Sessions 2022

Stay tuned for the full agenda

The sound-based machine learning solution developed for Hindustan Shipping Limited helps identify defects that are sub surface or interior to the part. The identification of defect is real time, during the part production. Thus, enabling one to take actions immediately and not wait to produce a scrap part. Takeaways of this presenation are:(1) insights into the sound based machine learning approach for sub surface and interior defect detection; (2) how to identify the location and magnitude of defect in real time.

Artificial intelligence and machine learning are well known in industry. When we look on production side, however real used solutions are very limited. In this table discussion, we like to discuss reasons for the actual situation. A blocking point for a rollout is that for business people machine learning and AI is hard to understand. On the other side, heuristics often generate similar results like complex machine learning algorithms and are much easier to understand for non data scientists. So, is machine learning just a hip? Also we will discuss another critical questions, because in general there two ways of implementing solutions: make or buy. So what is the best way in what situation?

Telefónica, the biggest Spanish telecommunications company, asked us to provide a machine learning solution capable of detecting when one of their DSLAMs has an anomaly in registered customers indicating a loss in customer IPTV service. You will be shown how to deal with thousands of time series data by combining clustering algorithms, smoothing methods and deep learning tools to obtain efficient and high-performance results.

A malt yield forecast with an excellent prediction performance, as well as first transfers to Augustiner Bräu, were successfully implemented to optimize the beer brewing process. The crucial next step is getting our ready-to-use analysis modules with built-in requirements into the running production. For this, we are creating an architecture for robust deployment, considering model resilience and automated detection of data drift and performance decay to eventually trigger new model training.

Machines from Kampf Schneid- und Wickeltechnik GmbH & Co. KG are used worldwide for winding and cutting a wide variety of materials. A tear-off of the flow materials during the production process means an expensive loss of production. We present an approach on how an “AI” can detect an imminent tear-off at an early stage. We present (1) the winding machine, process and infrastructure for data collection (2) the ML pipeline for unsupervised pattern recognition and (3) the business case briefly.

Within the research project AKKORD, MIELE, IPS and RapidMiner are working on developing a modular expandable and holistic reporting and analysis system that creates transparency about the present and future quality situation. In field data management, quality analyses are enabled by setting up standardized analysis modules and user-specific dashboards. MIELE designs the system to measure, monitor and forecast various KPIs to holistically improve the business unit’s quality management. The implementation at MIELE in particular shows the direct application in a business area where the use of predictive analytics is effective.

Assembling a car is a complex task consisting of many steps usually grouped and organized in work plans. Based on the car model and its specifications, creating a key performance indicator (KPI) optimized work plan can be very time consuming. This case study at Volkswagen shows how data science can assist and speed up this process. After using various text analytics methods to identify similar work plans descriptions, a semi-automated benchmarking approach provides a KPI-driven recommendation.

Building a reliable single source of truth in a company typically comes with the following dilemma: focusing the responsibility around a dedicated data engineering team (data lake) versus distributing the ownership among the product engineering teams (data mesh). This talk will focus on lessons learned from implementing a shared responsibility model for the trusted flow of information within a data platform.

Bayesian modeling allows for applications that are driven not only by data, but also by domain knowledge. With this technique we extended the functionality of an industrial tester at Hahn Automation to uncover the values of parameters that were not available through nondestructive testing. This in turn enables the finding root causes of NOK work-pieces as well as the shortening of test cicle times. The session will explain the basic of Bayesian modeling and walk the audience through the project and its impact for Hahn Automation.

This table discussion focuses on how data scientists, data nerds, head of data science, and chief data officers can contribute to sustainability in AI. We would like to discuss this from different views like science contribution to SDGs as well as running climate-neutral model developments. The participants should get some understanding on the carbon footprint of doing intensive modeling and what the industry can do to reduce it.

