local csv/xlsx, databases, cloud storage, and no-sql sources, then connect to sources like Twitter, MySQL, MongoDB, and Elasticsearch, all without coding.

Create a geo point from latitude and longitude, perform reverse geocoding to extract city, state, county, and country, then compute distance to a fixed location for model features.

Explore the four machine learning challenges: data quality, model interpretability, computational cost, and ethics, and address imbalanced data with resampling, synthetic data, ensemble methods, and cost-sensitive learning.

Examine model interpretability by revealing how predictions arise, using explainable AI techniques like Shap and Lime, and compare simple models with complex ones for transparency and accountability.

Build a loan default model with AutoML, compare models, and use a model fairness report to assess demographic parity and equalized odds across marital status and employment type.