Jan Mulkens

Microsoft Data Platform & BI Consultant

Jan Mulkens is a Microsoft MVP in AI, the Competence Lead for Microsoft Advanced Analytics at Ordina Belgium and a Microsoft BI Consultant .
In his spare time, he is a speaker at conferences and user groups in Europe and he organizes 2 user groups, a conference in Belgium and one online.
Power BI Days conference (www.powerbidays.com), Belgium Microsoft Advanced Analytics User Group (bit.ly/msaaug), Flemish Power BI User Group (bit.ly/FlemishPowerBI).

Topics

power bi
Machine Learning
SQL Server
SQL Server Integration Services
SQL Server Reporting Services
SQL Server Machine Learning Services
Azure
Azure Data Platform
Business Intelligence
Data Science
Artificial Intelligence
SQL Server Data Tools
SQL Server Report Server

Enabling Citizen Data Science with Microsoft

In times past, Data Science was only for those elite few with a rare combination of skills including advanced knowledge of statistics.
Microsoft has been making sure that everyone can participate in the data revolution by giving people access to predictive API’s, in-database advanced analytics and drag-and-drop predictive experiments. All thanks to SQL Server 2016 and the Cortana Intelligence Suite in Azure.
These advances have given people with less knowledge of statistics and programming the ability to become what Gartner calls citizen data scientists.
Should we be worried about fools-with-tools or should we embrace the democratization of data science as the golden age of data?
Using a combination of theory and demo’s, we explore Microsoft’s solutions to ensure democratization of data science and the possible dangers that lurk below the surface.

Towards Personal Data Science with Power BI

Microsoft states that Power BI is "a suite of business analytics tools to analyze data and share insights." Does this mean Power BI can also be used for more than just building pretty dashboards?
In this session we’ll explore how BI and Data Science are related and how Power BI can not only be used to democratize BI across the enterprise, but also to democratize Data Science!
No previous knowledge is required because after a 5 minute data science jump start, we dive into a demo scenario using Azure Machine Learning and Power BI! Throughout the story accompanying the demo's, it will become clear how you too can get start practicing Personal Data Science. Or as Gartner calls it: Citizen Data Science

At the end of the session, not only will you know the differences, and resemblances, between the BI and data science processes, you'll also be able to follow the general data science process and know in what ways Personal Data Science is an extension of Personal BI (aka: Self Service BI)
Attendees will know where Power BI can fit into the data science process (hint: not only at the end)
Attendees will know how to apply above principles to generate re-usable output in a personal data science process

Driving Power BI automation through monitoring

You finally got the go ahead and now you have a nice and shiny Power BI service or perhaps a Power BI Report Server environment.
Maybe you went all out and even have set up a deployment pipeline to automate deployments.

The one thing you probably haven't don't yet, is setting up that feedback loop.
You're missing metrics. The important metrics that enable you to manage your environment beyond 10 users.

So many things to think about...
Who's using your reports, how often and at what times?
Who actually needs a pro license, who doesn't need it anymore?
You need to plan for maintenance, you need impact assessments for outages or deployment failures.
And you surely need these metrics to show the validity of Power BI within your department or even the enterprise.

In this session you'll learn everything you'd ever want to know about monitoring your Power BI environments and how you too can start monitoring Power BI Report Server or the Power BI service like a pro!

6 Months to 5 minutes, DB & BI Deployments at the Speed of Light

Do you like Scrum as a Cargo Cult, hours of Planning Poker, CAB meetings, unhappy people, processes that are meant to slow down innovation, elaborate change management, and thousands of pages of documentation?
Neither do we.

This is the story of how a team of 6 at one of the largest international banks did everything they could to fight the status quo. From going against management demands and being the ugly duckling team to providing 4 other teams with a platform, services, and training.

You will not learn the typical cool technical tricks in this session.
Instead, you'll learn how we overcame organisational barriers to actually work in an Agile mindset and how we slowly implemented Continuous Delivery in our Database, BI, and Document Management systems while under constant outside pressure.

