Comparing Gradio, Streamlit, and FastAPI (with a little discussion of flask)
Jeff Hale's presentation for Data Science DC March 8, 2022
Repository with code at https://github.com/discdiver/dsdc-deploy-models
The 'macro view' on Big Query:
We started with an overview, some typical uses and moved to project hierarchy, access control and security.
In the end we touch about tools and demos.
This document outlines a Python-Django training course held at HCMUT in summer 2012. It provides details on the instructors, tools used in the training including Notepad++ and command line, and covers 13 parts that make up the content of the course including introductions to Python, Django, HTML/CSS, installation, models, views, templates and deployment. Exercises are provided to help reinforce learning concepts in Python and using HTML, CSS, and JavaScript to create a form.
This document discusses how FastAPI can be used to create web APIs for machine learning models. FastAPI allows ML developers to easily share models with colleagues by making them available as web APIs. It provides auto-generated documentation and supports features like validation, authentication, and file uploads that are useful for building ML APIs. FastAPI offers high performance and is easy to code, making it well-suited for both prototyping and production ML APIs.
The document provides an introduction to Airflow and Supervisor. It describes Airflow as a workflow management platform that uses directed acyclic graphs (DAGs) to define and automate tasks and workflows for batch processing. It discusses how Airflow uses Python as a runtime, has different executor types like Local and Celery, and stores metadata about tasks and DAGs in a database. The document also introduces Supervisor as a process monitoring tool used to run and monitor Celery, Flower and Airflow processes, and highlights its benefits like centralized control, a web UI, and automatic restart of failed applications.
This is the slides I used when I shared my humble insight on Django to the students in University of Taipei in 2016. Please feel free to correct me if there is anything wrong.
Lecture 6: Infrastructure & Tooling (Full Stack Deep Learning - Spring 2021)Sergey Karayev
The document discusses infrastructure and tooling for full stack deep learning. It provides an overview of the different components involved, including compute, data processing, experiment management, deployment, and software engineering practices. Specifically, it covers topics like GPU basics, cloud computing options, development versus training needs, popular programming languages and editors like Python and Jupyter Notebooks, and setting up development environments.
This document provides an overview of the Django web framework. It discusses Django's mission of encouraging rapid development and clean design. It demonstrates how to create a blog application with Django, including generating models, activating the admin interface, defining URLs and views, and using templates to display data. It also briefly compares Django to other frameworks like Ruby on Rails.
Google Cloud and Data Pipeline PatternsLynn Langit
1. The document discusses various Google Cloud Platform products and patterns for data pipelines, including virtual machines, storage, data warehousing, streaming analytics, machine learning, internet of things, and bioinformatics.
2. Demos and examples are provided of storage, virtual machines, BigQuery, Cloud Spanner, and machine learning on the Google Cloud Platform.
3. The core Google Cloud Platform products discussed for various data and analytics use cases include Cloud Storage, BigQuery, Cloud Dataflow, Compute Engine, Cloud Pub/Sub, and Bigtable.
The 'macro view' on Big Query:
We started with an overview, some typical uses and moved to project hierarchy, access control and security.
In the end we touch about tools and demos.
This document outlines a Python-Django training course held at HCMUT in summer 2012. It provides details on the instructors, tools used in the training including Notepad++ and command line, and covers 13 parts that make up the content of the course including introductions to Python, Django, HTML/CSS, installation, models, views, templates and deployment. Exercises are provided to help reinforce learning concepts in Python and using HTML, CSS, and JavaScript to create a form.
This document discusses how FastAPI can be used to create web APIs for machine learning models. FastAPI allows ML developers to easily share models with colleagues by making them available as web APIs. It provides auto-generated documentation and supports features like validation, authentication, and file uploads that are useful for building ML APIs. FastAPI offers high performance and is easy to code, making it well-suited for both prototyping and production ML APIs.
The document provides an introduction to Airflow and Supervisor. It describes Airflow as a workflow management platform that uses directed acyclic graphs (DAGs) to define and automate tasks and workflows for batch processing. It discusses how Airflow uses Python as a runtime, has different executor types like Local and Celery, and stores metadata about tasks and DAGs in a database. The document also introduces Supervisor as a process monitoring tool used to run and monitor Celery, Flower and Airflow processes, and highlights its benefits like centralized control, a web UI, and automatic restart of failed applications.
This is the slides I used when I shared my humble insight on Django to the students in University of Taipei in 2016. Please feel free to correct me if there is anything wrong.
Lecture 6: Infrastructure & Tooling (Full Stack Deep Learning - Spring 2021)Sergey Karayev
The document discusses infrastructure and tooling for full stack deep learning. It provides an overview of the different components involved, including compute, data processing, experiment management, deployment, and software engineering practices. Specifically, it covers topics like GPU basics, cloud computing options, development versus training needs, popular programming languages and editors like Python and Jupyter Notebooks, and setting up development environments.
