pybay 2023

pybay-2023/category.json

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+{
+  "title": "PyBay 2023"
+}

pybay-2023/videos/15311.json

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+{
+  "category": null,
+  "copyright_text": "CC BY-NC-SA",
+  "description": "Using Python to write code for web applications, scientific applications, and data analysis is extremely popular. If you're here at PyBay, you're probably doing it.  And while there are desktop applications in Python, it's far less popular for that.\r\n\r\nThose of us who write that back-end code are typically sitting in front of desktop or laptop computers for 6-10 hours a day.  And yet, while we may want those machines to do certain tasks for us, for some reason it rarely occurs to many of us to use Python to solve problems on *those* computers rather than the ones in the cloud.\r\n\r\nIn this talk, we'll explore some of the capabilities that local computation can give you which cloud and web applications can't, and look at some of the ways that Python can help you leverage that power.",
+  "language": "eng",
+  "quality_notes": null,
+  "recorded": "2023-10-08T12:15:00",
+  "slug": "Programming_Your_Computer_With_Python",
+  "source_url": "https://youtu.be/LceLUPdIzRs",
+  "speakers": [
+    "Glyph Lefkowitz"
+  ],
+  "summary": null,
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/LceLUPdIzRs/hqdefault.jpg",
+  "title": "Programming Your Computer With Python",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://youtu.be/LceLUPdIzRs"
+    }
+  ],
+  "related_urls": [
+    {
+      "label": "conf",
+      "url": "https://pybay.com/speakers/#sz-speaker-60c419c7-8de4-4c50-a99a-47403ed7cd54"
+    }
+  ],
+  "veyepar_state": 10
+}

pybay-2023/videos/15312.json

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+{
+  "category": null,
+  "copyright_text": "CC BY-NC-SA",
+  "description": "Python has a thriving ecosystem of single-purpose tools such as pytest, mypy, black and so on, but no standard orchestration tool to manage them efficiently. This makes it difficult to scale up Python codebases without a lot of bespoke scripting.\r\n\r\nAs a result, Python repos tend to be small, focused on building a single library or binary. Dependencies are managed by publishing versioned artifacts from one repo and consuming them in another repo by download.\r\n\r\nBut in the age of microservices, cloud functions, continuous delivery, and rapid iteration, this can be untenable. We often need to repeatedly build and deploy many small, interdependent parts out of a single large repo, and the sequential publishing cycle is too slow and cumbersome. \r\n\r\nPants is a build system with a focus on Python. It aims to be for Python what Cargo is for Rust: the one-stop shop for efficiently testing, typechecking, formatting, packaging and deploying code. Pants uses static analysis to grok your code's dependencies automatically, so you don't have to maintain large amounts of metadata. It uses this dependency data, along with its local and remote caching and concurrency capabilities, to dramatically speed up the development and CI cycle. \r\n\r\nThis talk will explain what Pants is and how it works. It will provide canonical examples of how to use Pants effectively with Python code, such Django apps and AWS Lambdas. And how to use it to package your code as a standalone binary or a Docker image.",
+  "language": "eng",
+  "quality_notes": null,
+  "recorded": "2023-10-08T12:15:00",
+  "slug": "Pants_Cargo_for_Python",
+  "source_url": "https://youtu.be/0-qKNTouuOY",
+  "speakers": [
+    "Benjy Weinberger"
+  ],
+  "summary": null,
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/0-qKNTouuOY/hqdefault.jpg",
+  "title": "Pants: Cargo for Python",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://youtu.be/0-qKNTouuOY"
+    }
+  ],
+  "related_urls": [
+    {
+      "label": "conf",
+      "url": "https://pybay.com/speakers/#sz-speaker-06c2b133-a57d-492e-b98b-385f2e54f2a9"
+    }
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+  "veyepar_state": 10
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pybay-2023/videos/15313.json

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+{
+  "category": null,
+  "copyright_text": "CC BY-NC-SA",
+  "description": "The nature of the field of Data Science encourages trial and error, but we can do a better job of destigmatizing failure and learn from our collective experiences. Join me as I take us on an adventure to find the beasts i.e. the different ways Data Science projects can fail. I will be talking about 4 major reasons for failure (data, infrastructure, implementation, and culture), their different aspects, and supplementing it with my experiences and case studies. I will also share how to control these beasts and recommend actions to be taken to ensure a successful end-to-end Data Science project.",
+  "language": "eng",
+  "quality_notes": null,
+  "recorded": "2023-10-08T13:15:00",
+  "slug": "Data_Science_beasts_failures_and_where_to_find_them",
+  "source_url": "https://youtu.be/pHlptwP20MY",
+  "speakers": [
+    "Grishma Jena"
+  ],
+  "summary": null,
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/pHlptwP20MY/hqdefault.jpg",
+  "title": "Data Science beasts (failures) and where to find them",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://youtu.be/pHlptwP20MY"
+    }
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+  "related_urls": [
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pybay-2023/videos/15314.json

