Course Code

dataikudss

Duration

21 hours (usually 3 days including breaks)

Requirements

Experience with Python, SQL, and R programming languages
Basic knowledge of data processing with Apache Hadoop and Spark
Comprehension of machine learning concepts and data models
Background in statistical analyses and data science concepts
Experience with visualizing and communicating data

Audience

Engineers
Data Scientists
Data Analysts

Overview

Dataiku Data Science Studio (Dataiku DSS) is a centralized platform for operating and implementing machine learning models in enterprise applications. It also allows its users to collaborate and iterate on various AI and ML approaches using computation abstraction features. Dataiku DSS, along with its supported instances on different cloud services, provides a solution to big data management that is aligned with the goals of the business.

This instructor-led, live training (online or onsite) is aimed at engineers, data scientists, and data analysts who wish to use Dataiku DSS for the development of machine learning pipelines, and leverage AI processes in generating strategic organizational initiatives.

By the end of this training, participants will be able to:

Install and configure Dataiku DSS upon their preferred operating system.
Understand AI/ML concepts and principles that are fundamental to Dataiku features.
Create and implement their own data visualization codes in Dataiku DSS projects.
Deploy ML models and pipelines into production environments built around Dataiku DSS.
Optimize the agility and flexibility of data analysis methods of their enterprise applications.
Utilize Dataiku DSS to secure and manage how data flows through further business systems.

Format of the Course

Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.

Course Customization Options

To request a customized training for this course, please contact us to arrange.

Course Outline

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 v.s. 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 merge folder
Local v.s. non-local managed folders

Constructing a Filesystem Dataset Using Managed Folder Contents

Performing cleanups with a DSS code recipe

Working with Metrics Dataset and Internal Stats Dataset

Implementing the DSS Download Recipe for HTTP Dataset

Relocating SQL Datasets and HDFS Datasets Using DSS

Ordering Datasets in Dataiku DSS

Writer ordering v.s. read-time ordering

Exploring and Preparing Data Visuals for a Dataiku DSS Project

Overview of Dataiku Schemas, Storage Types, and Meanings

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

Working with Dataiku DSS Charts Interface and Types of Visual Aggregations

Utilizing the Interactive Statistics Feature of DSS

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

Overview of Machine Learning with Dataiku DSS

Supervised ML v.s. unsupervised ML
References for DSS ML Algorithms and features handling
Deep Learning with Dataiku DSS

Overview of the Flow Derived from DSS Datasets and Recipes

Transforming Existing Datasets in DSS with Visual Recipes

Utilizing DSS Recipes Based on User-Defined Code

Optimizing Code Exploration and Experimentation with DSS Code Notebooks

Writing Advanced DSS Visualizations and Custom Frontend Features with Webapps

Working with Dataiku DSS Code Reports Feature

Sharing Data Project Elements and Familiarizing with the DSS Dashboard

Designing and Packaging a Dataiku DSS Project as a Reusable Application

Overview of Advanced Methods in Dataiku DSS

Implementing optimized datasets 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

Analyzing and Forecasting Dataiku DSS Time Series

Securing a Project in Dataiku DSS

Managing Project Permissions and Dashboard Authorizations
Implementing Advanced Security Options

Integrating Dataiku DSS with The Cloud

Troubleshooting

Summary and Conclusion

Testimonials

★★★★★

It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.

Jonathan Blease

Course:Artificial Neural Networks, Machine Learning, Deep Thinking

The trainer was so knowledgeable and included areas I was interested in.

Mohamed Salama

Course:Data Mining & Machine Learning with R

I genuinely liked excercises

L M ERICSSON LIMITED

Course:Machine Learning

I liked the lab exercises.

Marcell Lorant - L M ERICSSON LIMITED

Course:Machine Learning

The Jupyter notebook form, in which the training material is available

L M ERICSSON LIMITED

Course:Machine Learning

There were many exercises and interesting topics.

L M ERICSSON LIMITED

Course:Machine Learning

Some great lab exercises analyzed and explained by the trainer in depth (e.g. covariants in linear regression, matching the real function)

L M ERICSSON LIMITED

Course:Machine Learning

It's just great that all material including the exercises is on the same page and then it gets updated on the fly. The solution is revealed at the end. Cool! Also, I do appreciate that Krzysztof took extra effort to understand our problems and suggested us possible techniques.

Attila Nagy - L M ERICSSON LIMITED

Course:Machine Learning

It is showing many methods with pre prepared scripts- very nicely prepared materials & easy to traceback

Kamila Begej - GE Medical Systems Polska Sp. Zoo