Add Pydata Berlin 2022

pydata-berlin-2022/category.json

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+{
+  "title": "PyData Berlin 2022"
+}

pydata-berlin-2022/videos/adrian-boguszewski-optimize-your-network-inference-time-with-openvino.json

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+{
+  "description": "Speaker:: Adrian Boguszewski\n\nTrack: PyData: Deep Learning\nDuring the talk, I'll present the OpenVINO\u2122 Toolkit. You'll learn how to automatically convert the model using Model Optimizer and how to run the inference with OpenVINO Runtime to infer your model with low latency on the CPU and iGPU you already have. The magic with only a few lines of code.\n\n\nRecorded at the PyConDE & PyData Berlin 2022 conference, April 11-13 2022.\nhttps://2022.pycon.de\nMore details at the conference page: https://2022.pycon.de/program/PKERX8\nTwitter: https://twitter.com/pydataberlin\nTwitter: https://twitter.com/pyconde",
+  "duration": 1359,
+  "language": "eng",
+  "recorded": "2022-04-11",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://2022.pycon.de/"
+    },
+    {
+      "label": "https://2022.pycon.de",
+      "url": "https://2022.pycon.de"
+    },
+    {
+      "label": "https://twitter.com/pyconde",
+      "url": "https://twitter.com/pyconde"
+    },
+    {
+      "label": "https://twitter.com/pydataberlin",
+      "url": "https://twitter.com/pydataberlin"
+    },
+    {
+      "label": "https://2022.pycon.de/program/PKERX8",
+      "url": "https://2022.pycon.de/program/PKERX8"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "artificial intelligence",
+    "data",
+    "data engineering",
+    "deep learning",
+    "ethics",
+    "machine learning",
+    "python"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi_webp/z2kjm3xbhbA/maxresdefault.webp",
+  "title": "Adrian Boguszewski: Optimize your network inference time with OpenVINO",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=z2kjm3xbhbA"
+    }
+  ]
+}

pydata-berlin-2022/videos/alejandro-saucedo-secure-ml-automated-security-best-practices-in-machine-learning.json

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+{
+  "description": "Speaker:: Alejandro Saucedo\n\nTrack: PyData: Machine Learning & Stats\nAs data science capabilities scale, the core concept of security becomes growingly critical. In this talk we will introduce the security challenges and solutions for data science practitioners relevant to each of the phases of the machine learning lifecycle, and we will provide a practical set of best practices and frameworks that can be adopted to ensure a relevant level of security is present in the multiple stages of the machine learning lifecycle.\n\n\nRecorded at the PyConDE & PyData Berlin 2022 conference, April 11-13 2022.\nhttps://2022.pycon.de\nMore details at the conference page: https://2022.pycon.de/program/APTWQS\nTwitter: https://twitter.com/pydataberlin\nTwitter: https://twitter.com/pyconde",
+  "duration": 1494,
+  "language": "eng",
+  "recorded": "2022-04-11",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://2022.pycon.de/"
+    },
+    {
+      "label": "https://twitter.com/pyconde",
+      "url": "https://twitter.com/pyconde"
+    },
+    {
+      "label": "https://2022.pycon.de",
+      "url": "https://2022.pycon.de"
+    },
+    {
+      "label": "https://2022.pycon.de/program/APTWQS",
+      "url": "https://2022.pycon.de/program/APTWQS"
+    },
+    {
+      "label": "https://twitter.com/pydataberlin",
+      "url": "https://twitter.com/pydataberlin"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "artificial intelligence",
+    "data",
+    "data engineering",
+    "deep learning",
+    "ethics",
+    "machine learning",
+    "python"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi_webp/82uiA5evtyU/maxresdefault.webp",
+  "title": "Alejandro Saucedo: Secure ML: Automated Security Best Practices in Machine Learning",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=82uiA5evtyU"
+    }
+  ]
+}

pydata-berlin-2022/videos/aleksander-molak-practical-graph-neural-networks-in-python-with-tensorflow-and-spektral.json

