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.
Deploying machine learning models from training to production requires companies to deal with the complexity of moving workloads through different pipelines and re-writing code from scratch.
Or Zilberman will demonstrate how simple it is to automatically transfer a full machine learning pipeline from Jupyter notebook to scale-out serverless functions for event-driven and real-time applications.
He will also address versioning challenges, showing how serverless functions can enable developers to update machine learning models and code together as a single versioned entity. The session will include a deep walkthrough and interactive demos.
Or Zilberman is leading the Data Science efforts at iguazio, a Data Science Platform, where he acts as a source of truth for algorithmic parts and drives innovation through pushing the platform's boundaries in a way that will simplify the data science research and production processes as much as possible. Prior to that Or was heading the Glispa's Global Audience Platform & SSP's Data Science operations and was part of an innovation team applying ML to many verticals such as Risk Management, Fraud detection and more...
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.
Carmine Paolino is a Senior Data Scientist at OLX Group where he works on computer vision, interpretable machine learning, and recommender systems. He has more than 4 years of industry experience as a Data Scientist, and more than 9 as a Software Engineer. Soon after his bachelor thesis he published a book chapter about large-scale distributed graph analysis, and he has recently collaborated on a state-of-the-art saliency detection technique. He's also an avid open source contributor, with patches to MXNet Model Server and Theano amongst others. When he's not working, he loves to take photos, write music, DJ, and learn more about Machine Learning, psychology, and the stock market. Once saved the Coding Horror blog from losing all its content.
Applying machine learning in online applications requires solving the problem of model serving: Evaluating the machine-learned model over some data point(s) in real time while the user is waiting for a response. Solutions such as TensorFlow Serving are available to solve this problem, where the model only needs to be evaluated over one data point per user request, but this is not sufficient for problems where many data points must be evaluated to make a decision, such as in search and recommendation.This talk will show that this is a bandwidth constrained problem, and outline an architectural solution where computation is pushed down to data shards in parallel. It will demonstrate how this solution can be put into use with Vespa.ai, an open source engine, to achieve scalable model serving of TensorFlow and ONNX, and show benchmarks comparing performance and scalability to TensorFlow Serving. Model serving with Vespa is used today for some of the worlds largest recommender systems, such as serving personalized content on all Yahoo content pages and personalized ads in the worlds third-largest ad network. These systems evaluate models over millions of data points per request for hundreds of thousands of requests per second.
Jon Bratseth is a distinguished architect in the Big Data and AI group of Verizon, and the architect and one of the main contributors
to Vespa.ai, the open big data serving engine. Jon has 20 years experience as architect and programmer on large distributed systems, and a frequent public speaker. He has a master in computer science from the Norwegian University of Science and Technology.
The process of building an AI looks like is so glamorous but is a long process, and at the end of the day, the tasks related to the AI model are just a 5% or less of the project.
We will see how to start an AI project from zero: defining the objectives, creating the architecture, building the game interfaces, massive data pipelines, defining model strategies, how to parallelize everything, etc.
The "the abbey of crime" is an adamant 8-bit game. This game is more complicated than Montezuma Revenge and is a perfect challenge for an AI. Its complexity is about 10^1000 legal moves to solve it.
As AI technology, we will use Reinforcement Learning using Deep Neural Networks and Monte Carlo Tree Search.
The takeaways of this talk will be: understanding all the process involved to create an AI and learning the basics of Reinforcement Learning.
Juantomás leads Sngular’s Data Science team and is also Chief Envisioning Officer. Since joining Sngular in 2018, Juantomás has leveraged his extensive experience to harness the potential of new technologies and implement them across the company’s solutions and services.
Juantomás is Google Developer Expert for Cloud and Machine Learning, co-author of the software book La Pastilla Roja and creator of “AbadIA”, the artificial intelligence platform built to solve the popular Spanish game La Abadía del Crimen. He’s an expert on free software technologies and has been a speaker at more than 200 international industry events.
AI is everywhere. We get it. But still, many managers (Non-techy) can't understand how they can
harvest the huge leap we did in the field of Machine Learning to their own companies or
This special lecture will give you the whole picture when thinking
about adding some AI capabilities to your product/service.
In this lecture, you will learn the basics terms of AI; you will be introduced to all the latest
technologies in the field and finally, you will examine real use cases from global brands that
use AI as part of their strategy.
Uri is consulting companies that want to add AI capabilities to their servicesproducts. Uri is
working with big organizations and helps them to choose and implement the best AI solution for
their needs. Also, Uri has founded the biggest AI community in Israel called “Machine and
Deep Learning Israel”. The community has more than 16,000 members who are the leading AI
talents in Israel. Moreover, Uri is part of the national committees (Held by Prof. Eviatar Matania
and Prof.Isaac Ben Israel), which are responsible for establishing the Israeli strategy in the field
of AI. Alongside that, Uri is a member of the INSS committee, which examines AI in national
Data Science has a lot of work that's actually very tedious and most data scientists prefer to avoid that work.
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.
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).
Hotel cancellations can cause issues for many businesses in the industry. Not only do cancellations result in lost revenue, but this can also cause difficulty in coordinating bookings and adjusting revenue management practices.
This session explores how machine learning techniques can be used to predict hotel cancellations. Firstly, data manipulation techniques with pandas are employed to effectively process over 20,000 customer entries. Then, feature selection tools such as the Extra Trees Classifier are used to pinpoint the main drivers of hotel cancellations. The use of logistic regressions, support vector machines, and SARIMA are employed for prediction purposes, and extensive visualizations with pyplot are also generated to illustrate cancellation trends across different time periods.
