├── README.md ├── advanced_ml_and_api_approaches ├── DataSynth │ ├── DataRobotDataSynth.config.yaml │ ├── DataRobotDataSynth.py │ ├── Dockerfile │ ├── README.md │ ├── generator.py │ ├── requirements.txt │ └── test.env ├── Dimensionality reduction in DataRobot with t-SNE │ ├── Dimensionality reduction in DataRobot with t-SNE.config.yaml │ ├── Dimensionality reduction in DataRobot with t-SNE.ipynb │ └── test.env ├── Flight Delays - Starter Use Case for New DataRobot Users │ ├── Flight Delays - Starter Use Case for New DataRobot Users.config.yaml │ ├── Flight Delays - Starter Use Case for New DataRobot Users.ipynb │ └── test.env ├── GDL Featurizer │ ├── GDL Featurizer.config.yaml │ ├── GDL Featurizer.ipynb │ ├── custom.py │ ├── model-metadata.yaml │ ├── requirements.txt │ └── test.env ├── Hyperparameter_Optimization │ ├── HyperParam_Opt_Core_Concepts.config.yaml │ ├── HyperParam_Opt_Core_Concepts.ipynb │ ├── README.md │ ├── helpers.py │ ├── requirements.txt │ └── test.env ├── LIME with DataRobot Models │ ├── LIME analysis with DataRobot.config.yaml │ ├── LIME analysis with DataRobot.ipynb │ └── test.env ├── MLFLOW_w_datarobot_experiments │ ├── DR_Demo_Sales_Multiseries_training (1).xlsx │ ├── README.md │ ├── experiment_notebook.config.yaml │ ├── experiment_notebook.ipynb │ ├── orchestration_notebook.ipynb │ ├── requirements.txt │ └── test.env ├── PDP_ICE │ ├── Lending_Club_scoring.csv │ ├── PDP and ICE AV.config.yaml │ ├── PDP and ICE AV.ipynb │ ├── pdp_and_ice.py │ └── test.env ├── README.md ├── Reinforcement learning │ ├── Reinforcement_Learning.config.yaml │ ├── Reinforcement_Learning.ipynb │ └── test.env ├── Robust_Feature_Impact │ ├── Robust_Feature_Impact.config.yaml │ ├── Robust_Feature_Impact.ipynb │ └── test.env ├── Simple_Log_Lister │ ├── README.md │ ├── Simple_Log_Lister.config.yaml │ ├── Simple_Log_Lister.ipynb │ ├── sample.env │ └── test.env ├── Streamlit_template_datarobot_insights │ ├── DR_icon.jpeg │ ├── DR_logo.png │ ├── README.md │ ├── ai_accelerator_modelInsights_streamlit_v1.config.yaml │ ├── ai_accelerator_modelInsights_streamlit_v1.ipynb │ ├── prediction_dataset.csv │ ├── prediction_output.csv │ ├── requirements.txt │ ├── streamlit_app.py │ └── test.env ├── Using Feature Discovery SQL in Spark clusters │ ├── Dockerfile │ ├── README.md │ ├── Using Feature Discovery SQL in other Spark clusters.config.yaml │ ├── Using Feature Discovery SQL in other Spark clusters.ipynb │ ├── apps │ │ ├── DataRobotRunSSSQL.py │ │ └── LC_FD_SQL.sql │ ├── data │ │ └── .empty_file │ ├── docker-compose.yml │ ├── img │ │ ├── FD_SQL.png │ │ └── FD_graph.png │ ├── libs │ │ └── .empty_file │ ├── start-spark.sh │ ├── test.env │ └── utils.py ├── Viz Output │ ├── Viz Output.config.yaml │ ├── Viz Output.ipynb │ └── test.env ├── creating_custom_blueprints │ ├── catboost │ │ ├── catboost_pipeline.py │ │ ├── custom.py │ │ ├── feature_selection.py │ │ ├── model-metadata.yaml │ │ └── requirements.txt │ ├── create_custom_blueprint.config.yaml │ ├── create_custom_blueprint.ipynb │ ├── requirements.txt │ └── test.env ├── custom_leaderboard_metrics │ ├── custom_metrics.config.yaml │ ├── custom_metrics.ipynb │ ├── requirements.txt │ └── test.env ├── customizing_lift_charts │ ├── customizing_lift_charts.config.yaml │ ├── customizing_lift_charts.ipynb │ ├── requirements.txt │ └── test.env ├── data_enrichment_gcp_nlp_api │ ├── GCP_enrich_sentiment.config.yaml │ ├── GCP_enrich_sentiment.ipynb │ ├── churn_dataset.csv │ ├── churn_dataset_enriched.csv │ └── test.env ├── data_enrichment_ready_signal_ts │ ├── DataRobot_RXA.config.yaml │ ├── DataRobot_RXA.ipynb │ └── test.env ├── datarobot-lineage-app │ ├── Dockerfile │ ├── README.md │ ├── app.png │ ├── config.yaml │ ├── create_graph_from_use_case.py │ ├── create_use_case_file.py │ ├── datarobot.png │ ├── default.conf │ ├── dr_lineage_app.gif │ ├── index.html │ ├── script.js │ ├── server.js │ ├── style.css │ └── test.env ├── dr_synth_data │ ├── 3rdparty │ │ └── README.md │ ├── dr_synth_data.config.yaml │ ├── dr_synth_data.ipynb │ └── test.env ├── faster-rcnn-custom-model │ ├── .gitattributes │ ├── FasterR-CNN_training.config.yaml │ ├── FasterR-CNN_training.ipynb │ ├── README.md │ ├── assets │ │ ├── custom.py │ │ ├── model_state_dict.htp │ │ └── requirements.txt │ ├── display_imgs │ │ ├── mAP_sample.png │ │ ├── scratches_261.jpg │ │ └── scratches_261_scored.png │ ├── object_detection_custom_model.ipynb │ ├── test.env │ └── training │ │ └── requirements.txt ├── feature_reduction_with_fire │ ├── feature_reduction_with_fire.config.yaml │ ├── feature_reduction_with_fire.ipynb │ ├── fire.py │ ├── requirements.txt │ └── test.env ├── fine_tuning_with_eureqa │ ├── fine_tuning_with_eureqa.config.yaml │ ├── fine_tuning_with_eureqa.ipynb │ ├── requirements.txt │ └── test.env ├── graph_financial_fraud_classification │ ├── Graph_Financial_Fraud_Classification.config.yaml │ ├── Graph_Financial_Fraud_Classification.ipynb │ ├── custom_task │ │ ├── GIN_model.py │ │ ├── custom.py │ │ └── requirements.txt │ ├── custom_task_deployed.png │ ├── python311_gin │ │ ├── Dockerfile │ │ ├── README.md │ │ ├── __init__.py │ │ ├── env_info.json │ │ ├── fit.sh │ │ ├── requirements.txt │ │ └── start_server.sh │ ├── requirements.txt │ └── test.env ├── image_dataprep_classification_databricks │ ├── Image Data Preparation.config.yaml │ ├── Image Data Preparation.ipynb │ └── test.env ├── mlops_smart_audit │ ├── README.md │ ├── app.config.yaml │ ├── app.py │ ├── capability_requirements.json │ ├── metadata.yaml │ ├── requirements.txt │ ├── screenshot_mlops_smar_audit.png │ ├── start-app.sh │ └── test.env ├── model-factory-with-python-native-multithreading │ ├── Model Factory with Python Multithreading.config.yaml │ ├── Model Factory with Python Multithreading.ipynb │ └── test.env ├── model_migration_across_dr_instances │ ├── Model_Migration_Example.config.yaml │ ├── Model_Migration_Example.ipynb │ ├── requirements.txt │ └── test.env ├── multi_model_analysis │ ├── Multi-Model