German health insurance companies are obliged to check billings for accuracy. The effort involved is enormous and the use of machine learning promises great optimization potential. The talk presents the experiences made here by SpectrumK, a provider of data services for health insurance companies. It will be discussed that different regimes (e.g. drugs, hospital stays, home health care) come with different requirements for the machine learning technology used and how integration into existing processes can succeed.

Over the past two years, ONE LOGIC supported a major Covid vaccine manufacturer and several government agencies in using data to reliably scale the Covid vaccine production and distribute doses to when and where they are needed. We believe this case study can be applied broadly across sensitive supply chains in biotech and pharma, but also on a global scale for issues like the current “chip crisis”.

The Greater Manchester case study tells a compelling story about the collaboration around data sharing across a health system and utilisation of business intelligence technologies. Multiple health care providers work as a collective urgent care system, sharing system pressures using business intelligence and predictive analytics. The reporting is not only near live but also supports a view of department pressures above and beyond how many people are waiting for treatment. Predictive metrics enable proactive utilisation of directing ambulance flows to support each other as a system.

In our work, we created an AI and high-throughput imaging pipeline, which can help biologist to generate thousands of single cell images and analyze them in a short time. This pipeline assists biologist in designing, understanding the mode of action and predicting the efficacy of different antibodies.

In recent years, there have been various efforts to product thinking and decentralized data loading using data meshes. However in deep learning, data loading is still challenging to master at scale. In this talk, we present our decentralized data loading solution and show why flexibility and collaboration is key to enable novel ML use cases. We hope to make large-scale model training accessible to a wider community and move towards more sustainable ML.

While most people prefer writing free-form text, it is easier to process structured data. This is a dilemma which many companies face as did our customer felmo (a mobile vet service), when asking users to input appointment reasons for vet visits. We were able to leverage a Sentence Bert architecture to help them convert these to structured data, which they can now use to improve their appointment preparation and scheduling. We will share our learnings from applying Sentence Bert to this task.

AI algorithms deteriorate and fail silently over time impacting the business’ bottom line. The talk is focused on learning how you should be monitoring machine learning in production. It is a conceptual and informative talk addressed to data scientists & machine learning engineers. We’ll learn about the types of failures, how to detect and address them.

In this session, two open source libraries will be demonstrated to show you how you can quickly deploy custom Named Entity Recognition (NER) solutions. NERDA is an easy-to-use interface to apply pre-trained transformer models (e.g. based on Huggingface) to your own challenges. Combined with FastAPI, a lightweight webframework for Python, you can build your solution in short time

This session presents Anomaly Detection Neuroevolution (AD-NEv) – a multi-level optimized neuroevolution framework. The method adapts genetic algorithms for: i) creating an ensemble model based on the bagging technique; ii) optimizing the topology of single anomaly detection models; iii) non-gradient fine-tuning network parameters. The results prove that the models created by AD-NEv achieve significantly better results than the well-known anomaly detection deep learning models.

Estimating lifetime of a machine or a wearing-part in a complex machine is a requirement in cost-optimal maintenance planning to reduce costly downtime or avoid too frequent maintenance. Regression models mapping features to the time-to-failure are not suited in the real world because of censored data. This talk shows how survival regressions can be utilized for a maintenance planning application. We discuss and demonstrate available survival analysis tools, their strengths and limitations.

After quick successful POCs, the productive rollout of AI solutions often comes with unexpected challenges, especially for human-in-the-loop applications. The presentation will illustrate a holistic solution in three sections, starting with an end-to-end overview with the example of ML-based assistance systems, followed by deep-dives into the two main pain points: Imputation approaches for dealing with imperfect data and the psychology behind motivators for human interaction with such systems.

Despite the astounding results we have seen from transformers and language models in academia, deploying these models in industry is still very challenging because: 1) maintaining these models is very time and cost consuming 2) there is not yet enough clarity about when the advantages and challenges of these models outweigh classical ML models. In this talk, we discuss these challenges and share our findings and recommendations from working on real world examples at SPINS.