Michael Grogan is a machine learning consultant and educator from Ireland. He has a strong interest in time series analysis using both Python and R, and the use of such analysis in generating business intelligence solutions.
Michael regularly uses AWS and TensorFlow to build and deploy machine learning models and has delivered the “TensorFlow 2.0 Essentials – What’s New” course seminar for O’Reilly Media.
Michael is also a regular speaker at various data science seminars and has delivered talks at venues including Big Data Vilnius, Nordic Data Science and Machine Learning Summit Stockholm, University College Cork R-Users, Trinity College Dublin, and World Machine Learning Summit Dublin.
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.
Gunjan has been working in the industry for 3 years and has a background in Mathematics. Currently, she is working with the Fraud Team in the Gopay (Gojek) Data Science team.
She can talk about statistical models with you all day long and can’t help but notice patterns everywhere in her life. Along with her day job, she also mentors aspiring young data scientists.
Attacks on machine learning systems include a wide range of different approaches and do not end with the notorious Adversarial examples. Attacks can change the logic of the system (Adversarial examples and reprogramming) to obtain data from AI systems (so-called Membership inference or Model Extraction attacks) or, conversely, to inject data into the system (Poisoning, Backdoor, Trojan). Unfortunately, the silver bullet from these attacks has not been invented and is unlikely to be, but we will show you how to approach the security assessment of AI algorithms correctly and what metrics to look at, what approaches to protection can be applied and where is the best place to apply and how to eventually get the maximum protection for reasonable investment of resources.
* AI Security vs traditional Cybersecurity
* Who should care about AI Security: Industries
* Why should we care about AI Security: Threats, Initiatives, Research
* What is AI Security: AI Objects, Applications, ML tasks
* How to break AI: Different attacks
* When to protect AI: Approaches to protect AI
* Step by step AI Security project
* Where are we going?
Alexander Polyakov is an AI and cybersecurity expert, serial entrepreneur. He has 15 years’ practical experience in cybersecurity (pentesting, researching, compliance, security engineering, product management, architectures, technology leadership). He is a member of Forbes Technology Council; he publishes articles explaining his vision for future technologies and security. He has been recognized as Entrepreneur and R&D Professional of the Year by Hot Companies and Golden Bridge Awards. His expertise covers cybersecurity aspects of various complex systems from industry-specific to ML. He has found over 200 vulnerabilities, released dozens of whitepapers, two books, two MMOC trainings including the first AI security training
The amazing results of OpenAI's GPT-2 have rekindled interest in Natural Language Generation (NLG), a subfield of Natural Language Processing (NLP). But how does GPT-2 work, how is it trained and how does one interpret its output to generate text? And why, if those new neural network / transformer - based models have such an impressive performance, are rule-based NLG systems still the norm in commercial text generation applications?
This talk will cover the basics of rule based and ML-based NLG systems and their respective advantages and disadvantages. You will learn how Machine Learning systems like GPT-2 learn to generate text and what their strengths and weaknesses are. We will have a look at the latest attempts to better control the output of systems like GPT-2 and what is still necessary for Deep Learning based systems to completely take over one of the last bastions of symbolic/rule-based AI, natural language generation.
Christoph Henkelmann holds a degree in Computer Science from the University of Bonn. He is currently working at DIVISIO, an AI company from Cologne, where he is CTO and co-founder. At DIVISIO, he combines practical knowledge from two decades of server and mobile development with proven AI and ML technology. In his pastime he grows cacti, practices the piano and plays video games.
Artificial Intelligence (AI) is fundamentally changing business as we know it by creating significant value both in terms of productivity gains and increase in innovation. Companies are now faced with the challenge of developing the right AI strategy and adapting to a constantly evolving technology. This new reality requires managers to become familiar with the technology and to acquire the right tools that will help them plan their view on AI and build up their competitive edge.
In this “Transforming business with AI” talk we introduce the frameworks and tools that guide managers through the most critical questions such as “Is my organization ready for AI”, “How do I choose the right AI project” and “How to ensure the project is a success”? By using such frameworks managers can navigate the challenges brought-on by this new technology, and systematically and methodically deploy a winning AI strategy.
The talk is intended for decision makers, project managers and developers who are interested in understanding how AI can have an impact on their work, organization and competitive landscape.
Dr. Guy Yachdav is the Chief Data Science Officer at Artiio, an Israeli digital health startup. Additionally, Dr. Yachdav advises C-level executives on AI strategy as well as on building-up Data Science teams. Dr. Yachdav has over fifteen years experience designing, developing and commercializing big-data products in the healthcare, pharmaceutical and public sectors. He is also a guest lecturer at the Technical University of Munich and at the Technion, Israeli Institute of Technology. Dr. Yachdav holds a PhD in Computer Science from the Technical University of Munich and an MBA from Columbia Business School in New York.
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.
Dr. Maximilian Junker studied in Munich and Augsburg (Software Engineering) and received his Ph.D. form the Technical University of Munich. He is co-founder and managing director at Qualicen GmbH. For 10 years he has been consulting requirements engineers and test engineers to improve their requirements and tests. He regularly gives talks on topics related to requirements engineering and testing at scientific and industrial conferences as well as company events.
More Program will follow soon!
July 29 – 30, 2020
Workshop Day 9 am – 5 pm MESZ
Conference Day 9 am – 6 pm MESZ
Contact us: firstname.lastname@example.org
The Machine Learning Conference Online Edition 2020 is carried out with a browser based video solution with no need to install any additional software.
Teilnehmen und Gewinnen!
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 €!
Main conference day for free for all registered attendee of any ML Conference Edition 2020! Attendees will also receive a 25 % discount for the workshop day. The invitation will be sent to the email address used for the conference registration.