@@ -0,0 +1,48 @@
+{
+  "description": "Speaker:: Aleksander Molak\n\nTrack: PyData: Deep Learning\nGraph neural networks (GNNs) have become one of the hottest research topics in recent years. Their popularity is reinforced by hugely successful industry applications in social networks, biology, chemistry, neuroscience and many other areas. One of the main challenges faced by data scientists and researchers who want to apply graph networks in their work is that they require different data structures and a slightly different training approach than traditional deep learning models. During the workshop we\u2019ll demonstrate how to implement graph neural networks, how to prepare your data and \u2013 finally \u2013 how to train a GNN model for node-level and graph-level tasks using Spektral and TensorFlow.\n\n\nRecorded at the PyConDE & PyData Berlin 2022 conference, April 11-13 2022.\nhttps://2022.pycon.de\nMore details at the conference page: https://2022.pycon.de/program/ZMFZUB\nTwitter: https://twitter.com/pydataberlin\nTwitter: https://twitter.com/pyconde",
+  "duration": 5428,
+  "language": "eng",
+  "recorded": "2022-04-11",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://2022.pycon.de/"
+    },
+    {
+      "label": "https://2022.pycon.de/program/ZMFZUB",
+      "url": "https://2022.pycon.de/program/ZMFZUB"
+    },
+    {
+      "label": "https://2022.pycon.de",
+      "url": "https://2022.pycon.de"
+    },
+    {
+      "label": "https://twitter.com/pyconde",
+      "url": "https://twitter.com/pyconde"
+    },
+    {
+      "label": "https://twitter.com/pydataberlin",
+      "url": "https://twitter.com/pydataberlin"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "artificial intelligence",
+    "data",
+    "data engineering",
+    "deep learning",
+    "ethics",
+    "machine learning",
+    "python"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi_webp/hCY0_6etLjk/maxresdefault.webp",
+  "title": "Aleksander Molak: Practical graph neural networks in Python with TensorFlow and Spektral",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=hCY0_6etLjk"
+    }
+  ]
+}

pydata-berlin-2022/videos/alena-guzharina-overcoming-5-hurdles-to-using-jupyter-notebooks-for-data-science-by-the-jetbrai.json

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+{
+  "description": "Speaker:: Alena Guzharina\n\nTrack: PyData: Jupyter\nJupyter notebooks have pretty much become the standard for data science and data analysis teams. However, there\u2019s still a number of pain points when it comes to working with them. \r\n\r\nToday we\u2019ll discuss setting up environments, getting data from data providers, writing code without IDE support, and sharing results, as well as collaboration and reproducibility. We\u2019ll also explain how our team tackles these problems in Datalore.\n\n\nRecorded at the PyConDE & PyData Berlin 2022 conference, April 11-13 2022.\nhttps://2022.pycon.de\nMore details at the conference page: https://2022.pycon.de/program/9D9X8L\nTwitter: https://twitter.com/pydataberlin\nTwitter: https://twitter.com/pyconde",
+  "duration": 1499,
+  "language": "eng",
+  "recorded": "2022-04-11",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://2022.pycon.de/"
+    },
+    {
+      "label": "https://2022.pycon.de",
+      "url": "https://2022.pycon.de"
+    },
+    {
+      "label": "https://twitter.com/pyconde",
+      "url": "https://twitter.com/pyconde"
+    },
+    {
+      "label": "https://twitter.com/pydataberlin",
+      "url": "https://twitter.com/pydataberlin"
+    },
+    {
+      "label": "https://2022.pycon.de/program/9D9X8L",
+      "url": "https://2022.pycon.de/program/9D9X8L"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "artificial intelligence",
+    "data",
+    "data engineering",
+    "deep learning",
+    "ethics",
+    "machine learning",
+    "python"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi_webp/I_1y4pQ5ZQw/maxresdefault.webp",
+  "title": "Alena Guzharina: Overcoming 5 Hurdles to Using Jupyter Notebooks for Data Science, by the JetBrai...",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=I_1y4pQ5ZQw"
+    }
+  ]
+}

pydata-berlin-2022/videos/alexander-cs-hendorf-5-things-you-want-to-know-about-ai-adoption-in-the-enterprise.json

@@ -0,0 +1,48 @@
+{
+  "description": "Speaker:: Alexander CS Hendorf\n\nTrack: General: Production\nAll one needs is strategy, skill and resources to make digitalization and AI happen. So why is everything taking so long? Shouldn\u2019t you all be finished yesterday already? Or: how do we start? A practitioner's talk for everyone involved making AI happen in enterprises with use cases.\n\n\nRecorded at the PyConDE & PyData Berlin 2022 conference, April 11-13 2022.\nhttps://2022.pycon.de\nMore details at the conference page: https://2022.pycon.de/program/EMNPJW\nTwitter: https://twitter.com/pydataberlin\nTwitter: https://twitter.com/pyconde",
+  "duration": 1711,
+  "language": "eng",
+  "recorded": "2022-04-11",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://2022.pycon.de/"
+    },
+    {
+      "label": "https://twitter.com/pydataberlin",
+      "url": "https://twitter.com/pydataberlin"
+    },
+    {
+      "label": "https://2022.pycon.de",
+      "url": "https://2022.pycon.de"
+    },
+    {
+      "label": "https://twitter.com/pyconde",
+      "url": "https://twitter.com/pyconde"
+    },
+    {
+      "label": "https://2022.pycon.de/program/EMNPJW",
+      "url": "https://2022.pycon.de/program/EMNPJW"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "artificial intelligence",
+    "data",
+    "data engineering",
+    "deep learning",
+    "ethics",
+    "machine learning",
+    "python"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi_webp/0UG_JLUWJOQ/maxresdefault.webp",
+  "title": "Alexander CS Hendorf: 5 Things You Want to Know About AI Adoption in the Enterprise",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=0UG_JLUWJOQ"
+    }
+  ]
+}

pydata-berlin-2022/videos/alexandra-worner-a-data-scientist-s-guide-to-code-reviews.json