Despite the wealth of information available to job seekers, choosing one’s next job remains a complex endeavor. We will present an approach for creating job recommendations across industries by combining hand-annotated job data and a multivariate technique combining job title, job description, and skill set similarity. This method was used to scan 9 million job transitions to identify the best matches for any given job and uncover deep insights into how the labor market is organized today.

Survival analysis, the modeling of time-to-event data, is a statistical field with a long history and great potential to marketing and analytics. In this deep dive, you will learn about the brief origins of survival analysis and applications in the field of customer retention, which is of particular importance for subscription-based growth. Learn how to grow your understanding of customer churn, and learn how to better predict your customer’s lifetime, along with monetary aspects altogether.

For a publisher it is key to understand the customer journey of a submission process i.e., which online touchpoints and marketing channels drive submission. An attribution model has been evaluated using different machine learning approaches and is introduced into the marketing organisation of Springer Nature. The combination of describing elements with a predictive component and simulation approach helped us the understand the journey, prioritize and quantify the value of marketing activities.

Since 2017, Hauke is a Data Scientist at the innovation department of EWE AG in Oldenburg. Before that, he studied mathematics and worked as a researcher in the field of applied statistics. At EWE, Hauke is responsible for turning data into business value through data science projects in diverse application areas of the energy sector. Additionally, he focusses on spreading data literacy and data culture to enable colleagues to work with data and generate new ideas for data-driven solutions.

Fraud can be considerably reduced via speed, scalability, and stability. Investigating fraudulent activities, using fraud detection machine learning is crucial where decisions need to be made in microseconds, not seconds or even milliseconds. This becomes more challenging when things get demanding and scaling real-time fraud detection becomes a bottleneck. The talk will address these issues and provide solutions using the Hazelcast Open Source platform.

Adversarial archetypes exploit workings of any system to disrupt robustness and decision-making of the underlying algorithms. This deep dive presents AuthSHAP, a model agnostic and robust implementation of SHAP to uncover the extent to which key features are not appropriated by any model in the decision making. This ‘knowledge’ is significant information to a fraudster to design intelligent or adversarial attacks. The presentation shows that even in black-box settings, it is possible to understand the vulnerability.

Being able to predict KPIs for new, not yet published articles is a key factor in process optimization to assist editors with their daily work flow. Modern NLP methods allow us to use text content efficiently and to understand the connection between natural language and the respective KPIs. By combining statistical models (GAM), modern NLP methods (BERT), and XAI tools (SHAP), we are able to understand specific connections between text content and our KPI.

Classification is one of the most frequently solved problems using machine learning. Unfortunately, it cannot handle a case with a number of classes, varying over time, and require all the data to be labelled. There is another approach, designed to solve cases when we can’t perform full data annotation and/or would like to dynamically modify the number of classes. Similarity learning is capable of solving such problems even with extreme classification. We’re going to show how to use such models in production.

Proper utilization of Machine Learning and Predictive Modeling allow companies to increase profit, gain competitive advantages, grow, and win market completion. This is illustrated by results achieved across Major Telecom and Banking Companies. Efficient Proactive Retention, Revenue Stimulation, and Second-best Offer approaches require powerful Churn and Propensity models. There is a big number of techniques that can be applied to any modelling task, but it is almost impossible to know at the outset which will be most effective. Also, there are many ways to interconnect IT and Business. One way to address these topics is through advanced analytical base tables that proved to be highly efficient in all the illustrated cases.

Markdowns allow retailers to get rid of dead inventory and ensure turnover. Traditionally, even AI tools use a rule-based approach that constrains optimization and hurts revenues. Fashion retailer Benetton partnered with Evo to explore a new prescriptive approach to optimize markdown performance through complex process mapping. The resulting algorithm relied on a formal AI forecasting model feeding a price optimization model—a clear improvement that ultimately increased revenues by more than 5%

AI systems are fundamentially vulnerable – ignoring AI security risks will jeopardize safety of people and security of companies. This session presents a framework for secure AI development covering entire model lifecycle. It’s relevant for AI stakeholders across functions and levels in AI development, governance, and product security teams. The stages and activities described in the framework should enable AI stakeholders to implement a secure development lifecycle for their AI systems.