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+{
+  "category": null,
+  "copyright_text": "CC BY-NC-SA",
+  "description": "Retrieval augmented generation has proven to be quite an effective technique to achieve good results with LLMs, so that they may provide answers based on your own data.\r\n\r\nWhile retrieval is a key step in such applications, other step have also started to show promise for various use cases: Ranking.\r\nIn this session we will discuss why retrieval and ranking play important roles to build effective applications with LLMs. In particular, we will see how we can use Lost in the Middle and Diversity Rankers with Haystack, an open source LLM framework, to improve the quality of our RAG pipeline results. We will also briefly discuss the role of hybrid retrieval",
+  "language": "eng",
+  "quality_notes": null,
+  "recorded": "2023-10-08T13:15:00",
+  "slug": "Ranking_and_Retrieval_Techniques_for_Retrieval_Augmented_Generation_with_Haystack",
+  "source_url": "https://youtu.be/6u7osMnIQHg",
+  "speakers": [
+    "Tuana Celik"
+  ],
+  "summary": null,
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/6u7osMnIQHg/hqdefault.jpg",
+  "title": "Ranking and Retrieval Techniques for Retrieval Augmented Generation with Haystack",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://youtu.be/6u7osMnIQHg"
+    }
+  ],
+  "related_urls": [
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+      "label": "conf",
+      "url": "https://pybay.com/speakers/#sz-speaker-bf72d9fa-7408-4c51-8438-562227a0d619"
+    }
+  ],
+  "veyepar_state": 10
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pybay-2023/videos/15315.json

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+{
+  "category": null,
+  "copyright_text": "CC BY-NC-SA",
+  "description": "This talk will introduce Pydantic users, old or new, to the new APIs available in Pydantic v2, best practices for using them, and some of the powerful new features we added support for, like PEP 593's `Annotated` and PEP 695's `TypeAliasType`.\r\n\r\nWe'll then dive deeper into how Pydantic v2 interacts with Python's type system, what we've learned from that, and how we can improve runtime <-> static typing interactions even more.\r\n\r\nFinally, we'll touch on some of the internals of Pydantic, including our use of Rust and how we've essentially ended up building a DSL that translates type hints and snippets of arbitrary user-defined logic into a DAG of computations in Rust (i.e. how we accidentally built a compiler).",
+  "language": "eng",
+  "quality_notes": null,
+  "recorded": "2023-10-08T13:30:00",
+  "slug": "Type_safe_data_validation_using_Pydantic_v2",
+  "source_url": "https://youtu.be/h9uCUVjKeas",
+  "speakers": [
+    "Adrian Garcia Badaracco"
+  ],
+  "summary": "",
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/h9uCUVjKeas/hqdefault.jpg",
+  "title": "Type safe data validation using Pydantic v2",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://youtu.be/h9uCUVjKeas"
+    }
+  ],
+  "related_urls": [
+    {
+      "label": "conf",
+      "url": "https://pybay.com/speakers/#sz-speaker-04379c6c-6e4d-4004-93a2-4028147c9ba1"
+    }
+  ],
+  "veyepar_state": 10
+}

pybay-2023/videos/15316.json

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+{
+  "category": null,
+  "copyright_text": "CC BY-NC-SA",
+  "description": "Existing mock data generators can only create individual, unrelated tables of fake data. Synthetic data services that can produce interwoven datasets require real data to anonymize. This leaves only error-prone custom scripts to create realistic, interdependent datasets for development and testing.\r\n\r\nIn this session learn how to define a .json configuration file and leverage the graph-data-generator PyPi package to quickly create custom, deeply interconnected fake datasets for your own Python projects.",
+  "language": "eng",
+  "quality_notes": null,
+  "recorded": "2023-10-08T13:45:00",
+  "slug": "Craft_Complex_Mock_Data",
+  "source_url": "https://youtu.be/N5Anbq8vYNk",
+  "speakers": [
+    "Jason Koo"
+  ],
+  "summary": null,
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/N5Anbq8vYNk/hqdefault.jpg",
+  "title": "Craft Complex Mock Data",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://youtu.be/N5Anbq8vYNk"
+    }
+  ],
+  "related_urls": [
+    {
+      "label": "conf",
+      "url": "https://pybay.com/speakers/#sz-speaker-66726e07-256d-4ae1-b1f8-2b87c97c3546"
+    }
+  ],
+  "veyepar_state": 10
+}

