This deep dive explores the intersection of two trends in machine learning pipelines: state-of-the-art models in natural language processing and serverless cloud architectures. Since Google published Word2Vec in 2013, word embeddings became rapidly more powerful. However, this went along with a substantial increase in computational complexity. On the other hand, the shift to serverless architectures emphasizes lean modules, which are not suited for heavy calculations. In this deep dive, Timo will walk you through an implementation of a pretrained model into a serverless infrastructure on AWS. Afterward, you will have a blueprint that is easy to adapt to your use cases.

From the start Predictive Analytics World has been the place to discuss and share our common problems. These are your people – they understand your situation. Often rated the best session of all, sharing your problems with like-minded professionals is your path to answers and a stronger professional network. There will be two discussion rounds of 20 minutes each. So choose your two most burning topics and discuss with your colleagues.

Select your Round Table Discussion here.

What can we learn from the ancient Babylonians about predicting the future? And how can we close the gap between data analysis and the decision-making process?

The length of a decision-making process is almost entirely determined by the time humans spend transforming data into conclusions. This is because data must be interpreted before it can be taken into account. The gap between data and decision can be narrowed by focusing on relevant signals derived directly from data. Ideally, the algorithm used for prediction can provide these signals itself. Handing over responsibility to a system requires additional checks and controls. Therefore, the system must be flexible, robust and easy to monitor. The presented model for nonlinear regression shows such an all-in-one approach. You will see the algorithm working as a fund manager.

Sufficient training data is often a bottleneck for real-world machine learning applications. The computer vision community mitigated this problem by pretraining models on ImageNet and transferring knowledge to the desired task. Thanks to an emerging new class of deep language models, transfer learning has now also become hot in NLP. In this Malte will share strategies, tips & tricks along all model phases: Pretraining a language model from scratch, adjusting it for domain specific language and fine-tuning it for the desired down-stream task. He will demonstrate the practical implications by showing how BERT was deployed at a Fortune 500 company.

Machine learning algorithms for time series forecasting have become increasingly powerful in recent decades. Nevertheless, not only the quality of the forecasts is important, but also their acceptance by the staff. Especially with regard to automatic forecasts, distrust may arise among dispatchers. Furthermore, long-standing employees often have a detailed overview of customer behavior, market situation and other important factors. Therefore, it makes sense to include this expert knowledge in the predictions of complex algorithms. This can be achieved through the maximum entropy approach, which is discussed in this presentation. The approach is derived in detail and applied to real data.

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