Online Events

Due to time zones, events presented by American speakers will be spread over more days, and will take place in the afternoon from 2 pm to 6 pm Italian time

Data Virtualization: Technology and Use Cases

ONLINE LIVE STREAMING

Nov 29 - Nov 30, 2021

By: Rick van der Lans

Data Mesh, Data Fabric

ONLINE LIVE STREAMING

Dec 01 - Dec 02, 2021

By: Barry Devlin

Building a Business-Driven Roadmap for Modern Cloud Data Architecture

ONLINE LIVE STREAMING

Dec 03, 2021

By: John O'Brien

Real-Time Big Data Systems with Spark Streaming and Kafka

ONLINE LIVE STREAMING

Dec 06 - Dec 07, 2021

By: Jesse Anderson

Enterprise Data Governance & Master Data Management

ONLINE LIVE STREAMING

Dec 09 - Dec 10, 2021

By: Mike Ferguson

Understanding Enterprise Architecture

ONLINE LIVE STREAMING

Dec 13 - Dec 16, 2021

By: Mike Rosen

Creating Re-Usable Data Products for Analytics

ONLINE LIVE STREAMING

Mar 28 - Mar 29, 2022

By: Mike Ferguson

Incorporating Big Data, Hadoop and NoSQL in Data Warehouse and Business Intelligence Systems

ONLINE LIVE STREAMING

Apr 04 - Apr 05, 2022

By: Rick van der Lans

Data Minimization

ONLINE LIVE STREAMING

Apr 06, 2022

By: Rick van der Lans

Free article of the month

Mike Ferguson
November 2021

Upcoming events by this speaker:

Data Science Workbenches and Machine Learning Automation

In the last decade the rise of data science has been nothing short of spectacular. Today, almost every business has placed a priority on data and analytics in their determination to become data driven. Many companies have undergone what could only be described as a frenzy of activity in this area with different business units buying many different technologies to start developing machine learning models. However, with so many advances in the field of machine learning and data science, we have seen technologies in this area leapfrog each other at a very rapid rate. The result is that many companies now have a broad range of tools and libraries of algorithms scattered around their organisation. For example, some teams may be using Jupyter notebooks and Spark MLlib with Python. Others may be using Tensorflow and Keras. R and R-Studio are also popular as are drop data mining tools from vendors like Knime, SAS, Tibco and Dataiku.

                                                                                                                           …continue to read

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