pybay-2023/videos/15317.json

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+{
+  "category": null,
+  "copyright_text": "CC BY-NC-SA",
+  "description": "Embeddings are a Large-Language-Model-adjacent technology that allow data such as text or images to be represented as an array of floating point numbers, representing a location in a weird, multi-dimensional space.\r\n\r\nThey are surprisingly powerful. Embeddings can be used to implement semantic search, find related content and even build text search against image data.\r\n\r\nI'll explain how they work, show you how to use them and teach you how to build weird and wonderful things with them that you couldn't build any other way.",
+  "language": "eng",
+  "quality_notes": null,
+  "recorded": "2023-10-08T14:00:00",
+  "slug": "Embeddings_What_they_are_and_why_they_matter",
+  "source_url": "https://youtu.be/snKTqb10vWQ",
+  "speakers": [
+    "Simon Willison"
+  ],
+  "summary": null,
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/snKTqb10vWQ/hqdefault.jpg",
+  "title": "Embeddings: What they are and why they matter",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://youtu.be/snKTqb10vWQ"
+    }
+  ],
+  "related_urls": [
+    {
+      "label": "conf",
+      "url": "https://pybay.com/speakers/#sz-speaker-b154e0eb-4b03-4acd-9e90-4ba7ce0929c9"
+    }
+  ],
+  "veyepar_state": 10
+}

pybay-2023/videos/15318.json

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+{
+  "category": null,
+  "copyright_text": "CC BY-NC-SA",
+  "description": "JSON Web Tokens, or JWTs for short, are all over the web. They can be used to track bits of information about a user in a very compact way and can be used in APIs for authorization purposes. Join me and learn what JWTs are, what problems it solves, how you can use JWTs, and how to be safer when using JWTs on your applications. All of that with some examples on how to validate and deal with JWTs in Python.",
+  "language": "eng",
+  "quality_notes": null,
+  "recorded": "2023-10-08T14:30:00",
+  "slug": "Lets_talk_about_JWT",
+  "source_url": "https://youtu.be/0vxVUjUL_Nw",
+  "speakers": [
+    "Jessica Temporal"
+  ],
+  "summary": null,
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/0vxVUjUL_Nw/hqdefault.jpg",
+  "title": "Let's talk about JWT",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://youtu.be/0vxVUjUL_Nw"
+    }
+  ],
+  "related_urls": [
+    {
+      "label": "conf",
+      "url": "https://pybay.com/speakers/#sz-speaker-46a2337d-e054-48c7-9355-c143140e64c0"
+    }
+  ],
+  "veyepar_state": 10
+}

pybay-2023/videos/15320.json

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+{
+  "category": null,
+  "copyright_text": "CC BY-NC-SA",
+  "description": "I\u2019ve played Wordle most days since late 2021. Maybe you have too? One thing I wonder after solving the puzzle for the day is whether I made a bad choice of words. Should I have chosen SMASH, or STASH? Just how lucky was I to solve a puzzle?\r\n\r\nThis talk will explore how to implement a Wordle statistics bot using Python's concurrent processing tools. No spoilers, I promise.",
+  "language": "eng",
+  "quality_notes": null,
+  "recorded": "2023-10-08T15:15:00",
+  "slug": "FORKS_POOLS_ASYNC_Solving_Wordle_with_Pythons_concurrency_tools",
+  "source_url": "https://youtu.be/ViUEGvNDwrQ",
+  "speakers": [
+    "Christopher Neugebauer"
+  ],
+  "summary": null,
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/ViUEGvNDwrQ/hqdefault.jpg",
+  "title": "FORKS? POOLS? ASYNC? Solving Wordle with Python\u2019s concurrency tools",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://youtu.be/ViUEGvNDwrQ"
+    }
+  ],
+  "related_urls": [
+    {
+      "label": "conf",
+      "url": "https://pybay.com/speakers/#sz-speaker-df477a5e-31da-4727-a04b-2d7a9c698715"
+    }
+  ],
+  "veyepar_state": 10
+}