Analysis.config.yaml │ ├── Multi-Model Analysis.ipynb │ └── test.env ├── multi_objective_optimization │ ├── Multi-objective Optimization Guide.pdf │ ├── README.md │ ├── multi-objective-optimization-files │ │ ├── .streamlit │ │ │ └── config.toml │ │ ├── dr.png │ │ ├── drops.py │ │ ├── locales │ │ │ ├── en │ │ │ │ └── LC_MESSAGES │ │ │ │ │ ├── base.mo │ │ │ │ │ └── base.po │ │ │ └── ja │ │ │ │ └── LC_MESSAGES │ │ │ │ ├── base.mo │ │ │ │ └── base.po │ │ ├── requirements.txt │ │ ├── start-app.sh │ │ ├── steel_strength_en.csv │ │ ├── steel_strength_jp.csv │ │ └── streamlit_app_dr.py │ ├── requirements.txt │ ├── run_multi_objective_optimization.config.yaml │ ├── run_multi_objective_optimization.ipynb │ ├── sample.env │ └── test.env ├── parameter_tuning_with_hyperopt │ ├── Hyperparameter tuning with hyperopt.config.yaml │ ├── Hyperparameter tuning with hyperopt.ipynb │ ├── helpers.py │ ├── requirements.txt │ └── test.env ├── prediction_intervals_via_conformal_inference │ ├── prediction_intervals_via_conformal_inference.config.yaml │ ├── prediction_intervals_via_conformal_inference.ipynb │ └── test.env └── stat_test_airflow │ ├── artifact_test.tar.gz │ ├── stat_test_airflow.config.yaml │ ├── stat_test_airflow.ipynb │ ├── storage │ ├── airflow │ │ ├── dags │ │ │ ├── datarobot_kv_helper.py │ │ │ ├── dr_stat_tests.py │ │ │ └── nodes.py │ │ ├── requirements.txt │ │ └── t-test │ │ │ ├── custom.py │ │ │ └── model-metadata.yaml │ ├── data │ │ └── 10K_LC_predictions.csv │ ├── docs │ │ ├── example_doc.docx │ │ └── template.json │ └── images │ │ ├── airflow.png │ │ ├── autocorrelation.png │ │ ├── cooks.png │ │ └── custom_task.png │ └── test.env ├── ecosystem_integration_templates ├── AWS_Athena_template │ ├── AWS_Athena_End_to_End.config.yaml │ ├── AWS_Athena_End_to_End.ipynb │ └── test.env ├── AWS_S3_template │ ├── Amazon_S3_End_to_End.config.yaml │ ├── Amazon_S3_End_to_End.ipynb │ └── test.env ├── AWS_monitor_sagemaker_model_in_DataRobot │ ├── AWS_SageMaker_DataRobot_MLOps.config.yaml │ ├── AWS_SageMaker_DataRobot_MLOps.ipynb │ ├── container │ │ ├── Dockerfile │ │ ├── ReadMe.md │ │ ├── build_and_push.sh │ │ ├── decision_trees │ │ │ ├── nginx.conf │ │ │ ├── predictor.py │ │ │ ├── serve │ │ │ ├── train │ │ │ └── wsgi.py │ │ └── local_test │ │ │ ├── predict.sh │ │ │ ├── serve_local.sh │ │ │ ├── test_dir │ │ │ ├── input │ │ │ │ └── config │ │ │ │ │ ├── hyperparameters.json │ │ │ │ │ └── resourceConfig.json │ │ │ └── model │ │ │ │ └── decision-tree-model.pkl │ │ │ └── train_local.sh │ ├── readme.md │ └── test.env ├── AWS_sagemaker_deployment │ ├── .gitignore │ ├── README.md │ ├── dr_model_sagemaker.config.yaml │ ├── dr_model_sagemaker.ipynb │ ├── images │ │ └── DR and AWS Better Together.svg │ ├── scoring_data │ │ ├── 10K_Lending_Club_Loans_scoring.csv │ │ └── 1_row_Lending_Club_Loans_scoring.csv │ ├── test.env │ └── training_data │ │ └── 10K_Lending_Club_Loans.csv ├── Azure_template │ ├── Azure_End_to_End.config.yaml │ ├── Azure_End_to_End.ipynb │ └── test.env ├── Custom Model End-to-End With Compliance Docs │ ├── Custom Model End-to-End With Compliance Docs.config.yaml │ ├── Custom Model End-to-End With Compliance Docs.ipynb │ └── test.env ├── DataRobot-GraphQL │ ├── README.md │ ├── img │ │ └── example-graphql-result.png │ ├── package.json │ └── packages │ │ └── datarobot-source │ │ ├── .meshrc.yaml │ │ └── package.json ├── Databricks & Datarobot - Large Scale Forecasting │ ├── Databricks & Datarobot - Large Scale Forecasting.config.yaml │ ├── Databricks & Datarobot - Large Scale Forecasting.ipynb │ └── test.env ├── Databricks_template │ ├── Databricks_End_To_End.config.yaml │ ├── Databricks_End_To_End.ipynb │ └── test.env ├── GCP_template │ ├── GCP DataRobot End To End.config.yaml │ ├── GCP DataRobot End To End.ipynb │ └── test.env ├── README.md ├── SAP_template │ ├── SAP_End_to_End.config.yaml │ ├── SAP_End_to_End.ipynb │ └── test.env ├── Snowflake_snowpark_template │ ├── Native integration DataRobot and Snowflake Snowpark-Maximizing the Data Cloud.config.yaml │ ├── Native integration DataRobot and Snowflake Snowpark-Maximizing the Data Cloud.ipynb │ └── test.env ├── Snowflake_template │ ├── DR_and_Snowflake_logo.png │ ├── Snowflake - End-to-end Ecommerce Churn.config.yaml │ ├── Snowflake - End-to-end Ecommerce Churn.ipynb │ ├── datasets.yaml │ ├── requirements.txt │ ├── test.env │ └── utils.py ├── scoring-code-as-microservice-w_docker │ ├── Application.config.yaml │ ├── README.md │ ├── micronaut-scoring-code │ │ ├── pom.xml │ │ └── src │ │ │ ├── main │ │ │ └── java │ │ │ │ └── com │ │ │ │ └── datarobot │ │ │ │ └── micronaut │ │ │ │ ├── Application.java │ │ │ │ ├── Predictions.java │ │ │ │ └── ScoringController.java │ │ │ └── test │ │ │ └── java │ │ │ └── com │ │ │ └── datarobot │ │ │ └── micronaut │ │ │ └── ScoringControllerTest.java │ └── test.env └── teams_datarobot │ ├── README.md │ ├── app.py │ ├── bot.py │ ├── card_template.json │ ├── config.py │ ├── create_llm_deployment.config.yaml │ ├── create_llm_deployment.ipynb │ ├── requirements.txt │ ├── test.env │ └── utilities.py ├── generative_ai ├── AI_data_prep_assistant │ ├── README.md │ ├── Step 1 - Upload 1 or more files.png │ ├── Step 2 - Review a sample of the data while checks process.png │ ├── Step 3 - Review the detected quality issues.png │ ├── Step 4 - Decided which fixes to implement.png │ ├── Step 5 - Optionally review the generated code and download data.png │ ├── app.config.yaml │ ├── assets │ │ ├── DataRobot_black.svg │ │ ├── DataRobot_white.svg │ │ └── datarobot_favicon.png │ ├── backend │ │ ├── __pycache__ │ │ │ ├── data_preparation.cpython-310.pyc │ │ │ └── data_quality_checks.cpython-310.pyc │ │ ├── data_preparation.py │ │ └── data_quality_checks.py │ ├── requirements.txt │ ├── start-app.sh │ ├── streamlit_app.py │ └── test.env ├── Finetuned_Llama │ ├── Finetuned Llama 2 on Google GCP.config.yaml │ ├── Finetuned Llama 2 on Google GCP.ipynb │ └── test.env ├── JITR-bot │ ├── JITR-bot.config.yaml │ ├── JITR-bot.ipynb │ └── test.env ├── LLM Multimodal PDF RAG │ ├── LLM Multimodal PDF RAG.config.yaml │ ├── LLM Multimodal PDF RAG.ipynb │ ├── README.md │ ├── image │ │ ├── datarobot-playground-chat.png │ │ ├── image.png │ │ ├── markdown.png │ │ ├── pdf.png │ │ └── vectordb.png │ └── test.env ├── LLM_custom_inference_model_template │ ├── LLM_custom_inference_model_template.config.yaml │ ├── README.md │ ├── storage │ │ ├── _images │ │ │ ├── img1.png │ │ │ ├── img2.png │ │ │ └── img3.png │ │ ├── custom_model_prompts_example.csv │ │ └── templates │ │ │ ├── NousResearch:Nous-Capybara-7B-V1p9 │ │ │ ├── custom.py │ │ │ ├── model-metadata.yaml │ │ │ └── requirements.txt │ │ │ ├── anthropic-claude │ │ │ ├── README.md │ │ │ ├── custom.py │ │ │ ├── model-metadata.yaml │ │ │ ├── requirements.txt │ │ │ └── test_server.sh │ │ │ ├── cohere-command-r-plus │ │ │ ├── custom.py │ │ │ ├── model-metadata.yaml │ │ │ └── requirements.txt │ │ │ ├── cohere-command-r │ │ │ ├── custom.py │ │ │ ├── model-metadata.yaml │ │ │ └── requirements.txt │ │ │ ├── deepseek-ai:deepseek-coder-33b-instruct │ │ │ ├── custom.py │ │ │ ├── model-metadata.yaml │ │ │ └── requirements.txt │ │ │ ├── google-gemini │ │ │ ├── README.md │ │ │ ├── custom.py │ │ │ ├── model-metadata.yaml │ │ │ ├── requirements.txt │ │ │ └── test_server.sh │ │ │ ├── meta-llama:Llama-2-13b-chat-hf │ │ │ ├── custom.py │ │ │ ├── model-metadata.yaml │ │ │ └── requirements.txt │ │ │ ├── mistralai:Mixtral-8x7B-Instruct-v0.1 │ │ │ ├── custom.py │ │ │ ├── model-metadata.yaml │ │ │ └── requirements.txt │ │ │ ├── openai │ │ │ ├── README.md │ │ │ ├── custom.py │ │ │ ├── model-metadata.yaml │ │ │ ├── requirements.txt │ │ │ └── test_server.sh │ │ │ ├── workers-ai-gemma-7b-it │ │ │ ├── custom.py │ │ │ ├── model-metadata.yaml │ │ │ └── requirements.txt │ │ │ └── xai_grok │ │ │ ├── README.md │ │ │ ├── custom.py │ │ │ ├── model-metadata.yaml │ │ │ └── requirements.txt │ └── test.env ├── Medical Research Agent │ ├── Medical Research Conversational Agent.config.yaml │ ├── Medical Research Conversational Agent.ipynb │ └── test.env ├── Mistral 7B on Google GCP │ ├── Mistral 7B on Google GCP.config.yaml │ ├── Mistral 7B on Google GCP.ipynb │ └── test.env ├── Optimizing Customer Support workflows with Gen AI │ ├── Optimizing Customer Support workflows with Gen AI.config.yaml │ ├── Optimizing Customer Support workflows with Gen AI.ipynb │ ├── test.env │ └── utilities.py ├── Product Innovation GenAI │ ├── Product Innovation using Generative AI and DataRobot Auto ML.config.yaml │ ├── Product Innovation using Generative AI and DataRobot Auto ML.ipynb │ └── test.env ├── README.md ├── Using Custom Metrics to effectively monitor Generative AI │ ├── Using Custom Metrics to effectively monitor Generative AI.config.yaml │ ├── Using Custom Metrics to effectively monitor Generative AI.ipynb │ ├── storage │ │ └── utilities.py │ └── test.env ├── XoT Evaluation │ ├── Pulumi.yaml │ ├── README.md │ ├── XoT Evaluations_vdb.config.yaml │ ├── XoT Evaluations_vdb.ipynb │ ├── drops.py │ ├── output │ │ └── .gitkeep │ ├── requirements.txt │ ├── system_prompt_chain_of_noting.md │ ├── system_prompt_chain_of_thought.md │ ├── system_prompt_ratt.md │ ├── system_prompt_tree_of_thought.md │ └── test.env ├── customer_communication_datarobot_gen_ai │ ├── effective_customer_communication_datarobot_gen_ai.config.yaml │ ├── effective_customer_communication_datarobot_gen_ai.ipynb │ └── test.env ├── external_monitoring │ ├── README.md │ ├── anthropic_claude_observability_starter.config.yaml │ ├── anthropic_claude_observability_starter.ipynb │ ├── bedrock_titan_observability_starter.ipynb │ ├── gpt_observability_starter.ipynb │ ├── huggingface_observability_starter.ipynb │ ├── llmchain_observability.ipynb │ ├── palm2_observability_starter.ipynb │ ├── test.env │ └── utilities.py ├── fine-tuning-in-codespaces │ ├── Fine-tuning in DataRobot Codespaces.config.yaml │ ├── Fine-tuning in DataRobot Codespaces.ipynb │ ├── custom-model │ │ ├── custom.py │ │ ├── engine_config.json │ │ └── model-metadata.yaml │ ├── requirements.txt │ └── test.env ├── gemini-app │ ├── Application.config.yaml │ ├── README.md │ ├── Screenshot-1.png │ ├── Screenshot-2.png │ ├── Screenshot-3.png │ ├── Screenshot-4.png │ ├── app │ │ ├── Chat.py │ │ ├── datarobot_logo_icon.png │ │ ├── helpers.py │ │ ├── pages │ │ │ └── 1_Chat_history.py │ │ └── start-app.sh │ ├── multimodal_model │ │ └── custom.py │ ├── test.env │ └── text_model │ │ └── custom.py ├── playground │ ├── DataRobot GenAI Playground Accelerator.config.yaml │ ├── DataRobot GenAI Playground Accelerator.ipynb │ └── test.env ├── rssfeed │ ├── README.md │ ├── rssfeed.config.yaml │ ├── rssfeed.ipynb │ └── test.env ├── smart_cluster_naming │ ├── clusters.jpg │ ├── settings.yaml │ ├── smart_cluster_labeling.config.yaml │ ├── smart_cluster_labeling.ipynb │ └── test.env ├── vectorstore-chunk-visualization │ ├── Application.config.yaml │ ├── README.md │ ├── dr.png │ ├── image │ │ ├── add_app_source.png │ │ ├── add_credential.png │ │ ├── build_env.png │ │ ├── build_source.png │ │ ├── chunk_summary.png │ │ ├── chunk_summary_text.png │ │ ├── cluster_number.png │ │ ├── cluster_summary.png │ │ ├── cluster_summary_text.png │ │ ├── exsited_vdb.png │ │ ├── readme.md │ │ ├── runtime_parameters.png │ │ └── upload_document.png │ ├── metadata.yaml │ ├── requirements.txt │ ├── start-app.sh │ ├── streamlit_app.py │ └── test.env ├── voice_chatbot │ ├── Application.config.yaml │ ├── README.md │ ├── analysis.png │ ├── chat_input.png │ ├── requirements.txt │ ├── select_bot.png │ ├── test.env │ ├── voice_chatbot.py │ └── voice_playback.png └── zero_shot_LMM_error_analysis_NLP │ ├── Zero Shot Text Classification for Error Analysis.config.yaml │ ├── Zero Shot Text Classification for Error Analysis.ipynb │ └── test.env └── use_cases_and_horizontal_approaches ├── Automated_Feature_Discovery_template_ML_pipeline ├── End-to-end Automated Feature Discovery Production Workflow.config.yaml ├── End-to-end Automated Feature