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

Define the machine learning problem through context and the task, then collect and manage internal and external data with quality, privacy, governance, and version control for modeling.

Clean, standardize, and transform data for machine learning, addressing missing values and outliers. Apply feature engineering, encoding, normalization, principal component analysis, and stratified sampling to improve model performance.

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.

Explore Kaggle's dataset workflow to locate the Titanic machine learning from disaster dataset, download training and test data, and build a no-code model predicting survived with Dataiku.

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

Analyze missing values, decide to fill age with the median, remove a few rows, and track each preprocessing step in Dataiku.

Apply normalization to the age feature to map values to a 0–1 range using (value minus min) over (max minus min), preventing scale bias and ensuring equal feature importance.

Explore standardization, or z-score scaling, which centers data around zero and scales by the standard deviation using the mean.

Explore the data flow in a no-code ml workflow, detailing preprocessing, removing columns, imputing ages from Kaggle, and normalizing data to prepare train-ready data for model building.

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.

Explore no-code ml with Dataiku in a model training demo. Compare four models and evaluate with ROC, accuracy, and F1, then explore what-if explanations to understand feature impact.

Explore model explainability in no-code ml with dataiku, detailing a 100-tree gradient boosted model, a three-level decision tree, and shap-based feature importance for gender, age, fare, and class.

Explore how a confusion matrix evaluates classification models, detailing true positives and negatives, false positives and negatives, and connect accuracy, precision, recall, and F1 score with cost matrix analysis.

Analyze decision charts showing precision, recall, F1 score, and cost matrix gain cutoffs, and learn how gradient boosted trees generate survival probabilities to set thresholds and compare with lift charts.

Explore lift charts to assess a model's ability to predict survivors, comparing the actual blue curve to the random model and ideal orange lines, and recognize meaningful value addition.

ROC vs PR curves show how good a model is. ROC measures true positive rate and false positive rate, while PR emphasizes precision and recall in imbalanced data.

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.

Explore feature engineering to turn raw data into useful inputs via feature extraction, aggregation, interaction features, and feature construction, then select important features with filter, wrapper, and embedded methods.

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.

Master training and validation through k-fold cross validation, hyperparameter tuning, and regularization to prevent overfitting and boost generalization in no-code ml projects.

Package and containerize your trained model with docker to deploy reliably across cloud or on-premises environments, then choose batch or real-time serving and enable CI/CD.

Deploy the gradient boosted trees model in Dataiku, export to a Jupyter notebook if needed, create an active version, and compare versions while reviewing 80/20 split and k-fold validation.

Apply the deployed model to a preprocessed train data set, generate predictions and class probabilities, save a train prepared score file, and validate performance on a separate test data set.

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.

Monitor and maintain deployed machine learning models by tracking KPIs, detecting data and concept drift, and applying automated retraining, version control, and feedback loops to stay accurate and reliable.

Explore how Dataiku API service exposes machine learning models and data workflows as rest APIs, enabling real-time predictions and seamless integration with web, mobile, and enterprise systems.

Explore exposing machine learning models as RESTful APIs with Dataiku, creating endpoints for real-time predictions and testing them via API requests.

Deploy gradient boosting trees and random forest models in the no-code ml workflow, then compare their performance using ROC, accuracy, precision, recall, F1, and feature importance.

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.

No-code ml empowers nontechnical teams to build, test, and deploy models quickly, fostering collaboration; it also clarifies when coding is necessary and highlights key limitations.

Explore no-code platforms to import, clean, and transform data, select algorithms, train models, and visualize results with an intuitive graphical interface, plus AutoML for easy deployment.

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.

Showcase no-code data pre-processing by extracting geo IP information from IP addresses, including country, city, region, latitude, longitude, time zone, and postal code.

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.

Import a data set for sentiment analysis, split into meaningful columns, clean and normalize text, remove stopwords, apply stemming, convert scores to numbers, and prepare data for model building.

Build and deploy a sentiment analysis model using no-code AutoML, from pre-processed text to training with random forest or logistic regression, deploying, and evaluating with precision, recall, and AUC.

Choose the right algorithm (classification, regression, clustering) and select predictive features with a no-code platform. Train and deploy via apis, then monitor performance and retrain as data evolves.

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.

Optimize algorithms to lower computational cost and leverage cloud services for scalable training. Ensure fairness, transparency, and privacy through bias mitigation, explainable AI, and data protection.

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.

Explore how foundation models and large language models predict the next word from vast text data, using tokens and a context window, and why outputs are drafts to be checked.

Ground your generative AI on your own data using retrieval augmented generation (RAG) to improve accuracy and tie answers to source material from your documents.

AI agents turn goals into executable steps, using tools to perform actions, check progress, and decide next moves, with a human in the loop for important outcomes.

Learn the limits and risks of generative artificial intelligence, including hallucinations. Adopt habits for responsible use: verify facts, protect private data with approved tools, and keep a human in charge.