This document provides an overview of the Django web framework. It discusses Django's mission of encouraging rapid development and clean design. It demonstrates how to create a blog application with Django, including generating models, activating the admin interface, defining URLs and views, and using templates to display data. It also briefly compares Django to other frameworks like Ruby on Rails.
Google Cloud and Data Pipeline PatternsLynn Langit
1. The document discusses various Google Cloud Platform products and patterns for data pipelines, including virtual machines, storage, data warehousing, streaming analytics, machine learning, internet of things, and bioinformatics.
2. Demos and examples are provided of storage, virtual machines, BigQuery, Cloud Spanner, and machine learning on the Google Cloud Platform.
3. The core Google Cloud Platform products discussed for various data and analytics use cases include Cloud Storage, BigQuery, Cloud Dataflow, Compute Engine, Cloud Pub/Sub, and Bigtable.
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
Flask is a micro web framework written in Python that allows developers to create web applications and APIs quickly. It is lightweight and extensible, allowing developers to add additional functionality through extensions. Flask applications are easy to get started with - they can be created with just a few lines of code. Common features like unit testing, database support, and template rendering are supported out of the box or through extensions.
Have you always wanted a flexible & interactive visualization that is easy for others to work with without handling all the Javascript libraries? Or do you want to build a user interface for your Machine Learning Model? This talk has you covered with building data apps in Python using Streamlit. It was presented at the Pyjamas Conference held virtualy across December 5th & 6th, 2020 (https://pyjamas.live/)
Youtube Link: https://youtu.be/woVJ4N5nl_s
** Python Certification Training: https://www.edureka.co/data-science-python-certification-course **
This Edureka PPT on 'Python Basics' will help you understand what exactly makes Python special and covers all the basics of Python programming along with examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Airflow is a workflow management system for authoring, scheduling and monitoring workflows or directed acyclic graphs (DAGs) of tasks. It has features like DAGs to define tasks and their relationships, operators to describe tasks, sensors to monitor external systems, hooks to connect to external APIs and databases, and a user interface for visualizing pipelines and monitoring runs. Airflow uses a variety of executors like SequentialExecutor, CeleryExecutor and MesosExecutor to run tasks on schedulers like Celery or Kubernetes. It provides security features like authentication, authorization and impersonation to manage access.
The document discusses NPR's content management strategy of creating content once and publishing it everywhere (COPE) using their API. It outlines the benefits of this approach, including improved development efficiencies, enabling new digital experiences and business opportunities. It also shares lessons learned, such as the need for flexible content structures, clear examples over documentation, and ensuring technology and content teams are aligned. Upcoming improvements to the API are also mentioned, such as image cropping and new output formats.
Python Flask Tutorial For Beginners | Flask Web Development Tutorial | Python...Edureka!
This document provides an overview of Flask, a Python-based web application framework. It begins with an introduction to Flask, explaining what Flask is and its advantages like being open source with a large community. It then covers topics like installing Flask, creating Flask applications, routing, templates, static files, the request object, cookies, redirects and errors. It concludes by mentioning some popular Flask extensions that add additional functionality for tasks like email, forms, databases and AJAX. The document appears to be from an online training course on Flask and aims to teach the basics of how to use the Flask framework to build web applications.
In this webinar you'll learn about the best practices for Google BigQuery—and how Matillion ETL makes loading your data faster and easier. Find out from our experts how to leverage one of the largest, fastest, and most capable cloud data warehouses to improve your business and save money.
In this webinar:
- Discover how to work fast and efficiently with Google BigQuery
- Find out the best ways to monitor and control costs
- Learn to leverage Matillion ETL and optimize Google BigQuery
- Get tips and tricks for better performance
PySpark Training | PySpark Tutorial for Beginners | Apache Spark with Python ...Edureka!
** PySpark Certification Training: https://www.edureka.co/pyspark-certification-training **
This Edureka tutorial on PySpark Training will help you learn about PySpark API. You will get to know how python can be used with Apache Spark for Big Data Analytics. Edureka's structured training on Pyspark will help you master skills that are required to become a successful Spark Developer using Python and prepare you for the Cloudera Hadoop and Spark Developer Certification Exam (CCA175).
This document provides an overview of Flask, a microframework for Python. It discusses that Flask is easy to code and configure, extensible via extensions, and uses Jinja2 templating and SQLAlchemy ORM. It then provides a step-by-step guide to setting up a Flask application, including creating a virtualenv, basic routing, models, forms, templates, and views. Configuration and running the application are also covered at a high level.
Big Query - Utilizing Google Data Warehouse for Media Analyticshafeeznazri
This topic will cover the intermediate understanding of Google Big Query and how Media Prima Digital utilizing Big Query as data warehouse for production.