Discovery Production Workflow.ipynb ├── FD_download.csv ├── README.md ├── datasets.yaml ├── img │ ├── FD_Config.png │ └── lineage_complex.png ├── predicted.csv ├── requirements.txt ├── test.env └── utils.py ├── Causal_AI ├── Causal_AI.config.yaml ├── Causal_AI.ipynb ├── storage │ └── diabetes_subset.csv └── test.env ├── Conjoint Analysis ├── Choice-based-conjoint.config.yaml ├── Choice-based-conjoint.ipynb └── test.env ├── Demand_forecasting1_end_to_end ├── End_to_end_demand_forecasting.config.yaml ├── End_to_end_demand_forecasting.ipynb ├── datasets.yaml ├── dr_utils.py └── test.env ├── Demand_forecasting2_cold_start ├── End_to_end_demand_forecasting_cold_start.config.yaml ├── End_to_end_demand_forecasting_cold_start.ipynb ├── cold_start.png ├── datasets.yaml ├── dr_utils.py └── test.env ├── Demand_forecasting3_retraining ├── End_to_end_demand_forecasting_retraining.config.yaml ├── End_to_end_demand_forecasting_retraining.ipynb ├── datasets.yaml ├── dr_utils.py ├── images │ ├── accuracy.png │ ├── auto_feedback.png │ ├── data_drift.png │ ├── model_action.png │ ├── model_selection.png │ ├── monitoring.png │ ├── reatraining_data.png │ └── retrain_add.png └── test.env ├── Demand_forecasting4_what_if_app ├── README.md ├── app.png ├── app_utils.py ├── config │ └── config.toml ├── demand_forecasting_app.config.yaml ├── demand_forecasting_app.py ├── dr_logo.jpg ├── predictions │ └── .gitkeep ├── requirements.txt └── test.env ├── Ecommerce_recommendation_engine ├── Recommendation Engine.config.yaml ├── Recommendation Engine.ipynb └── test.env ├── FP&A ├── FP&A.config.yaml ├── FP&A.ipynb ├── storage │ └── sales.csv └── test.env ├── Feature Engineering For Molecular SMILES ├── Feature_Engineering_For_Molecular_SMILES.config.yaml ├── Feature_Engineering_For_Molecular_SMILES.ipynb ├── README.md └── test.env ├── Prediction of Model Performance Degradation and Service Failure ├── Prediction of Model Performance Degradation and Service Failure.config.yaml ├── Prediction of Model Performance Degradation and Service Failure.ipynb └── test.env ├── README.md ├── Retail_Industry_Predicting_Factory_Orders_New_Products ├── Retail Industry - Predicting Factory Order Quantities for New Products.config.yaml ├── Retail Industry - Predicting Factory Order Quantities for New Products.ipynb └── test.env ├── Self_join_technique_for_panel_data ├── self-joins-panel-data.config.yaml ├── self-joins-panel-data.ipynb └── test.env ├── Speech Recognition ├── Speech Recognition.config.yaml ├── Speech Recognition.ipynb └── test.env ├── VisualAI_for_geospatial ├── README.md ├── Visual AI for geospatial data.config.yaml ├── Visual AI for geospatial data.ipynb ├── storage │ └── visualAImaps.py └── test.env ├── Whisper Speech Recognition Deployment ├── Whisper Speech Recognition Deployment.config.yaml ├── Whisper Speech Recognition Deployment.ipynb └── test.env ├── anti-money-laundering ├── Anti-Money Laundering (AML) Alert Scoring.config.yaml ├── Anti-Money Laundering (AML) Alert