You'll leave with the knowledge to help your team and yourself!
Prerequisites: Attendees should have worked in IT in any organisation in the past.
Having read the Agile Manifesto AND it's 12 principles is also very helpful.

Applying DevOps practices to Machine Learning

DevOps practices range from continuous integration to continuous delivery from ensuring production is always online or easy to rollback to measuring all the things, from ensuring code and knowledge get shared to having a loosely coupled architecture and of course much more.

How do we actually get that to work with Machine Learning? And why should you care?
First of all, it turns out that any (non-) data scientist can actually be of value in the lifecycle of a machine learning solution. But more importantly, it turns out that by using the knowledge and experience from the community, it's actually possible to transform the way you deliver your machine learning solutions to the end user. It turns out it's even possible to do this much faster than ever before.

In this session, you'll be guided through a solution that uses DevOps practices to help you overcome classic issues with delivering machine learning solutions. Come for the buzzwords, stay because this is what you've always wanted in your organisation!

MLOps, Automated Machine Learning Made Easy

In this session we'll go through what Automated Machine Learning is, how to automate it's deployment and how that in turn simplifies and thus democratizes AI for everyone.

From a short high level overview of all the tools to picking the right tool for the job. The largest part of the session will be a live example of how anyone can actually start using these tools to get a model in production. As we go along, we'll touch on how to avoid the pitfalls that naturally come with the automation of a complex process.

You'll walk away with the knowledge and code to start Automating Machine Learning on your own data and delivering solutions at the speed of light.
Prerequisites: A high level understanding of Machine Learning is certainly helpful but not required.

AutoML, the good, the bad and the why

Data Science has a lot of work that's actually very tedious and most data scientists prefer to avoid that work. Most data engineers and developers who are involved in the Machine Learning process prefer to avoid this as well.
From training the same model multiple times using different features or hyperparameters to preventing over-fitting.

What do you do with work you prefer not to do? You automate it!
Throughout the industry, the smartest people at Microsoft, Google, Facebook and others have been trying to tackle this issue for a while already.
With the results we see now, it's safe to say AutoML is here to stay.

So, people tend to have a lot of questions:
When and why should you use it?
What options does this open for your data analytics team?
When and why should you avoid it?
How do you ensure it has the largest impact possible?
Can you still be compliant certain requirements?

We'll explore these questions while keeping our mind open to all solutions that exist in the wild.

Power BI: All the tools you should be using

Stop using only Power BI Desktop, you're missing out. You really are.

Microsoft, other vendors and of course our community have been creating tools for Power BI since day 1.
In this session, we'll go over the tools that exist out there with demo's left and right. From creating better models, to crafting the world's most dazzling report to managing it all. You'll of course be learning when or why you should be using each tool.

In the end, you'll leave with an understanding of how you can improve the speed and quality of the work you do with Power BI and what tools are available for you at each stage.

Scottish Summit Sessionize Event

February 2020 Glasgow, United Kingdom

Global AI BootCamp Bulgaria 2019 Sessionize Event

December 2019 Sofia, Bulgaria

Azure Saturday Cologne 2019 Sessionize Event

December 2019 Köln, Germany

DATA:Scotland 2019 Sessionize Event

September 2019 Glasgow, United Kingdom

Power Saturday 2019 Sessionize Event

June 2019 Paris, France

EXPERTS LIVE NETHERLANDS 2019 Sessionize Event

June 2019

Intelligent Cloud Conference 2019 Sessionize Event

April 2019 Copenhagen, Denmark

Power BI Gebruikersdag 2019 Sessionize Event

March 2019 Utrecht, The Netherlands

SQLGLA 2018 Sessionize Event

September 2018 Glasgow, United Kingdom

Power Saturday 2018 Sessionize Event

June 2018 Paris, France

Global Azure Bootcamp Lisboa 2018 Sessionize Event

April 2018

Jan Mulkens

Microsoft Data Platform & BI Consultant

Links

Please note that Sessionize is not responsible for the accuracy or validity of the data provided by speakers. If you suspect this profile to be fake or spam, please let us know.