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
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
Testimonials
The knowledge of the trainer was very high and the material was well prepared and organised.
Otilia - Gareth Morgan, TCMT
Machine Learning with Python – 2 Days Course
I thought the trainer was very knowledgeable and answered questions with confidence to clarify understanding.
Jenna - Gareth Morgan, TCMT
Machine Learning with Python – 2 Days Course
Bardzo merytoryczne szkolenie, bardzo duża wiedza prowadzącego.
Danuta Haber, Orange Szkolenia Sp. z o.o.
Feature Engineering for Machine Learning Course
Humor prowadzącego.
Danuta Haber, Orange Szkolenia Sp. z o.o.
Feature Engineering for Machine Learning Course
Wiedza i umiejetnosc jej przekazania
Danuta Haber, Orange Szkolenia Sp. z o.o.
Feature Engineering for Machine Learning Course
The details and the presentation style.
Cristian Mititean - Edina Kiss, Accenture Industrial SS
Azure Machine Learning (AML) Course
Interactive, a lot of exercises
Edina Kiss, Accenture Industrial SS
Azure Machine Learning (AML) Course
The Exercises
Khaled Altawallbeh - Edina Kiss, Accenture Industrial SS
Azure Machine Learning (AML) Course
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
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Going through the notebooks, becoming more familiar with Qiskit and the various ways to do things.
Bank of Canada
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Very very competent trainer who know how to adapt to his audience, and to solve problems Interactive presentation
OLEA MEDICAL
MLflow Course
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
MLflow Course
The way of transferring knowledge and the knowledge of the trainer.
Jakub Rękas - Sebastian Pawłowski, Bitcomp Sp. z o.o.
Machine Learning on iOS Course
The explaination
Wei Yang Teo - Ministry of Defence, Singapore
Machine Learning with Python – 4 Days Course
The trainer took the time to answer all our questions.
Ministry of Defence, Singapore
Machine Learning with Python – 4 Days Course
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life™
Kubeflow Course
Working with real industry-leading ML tools, real datasets and being able to consult with a very experienced data scientist.
Zakład Usługowy Hakoman Andrzej Cybulski
Applied AI from Scratch in Python Course
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Applied AI from Scratch in Python Course
The enthusiasm to the topic. The examples he made an he explained it very well. Sympatic. A little to detailed for beginners. For managers, it could be more abstract in fewer days. But it was designed to fit and we had a good alignment in advance.
Benedikt Chiandetti - HDI Deutschland Bancassurance Kundenservice GmbH
Machine Learning Concepts for Entrepreneurs and Managers Course
Convolution filter
Francesco Ferrara - Inpeco SpA
Introduction to Machine Learning Course
Adjusting to our needs
Sumitomo Mitsui Finance and Leasing Company, Limited
Kubeflow Course
The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
Applied AI from Scratch in Python Course
I like that it focuses more on the how-to of the different text summarization methods
Text Summarization with Python Course
There were many exercises and interesting topics.
- L M ERICSSON LIMITED
Machine Learning Course
The Jupyter notebook form, in which the training material is available
- L M ERICSSON LIMITED
Machine Learning Course
I liked the lab exercises.
Marcell Lorant - L M ERICSSON LIMITED
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The trainer was so knowledgeable and included areas I was interested in
Mohamed Salama
Data Mining & Machine Learning with R Course
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.