Our guest <a href="https://www.linkedin.com/in/maltepietsch/">Malte Pietsch</a> is a Co-Founder of <a href="https://deepset.ai">deepset</a>, 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 &#038; haystack and published the German BERT model. He is particularly interested in transfer learning and its application to question answering / semantic search.
Resources:
<ul>
<li>Deepset &#8211; Make sense out of your text data &#8211; <a href="https://deepset.ai/">https://deepset.ai/</a></li>
<li>FARM &#8211; Fast &#038; easy transfer learning for NLP &#8211; <a href="https://github.com/deepset-ai/FARM">https://github.com/deepset-ai/FARM</a></li>
<li>HayStack &#8211; Transformers at scale for question answering &#038; search &#8211; <a href="https://github.com/deepset-ai/haystack">https://github.com/deepset-ai/haystack</a></li>
<li>SageMaker &#8211; Machine learning for every developer and data scientist &#8211; <a href="https://aws.amazon.com/sagemaker/">https://aws.amazon.com/sagemaker/</a></li>
<li>Spot Instances &#8211; Managed Spot Training in Amazon SageMaker &#8211; <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html">https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html</a></li>
<li>ElasticSearch &#8211; Fully managed, scalable, and secure Elasticsearch service &#8211; <a href="https://aws.amazon.com/elasticsearch-service/">https://aws.amazon.com/elasticsearch-service/</a></li>
<li>Automatic mixed precision &#8211; Automatic Mixed Precision for Deep Learning &#8211; <a href="https://developer.nvidia.com/automatic-mixed-precision">https://developer.nvidia.com/automatic-mixed-precision</a></li>
<li>PyTorch &#8211; Open source machine learning framework that accelerates the path from research prototyping to production deployment &#8211; <a href="https://pytorch.org/">https://pytorch.org/</a></li>
<li>NumPy &#8211; Fundamental package for scientific computing with Python &#8211; <a href="https://numpy.org/">https://numpy.org/</a></li>
<li>MLFlow &#8211; An open source platform for the machine learning lifecycle &#8211; <a href="https://mlflow.org/">https://mlflow.org/</a></li>
<li>BERT &#8211; Bidirectional Encoder Representations from Transformers &#8211; <a href="https://en.wikipedia.org/wiki/BERT_(language_model)">https://en.wikipedia.org/wiki/BERT_(language_model)</a></li>
<li>SQuAD &#8211; The Stanford Question Answering Dataset &#8211; <a href="https://rajpurkar.github.io/SQuAD-explorer/">https://rajpurkar.github.io/SQuAD-explorer/</a></li>
<li>Sebastian Ruder &#8211; Research scientist at DeepMind &#8211; <a href="https://ruder.io/">https://ruder.io/</a></li>
<li>Andrew Ng &#8211; His machine learning course is the MOOC that had led to the founding of Coursera &#8211; <a href="https://www.coursera.org/instructor/andrewng">https://www.coursera.org/instructor/andrewng</a></li>
</ul>

Malte Pietsch

Rob De Feo

Deepset - Machine learning research to enterprise ready services

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 &#038; haystack and [&hellip;]

Startup Engineering Episode #2

Deepset - Machine learning research to enterprise ready services