Dataiku for Enterprise AI and Machine Learning Training Course

Introduction

Installing and Configuring Dataiku Data Science Studio (DSS)

• System requirements for Dataiku DSS
• Setting up Apache Hadoop and Apache Spark integrations
• Configuring Dataiku DSS with web proxies
• Migrating from other platforms to Dataiku DSS

Overview of Dataiku DSS Features and Architecture

• Core objects and graphs foundational to Dataiku DSS
• What is a recipe in Dataiku DSS?
• Types of datasets supported by Dataiku DSS

Creating a Dataiku DSS Project

Defining Datasets to Connect to Data Resources in Dataiku DSS

• Working with DSS connectors and file formats
• Standard DSS formats vs. Hadoop-specific formats
• Uploading files for a Dataiku DSS project

Overview of the Server Filesystem in Dataiku DSS

Creating and Using Managed Folders

• Dataiku DSS recipe for merging folders
• Local vs. non-local managed folders

Constructing a Filesystem Dataset Using Managed Folder Contents

• Performing cleanups with a DSS code recipe

Working with Metrics Datasets and Internal Stats Datasets

Implementing the DSS Download Recipe for HTTP Datasets

Relocating SQL Datasets and HDFS Datasets Using DSS

Ordering Datasets in Dataiku DSS

• Writer ordering vs. read-time ordering

Exploring and Preparing Data Visuals for a Dataiku DSS Project

Overview of Dataiku Schemas, Storage Types, and Meanings

Performing Data Cleansing, Normalisation, and Enrichment Scripts in Dataiku DSS

Working with the Dataiku DSS Charts Interface and Types of Visual Aggregations

Utilising the Interactive Statistics Feature of DSS

• Univariate analysis vs. bivariate analysis
• Making use of the Principal Component Analysis (PCA) DSS tool

Overview of Machine Learning with Dataiku DSS

• Supervised ML vs. unsupervised ML
• References for DSS ML algorithms and feature handling
• Deep Learning with Dataiku DSS

Overview of the Flow Derived from DSS Datasets and Recipes

Transforming Existing Datasets in DSS with Visual Recipes

Utilising DSS Recipes Based on User-Defined Code

Optimising Code Exploration and Experimentation with DSS Code Notebooks

Writing Advanced DSS Visualisations and Custom Frontend Features with Webapps

Working with the Dataiku DSS Code Reports Feature

Sharing Data Project Elements and Familiarising with the DSS Dashboard

Designing and Packaging a Dataiku DSS Project as a Reusable Application

Overview of Advanced Methods in Dataiku DSS

• Implementing optimised dataset partitioning using DSS
• Executing specific DSS processing parts through computations in Kubernetes containers

Overview of Collaboration and Version Control in Dataiku DSS

Implementing Automation Scenarios, Metrics, and Checks for DSS Project Testing

Deploying and Updating a Project with the DSS Automation Node and Bundles

Working with Real-Time APIs in Dataiku DSS

• Additional APIs and REST APIs in DSS

Analysing and Forecasting Dataiku DSS Time Series

Securing a Project in Dataiku DSS

• Managing Project Permissions and Dashboard Authorisations
• Implementing Advanced Security Options

Integrating Dataiku DSS with The Cloud

Troubleshooting

Summary and Conclusion