pybay-2023/videos/15321.json

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+{
+  "category": null,
+  "copyright_text": "CC BY-NC-SA",
+  "description": "Platform Engineering teams face unique challenges in product development organizations. They have a big mission\u2014enabling the rest of the engineering organization to move fast without breaking things\u2014while usually lacking product managers on the team. However, applying product principles can be useful in achieving that goal.\r\n\r\nOne key area Platform Engineering owns is how services are built and which tools are used. In this talk, we'll explore how a product-focused approach can guide creating principled developer products. Pulling from my own experiences, I'll share real-world insights and lessons learned as a Platform Engineer.",
+  "language": "eng",
+  "quality_notes": null,
+  "recorded": "2023-10-08T15:15:00",
+  "slug": "Infrastructure_as_a_Product_Lessons_in_Platform_Engineering",
+  "source_url": "https://youtu.be/5hbxUX4dwyk",
+  "speakers": [
+    "Nick DiRienzo"
+  ],
+  "summary": null,
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/5hbxUX4dwyk/hqdefault.jpg",
+  "title": "Infrastructure as a Product: Lessons in Platform Engineering",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://youtu.be/5hbxUX4dwyk"
+    }
+  ],
+  "related_urls": [
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+      "label": "conf",
+      "url": "https://pybay.com/speakers/#sz-speaker-251cd32c-29ce-4562-acb3-ba0b40bef001"
+    }
+  ],
+  "veyepar_state": 10
+}

pybay-2023/videos/15322.json

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+{
+  "category": null,
+  "copyright_text": "CC BY-NC-SA",
+  "description": "In the ever-evolving landscape of Python development, managing dependencies and ensuring reproducibility remain pivotal challenges. Enter the Nix Package Manager \u2013 a powerful tool that transcends conventional package management approaches. Join us in this talk as we embark on a journey through the intricacies of Nix and its profound impact on Python projects.\r\n\r\nDive into the heart of Nix as we demystify its functionality and reveal its potential to transform your Python development workflow. Uncover how Nix transcends the limitations of traditional package managers by providing declarative configuration, fine-grained control over dependencies, and unmatched reproducibility.\r\n\r\nOur discussion delves deep into Nix's utility for Python projects, demonstrating how it streamlines package management and safeguards your projects against the pitfalls of dependency chaos. Witness how Nix ensures consistent environments across development, testing, and deployment, fostering collaboration and expediting development cycles.\r\n\r\nDrawing upon a decade of Python expertise, our speaker brings firsthand insights into how Nix can enhance the Python ecosystem. From managing intricate dependency graphs to crafting resilient virtual environments, Nix empowers you to focus on code rather than package wrangling.\r\n\r\nThroughout this talk, we will showcase practical examples and real-world scenarios, illuminating how Nix orchestrates Python projects with elegance and precision. Whether you're a seasoned Pythonista or a curious newcomer, this talk equips you with the knowledge to integrate Nix into your workflow, revolutionizing the way you approach Python development.\r\n",
+  "language": "eng",
+  "quality_notes": null,
+  "recorded": "2023-10-08T15:45:00",
+  "slug": "Elevating_Python_Development_with_Nix_Package_Manager",
+  "source_url": "https://youtu.be/AJs_izrEBOA",
+  "speakers": [
+    "Salar Rahmanian"
+  ],
+  "summary": null,
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/AJs_izrEBOA/hqdefault.jpg",
+  "title": "Elevating Python Development with Nix Package Manager",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://youtu.be/AJs_izrEBOA"
+    }
+  ],
+  "related_urls": [
+    {
+      "label": "conf",
+      "url": "https://pybay.com/speakers/#sz-speaker-906582fb-d40b-4c3a-9203-76e84148face"
+    }
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+  "veyepar_state": 10
+}

pybay-2023/videos/15323.json

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+{
+  "category": null,
+  "copyright_text": "CC BY-NC-SA",
+  "description": "Time series data from scientific instruments for fermentation, environmental sensors, or spectroscopy often comes in proprietary or unusual formats that are require custom logic to process. In addition, processing data at scale is challenge since enterprise laboratory information management systems (LIMS) typically rely on transactional, row-oriented databases that are not designed to handle millions of records at a time. However, with clever use of pandas for unusually formatted files or pyspark (via Databricks) for large numbers of records, this data can be processed into cleaner, more useful forms for further analysis.",
+  "language": "eng",
+  "quality_notes": null,
+  "recorded": "2023-10-08T15:45:00",
+  "slug": "Using_pandas_and_pyspark_to_address_challenges_in_processing_and_storing_time_series_instrument_data",
+  "source_url": "https://youtu.be/yCp6b_rHrLQ",
+  "speakers": [
+    "Aaron Wiegel"
+  ],
+  "summary": null,
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/yCp6b_rHrLQ/hqdefault.jpg",
+  "title": "Using pandas and pyspark to address challenges in processing and storing time series instrument data",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://youtu.be/yCp6b_rHrLQ"
+    }
+  ],
+  "related_urls": [
+    {
+      "label": "conf",
+      "url": "https://pybay.com/speakers/#sz-speaker-61dd28d8-78b9-4c39-9b10-11f62d83ab10"
+    }
+  ],
+  "veyepar_state": 10
+}