The desire to embody Responsible AI practices requires an understanding of, the context around, and the impact on the end user. To achieve this, design and research are just as pivotal to the RAI conversation as ML. There is no bigger risk, and no greater irresponsibility, than to not interface with those who will be affected by your design. This talk will share how to navigate customer relationships to encourage end user contact and mitigate assumptions and therefore risk.

 As far as people moved to sell and buy online because of covid-19, the importance of making right pricing decisions increased dramatically. Market is extremely competitive, supply chain is very complex. That’s why being top seller is much more complicated than it was before. This deep dive session will show how to build pricing agents that will react on different changes in a market and will keep your products on top of sellers.

During this session, we will discuss the different risk-based categories of AI laid out by the EU’s Artificial Intelligence Act and find out how to become more admissible as per the Act. Thereafter, we will walk through the concrete steps, tools, and practices such as monitoring, explainability, model fairness, and compliance that are instrumental in achieving Responsible AI and building more risk-proof and market-friendly solutions.

To evaluate the goodness of models we have a whole lot of KPI with a set of underlying ideas of “what goodness is”. However, the impact on the real-world (when the model is applied there) is very rarely taken systematically into account when the goodness of models is evaluated. Here we show a simple idea to assess the expected improvement (win) an a real-word situation for any classification problem at the example of campaign optimization and credit rating. Of course, this also allows to compare the real-world impact on win of different classifiers.

Machine learning models are becoming obsolete and must be retrained – this is the current widespread tenor. Is this actually true? And if yes, which components does a CI/CD pipeline for machine learning really need – and which are optional? How can the whole thing be implemented without building a complete Machine Learning Platform team? And which challenges are still difficult to solve at present? A field report including (mis)decisions, which will help to choose the right path for your own challenges.

SHAP is a great tool to help developers and users understand black box models. To push it to the next level, we will show how to leverage on Dash, SHAP, gifs, LSTM and auto-encoders to generate interactive dashboards with animations and visual representations to understand how different AI models learn and change their minds while progressively trained with growing amounts of data. We will show this application in the context of Chronic Kidney Disease prediction and broader Healthcare AI.

Healthcare is about people- the patients receiving care, the people delivering it and those creating ways to support this. Data via predictive analytics is an enabler to supports a healthcare provider or system to achieve operational excellence to help problems thus faced by the people. Doing this well not only delivers patient outcomes but supports preventative and proactive population health management. In this table discussion, we would like to discuss these questions: What is the connection between operational excellence, predictive data analytics and artificial intelligence? What are the driving forces for achieving organizational performance by using artificial intelligence? What are the barriers to achieving organizational performance using artificial intelligence?

The changes in the ads ecosystem have led marketers to lean on existing aggregate experimentation tools that assume a predetermined treatment effect. Choosing the treatment group to ensure you have high chances of detecting an effect is non-trivial. Built by Meta Open Source, GeoLift solves this problem by building well powered geographical experiments. Join us to go over why geographical experiments are necessary and their implications in the marketing industry, along with a demo of GeoLift.

The latest formula for making sound investment decisions involves mining new alternative data sources, using predictive analytics, swarm intelligence, reinforcement learning, and high-performance computing. In this talk, Prof. Anasse Bari explains how those components are driving value in the world of finance and how new Artificial Intelligence algorithms are reinventing Wall Street. He will filter fact from fiction, and outline successful use cases that he has recently led (e.g. how social performance and consumer reviews could be used as predictive features, how to derive actionable insights from geospatial images.). Prof. Bari will also present an overview that can help you design an AI strategy and implement viable solutions to generate a “predictive analytics-based investment thesis.”