Our guest Malte Pietsch is a Co-Founder of deepset, where he builds NLP solutions for enterprise clients, such as Siemens, Airbus and Springer Nature. He holds a M.Sc. with honors from TU Munich and conducted research at Carnegie Mellon University.

He is an active open-source contributor, creator of the NLP frameworks FARM & haystack and published the German BERT model. He is particularly interested in transfer learning and its application to question answering / semantic search.

Resources:

• Deepset - Make sense out of your text data - https://deepset.ai/
• FARM - Fast & easy transfer learning for NLP - https://github.com/deepset-ai/FARM
• HayStack - Transformers at scale for question answering & search - https://github.com/deepset-ai/haystack
• SageMaker - Machine learning for every developer and data scientist - https://aws.amazon.com/sagemaker/
• Spot Instances - Managed Spot Training in Amazon SageMaker - https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html
• ElasticSearch - Fully managed, scalable, and secure Elasticsearch service - https://aws.amazon.com/elasticsearch-service/
• Automatic mixed precision - Automatic Mixed Precision for Deep Learning - https://developer.nvidia.com/automatic-mixed-precision
• PyTorch - Open source machine learning framework that accelerates the path from research prototyping to production deployment - https://pytorch.org/
• NumPy - Fundamental package for scientific computing with Python - https://numpy.org/
• MLFlow - An open source platform for the machine learning lifecycle - https://mlflow.org/
• BERT - Bidirectional Encoder Representations from Transformers - https://en.wikipedia.org/wiki/BERT_(language_model)
• SQuAD - The Stanford Question Answering Dataset - https://rajpurkar.github.io/SQuAD-explorer/
• Sebastian Ruder - Research scientist at DeepMind - https://ruder.io/
• Andrew Ng - His machine learning course is the MOOC that had led to the founding of Coursera - https://www.coursera.org/instructor/andrewng