Scoring.ipynb └── test.env ├── audio_and_sensors-gramian_angular_fields_for_high_freq_data_to_images ├── high_frequency_data_classification_using_gramian_angular_fields.config.yaml ├── high_frequency_data_classification_using_gramian_angular_fields.ipynb ├── signal_to_gaf.webp └── test.env ├── churn_problem_framing_feature_eng ├── Churn_Before_Modelling.config.yaml ├── Churn_Before_Modelling.html ├── Churn_Before_Modelling.ipynb ├── Churn_Before_Modelling_files │ └── libs │ │ ├── bootstrap │ │ ├── bootstrap-icons.css │ │ ├── bootstrap-icons.woff │ │ ├── bootstrap.min.css │ │ └── bootstrap.min.js │ │ ├── clipboard │ │ └── clipboard.min.js │ │ └── quarto-html │ │ ├── anchor.min.js │ │ ├── popper.min.js │ │ ├── quarto-syntax-highlighting.css │ │ ├── quarto.js │ │ ├── tippy.css │ │ └── tippy.umd.min.js ├── img │ ├── max_sql_output.png │ └── safer.png └── test.env ├── data_annotator_app ├── app.py ├── data_annotator_app.config.yaml ├── data_annotator_app.ipynb ├── docs │ ├── avocado.gif │ └── fruits.gif ├── requirements.txt ├── static │ └── style.css ├── templates │ └── index.html ├── test.env └── utils │ └── data_manager.py ├── datarobot-neo4j-knowledge-graph-for-fraud-detection ├── 01_create_neo4j_db.config.yaml ├── 01_create_neo4j_db.ipynb ├── 02_fraud_detection_dr_project_and_update_neo4j.config.yaml ├── 02_fraud_detection_dr_project_and_update_neo4j.ipynb ├── FraudGraphFeatureExtractor.py ├── README.md ├── config.yaml ├── datarobot_kg_neo4j.png └── test.env ├── drx_python_package_overview ├── drx-overview.config.yaml ├── drx-overview.ipynb └── test.env ├── healthcare_appointment_no_show_prediction ├── no_show.config.yaml ├── no_show.ipynb └── test.env ├── high_frequency_data_classification_using_spectrograms_n_numerics ├── create_features.py ├── helpers.py ├── high_frequency_classification_spectrograms_n_numerics.config.yaml ├── high_frequency_classification_spectrograms_n_numerics.ipynb ├── imgs │ ├── .gitkeep │ └── spectrogram_audioform_sample.png ├── requirements.txt └── test.env ├── insurance_pricing ├── Code first Incurred Claims - Insurance pricing.config.yaml ├── Code first Incurred Claims - Insurance pricing.ipynb └── test.env ├── marketing_mix_modeling ├── helpers.py ├── marketing_attribution_w_datarobot.config.yaml ├── marketing_attribution_w_datarobot.ipynb └── test.env ├── marketing_uplift_modeling ├── test.env ├── uplift_modeling.config.yaml └── uplift_modeling.ipynb ├── model_factory_selfjoin_fantasy_baseball ├── fantasy_baseball_predictions_model_factory.config.yaml ├── fantasy_baseball_predictions_model_factory.ipynb └── test.env ├── object_detection_on_video ├── README.md ├── app.py ├── glasses.png └── no-glasses.png ├── time_series_hierarchical_reconciliation ├── test.env ├── time_series_hierarchical_reconciliation.config.yaml └── time_series_hierarchical_reconciliation.ipynb ├── trading_volume_profile_curve_model_factory ├── Create_Deployments.config.yaml ├── 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