@@ -0,0 +1,48 @@
+{
+  "description": "Speaker:: Alexandra W\u00f6rner\n\nTrack: General: Python & PyData Friends\nA crucial aspect of software engineering teams' working agreements are code reviews. By applying the four-eyes principle on code, teams can reduce the number of bugs and errors, uncover misunderstandings early and ensure a certain level of quality across their common code base. \r\nIn essence, the relevance of code reviews does not change for data teams, including data scientists. However, due to the often experimental nature of data science tasks, standard code reviews do not always work well and therefore need some tweaks. \r\n\r\nThis talk will give a data scientist's view on code reviews, focussing on which aspects data scientists can pull from the general process and what needs to be adjusted in order to have effective and satisfying code reviews. Building on that, you will get recommendations for the following questions:\r\n* When and what should I review?\r\n* What feedback should I give?\r\n* What tools support me in executing this task?\n\n\nRecorded at the PyConDE & PyData Berlin 2022 conference, April 11-13 2022.\nhttps://2022.pycon.de\nMore details at the conference page: https://2022.pycon.de/program/YT7WM7\nTwitter: https://twitter.com/pydataberlin\nTwitter: https://twitter.com/pyconde",
+  "duration": 1807,
+  "language": "eng",
+  "recorded": "2022-04-11",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://2022.pycon.de/"
+    },
+    {
+      "label": "https://2022.pycon.de",
+      "url": "https://2022.pycon.de"
+    },
+    {
+      "label": "https://twitter.com/pyconde",
+      "url": "https://twitter.com/pyconde"
+    },
+    {
+      "label": "https://twitter.com/pydataberlin",
+      "url": "https://twitter.com/pydataberlin"
+    },
+    {
+      "label": "https://2022.pycon.de/program/YT7WM7",
+      "url": "https://2022.pycon.de/program/YT7WM7"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "artificial intelligence",
+    "data",
+    "data engineering",
+    "deep learning",
+    "ethics",
+    "machine learning",
+    "python"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi_webp/h8oI24i9dPk/maxresdefault.webp",
+  "title": "Alexandra W\u00f6rner: A data scientist's guide to code reviews",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=h8oI24i9dPk"
+    }
+  ]
+}

pydata-berlin-2022/videos/andreu-mora-unsupervised-shallow-learning-for-fraud-detection-on-marketplaces.json

@@ -0,0 +1,48 @@
+{
+  "description": "Speaker:: Andreu Mora\n\nTrack: PyData: Machine Learning & Stats\nCombating fraud, scams and wrongdoings in large marketplaces and platforms that connect millions of individuals as sellers and shoppers poses a very exciting and also difficult problem. Adyen leverages massive transaction information to solve this problem for platforms such as eBay or GoFundMe. In this talk we'll cover how we defined the problem, iterated on it and leveraged open source data tooling over python (airflow, spark, tensorflow, keras) and shallow unsupervised learning to solve it.\n\n\nRecorded at the PyConDE & PyData Berlin 2022 conference, April 11-13 2022.\nhttps://2022.pycon.de\nMore details at the conference page: https://2022.pycon.de/program/GCVHBH\nTwitter: https://twitter.com/pydataberlin\nTwitter: https://twitter.com/pyconde",
+  "duration": 1488,
+  "language": "eng",
+  "recorded": "2022-04-11",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://2022.pycon.de/"
+    },
+    {
+      "label": "https://2022.pycon.de",
+      "url": "https://2022.pycon.de"
+    },
+    {
+      "label": "https://twitter.com/pyconde",
+      "url": "https://twitter.com/pyconde"
+    },
+    {
+      "label": "https://twitter.com/pydataberlin",
+      "url": "https://twitter.com/pydataberlin"
+    },
+    {
+      "label": "https://2022.pycon.de/program/GCVHBH",
+      "url": "https://2022.pycon.de/program/GCVHBH"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "artificial intelligence",
+    "data",
+    "data engineering",
+    "deep learning",
+    "ethics",
+    "machine learning",
+    "python"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi_webp/Ce_IPb7htGY/maxresdefault.webp",
+  "title": "Andreu Mora: Unsupervised shallow learning for fraud detection on marketplaces",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=Ce_IPb7htGY"
+    }
+  ]
+}