July 30
The main conference day offers 12 sessions, subdivided into two parallel tracks, focussing on topics like AI, Data Driven Business, Algorithm and much more. And not only that – various surprises await you throughout the day. So it’s worth booking July 29-30 in your calendar, so you won’t miss anything exciting!
Whether you’re working from home or in the office, you decide from where you would like to take part.
Save on travel and hotel costs, as well as what matters most: your time!
Our seasoned and trusted ML Con speakers are highly experienced with the learning opportunities of online conferences and workshops.
On the main conference day, you can choose from 2 parallel sessions and switch between them at any time.
On the workshop day you can expect live coding and practical exercises on selected topics that cover state of the art technologies.
You will follow the speaker’s presentation via video stream and will be guided through the learning content.
All sessions on the main conference day will be recorded and made available to you after the conference is over.
Online workshop participants will also be provided with a recording so they can follow up on the content.
Interaction is a key focus of our online workshops!
With special Q&A sessions, a chat function, and the possibility for audio/video communication, individual questions can be taken into account and the pace of the workshop can be adjusted accordingly.
Virtual Get-Together – an online meeting with our experts in three virtual rooms on predefined topics.
en
Booking note: Data Science Workshop
en
With language assistants, automatic translators and new record-breaking language models appearing every week, it seems like anything is possible, doesn't it?
We have been using Natural Language Processing (NLP) techniques for many years in more than 60 projects in the automotive and insurance sectors for the quality assurance of software. Examples are the automatic checking of requirements, test generation from user stories, and automated traceability analyses.
This results in a somewhat more differentiated picture.
In this presentation we will show what is state of the art, what is not yet possible in practice, and what (in our opinion) will never work. With this we want to show the possibilities of text analysis without falling into unrealistic expectations or even buzzwords.
en
At the end of the main conference days, we will summarize the sessions and address the most important issues that have emerged during the last days.
This will be followed by a raffle for the chance to win some great prizes.
en
Don’t miss the introduction to ML Con & IoT Con with an outlook into the program and to the conference proceedings by Conference Chair Sebastian Meyen.
en
Let’s be frank. Machine learning is not about algorithms. Number one question in machine learning is data: how to collect and process it. Number two question is labeling. Machine learning engineers love labels and hate labeling. We spend time and resources, we invite humans in the game to say explicitly what the right answer is. But sometimes we want to be cautious and avoid trusting judgments. This helps us to perceive what the data actually mean, and to find hidden rules. Today we’ll look at a few business cases and figure out how to apply unsupervised machine learning in order to create powerful systems.
en
I started working with user experience (UX) long before the term was even known. Over the past 40 years, I’ve encountered many issues that have disturbed me – from creating purposely addictive programs, sites, and apps, to the current zeitgeist for various design trends at the expense of basic usability. I have seen research that is faked, ignored, or twisted by internal company politics and by the cognitive bias of the design team. And I have seen countless dark patterns that suppress accessibility and diversity by promoting false beliefs and false security.
Whenever we say, “That’s not my problem,” or, “My company won’t let me do that,” we are handing over our ethical responsibility to someone else – for better or for worse. Do innocent decisions evolve so that they promote racism or gender discrimination through inadvertent cognitive bias or unwitting apathy? Far too often they do.
We, as technologists, hold incredible power to shape the things to come. I would like to share my thoughts with you so you can use this power to truly build a better world for those who come after us!
en
en
en
en
en
AI is no longer a secret ritual performed by digital-native organizations. Indeed, there was a time when legacy industries would brush off the need of AI systems right at the outset. The times have changed! This talk is not about how to build an AI use case, or how to get managements' buy-in, or how the lack of talent hampers such initiatives. Neither does it focus on how to get the necessary data, nor on building DL models. While a few organizations continue to be challenged by these concerns, most are facing a completely new predicament. A business-driven dilemma of operationalizing AI systems beyond prototypes. That's what we are going to talk about. I will share my vision, blue print and personal stories across telecom, manufacturing and automotive industries, including both corporate and start-up experiences. I will tell you how to go from a shiny proof-of-concept to AI production systems, what challenges we faced, and the best practices to avoid the pitfalls.
en
Whether it's a linear regressor or a system of connected deep learning models, getting your models ready is half the battle. Did you design your machine learning system to survive the onslaught of visitors from your latest Reddit and Hacker News post? Or the influx of users shopping during Black Friday? Are you ready for a world filled with flakey networks, invalid data, and impatient users? In this talk you'll learn how to design and architect your machine learning systems for the harsh realities it will face. We will show you how we tackled these problems in a real, complex machine learning system at OLX and scaled it to serve up to billions of predictions per day, using software engineering principles while debunking the myth that Python code cannot scale.
en
en
en
en
GoJek has millions of monthly active users in Indonesia across our 20+ products and services. A major problem we faced was targeting these customers with promos and vouchers that were relevant to them. We developed a generalized model that takes into account the transaction history of users and gives a ranked list of our services that they are most likely to use next. From here on, we are able to determine the vouchers that we can target these customers with.
In this talk, I will be talking about our process while developing the model, the challenges we faced during the time, how we used PySpark to tackle these challenges and the impact it had on our conversion rates.
en
en
Any questions?
Contact us: [email protected]
Ausgefeilte Ingenieurskunst und modernste Technik vereint in einer Drohne: Mit der Mavic-2-Pro und ihrer ikonischen Hasselblad-Bildqualität, entdeckt ihr die Welt der Luftbildfotografie in herausragender Detailgenauigkeit völlig neu.
Meldet euch jetzt für die Online Edition an und gewinnt mit etwas Glück eine Mavic-2-Pro-Drohne im Wert von 1499 €!
Wir drücken Euch die Daumen!