Presentation of the paper "On Using JSON-LD to Create Evolvable RESTful Services" at the 3rd International Workshop on RESTful Design (WS-REST 2012) at WWW2012 in Lyon, France
The Modern Data Team for the Modern Data Stack: dbt and the Role of the Analy...Databricks
A traditional data team has roles including data engineer, data scientist, and data analyst. However, many organizations are finding success by integrating a new role – the analytics engineer. The analytics engineer develops a code-based data infrastructure that can serve both analytics and data science teams. He or she develops re-usable data models using the software engineering practices of version control and unit testing, and provides the critical domain expertise that ensures that data products are relevant and insightful. In this talk we’ll talk about the role and skill set of the analytics engineer, and discuss how dbt, an open source programming environment, empowers anyone with a SQL skillset to fulfill this new role on the data team. We’ll demonstrate how to use dbt to build version-controlled data models on top of Delta Lake, test both the code and our assumptions about the underlying data, and orchestrate complete data pipelines on Apache Spark™.
Apache Calcite is a dynamic data management framework. Think of it as a toolkit for building databases: it has an industry-standard SQL parser, validator, highly customizable optimizer (with pluggable transformation rules and cost functions, relational algebra, and an extensive library of rules), but it has no preferred storage primitives. In this tutorial, the attendees will use Apache Calcite to build a fully fledged query processor from scratch with very few lines of code. This processor is a full implementation of SQL over an Apache Lucene storage engine. (Lucene does not support SQL queries and lacks a declarative language for performing complex operations such as joins or aggregations.) Attendees will also learn how to use Calcite as an effective tool for research.
This document provides guidance on how to structure prompts to get the desired output from AI image generators. It recommends asking yourself questions about the type of image wanted, such as if it's a photo or painting, the subject, desired details, art style, and more. These answers can then be combined into a prompt sentence with keywords separated by commas to help steer the generation. Ordering and presentation of concepts is important to get the intended results.
Presentation given at Coolblue B.V. demonstrating Apache Airflow (incubating), what we learned from the underlying design principles and how an implementation of these principles reduce the amount of ETL effort. Why choose Airflow? Because it makes your engineering life easier, more people can contribute to how data flows through the organization, so that you can spend more time applying your brain to more difficult problems like Machine Learning, Deep Learning and higher level analysis.
Intro to Airflow: Goodbye Cron, Welcome scheduled workflow managementBurasakorn Sabyeying
This document discusses Apache Airflow, an open-source workflow management platform for authoring, scheduling, and monitoring workflows or pipelines. It provides an overview of Airflow's key features and components, including Directed Acyclic Graphs (DAGs) for defining workflows as Python code, various operators for building tasks, and its rich web UI. The document compares Airflow to traditional cron jobs, noting Airflow can handle task dependencies and failures better than cron. It also outlines how to set up an Airflow cluster on multiple nodes for scaling workflows.
Getting more out of Matplotlib with GRJosef Heinen
Matplotlib is the most popular graphics library for Python. It is the workhorse plotting utility of the scientific Python world. However, depending on the field of application, the software may be reaching its limits. This is the point where the GR framework will help. GR can be used as a backend for Matplotlib applications and significantly improve the performance and expand their capabilities.
This document describes a scalable, versioned document store built within PostgreSQL. It discusses the motivation for moving from multiple data stores and repositories to a single PostgreSQL database. It then covers the design of storing immutable content as Merkle DAG nodes linked by cryptographic hashes, with references and tags allowing different versions. It also explains how the system was implemented using PostgreSQL functions to generate hashes, insert nodes, and handle migrations from the original data model.
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
Flask is a micro web framework written in Python that allows developers to create web applications and APIs quickly. It is lightweight and extensible, allowing developers to add additional functionality through extensions. Flask applications are easy to get started with - they can be created with just a few lines of code. Common features like unit testing, database support, and template rendering are supported out of the box or through extensions.
Have you always wanted a flexible & interactive visualization that is easy for others to work with without handling all the Javascript libraries? Or do you want to build a user interface for your Machine Learning Model? This talk has you covered with building data apps in Python using Streamlit. It was presented at the Pyjamas Conference held virtualy across December 5th & 6th, 2020 (https://pyjamas.live/)
Youtube Link: https://youtu.be/woVJ4N5nl_s
** Python Certification Training: https://www.edureka.co/data-science-python-certification-course **
This Edureka PPT on 'Python Basics' will help you understand what exactly makes Python special and covers all the basics of Python programming along with examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Airflow is a workflow management system for authoring, scheduling and monitoring workflows or directed acyclic graphs (DAGs) of tasks. It has features like DAGs to define tasks and their relationships, operators to describe tasks, sensors to monitor external systems, hooks to connect to external APIs and databases, and a user interface for visualizing pipelines and monitoring runs. Airflow uses a variety of executors like SequentialExecutor, CeleryExecutor and MesosExecutor to run tasks on schedulers like Celery or Kubernetes. It provides security features like authentication, authorization and impersonation to manage access.
The document discusses NPR's content management strategy of creating content once and publishing it everywhere (COPE) using their API. It outlines the benefits of this approach, including improved development efficiencies, enabling new digital experiences and business opportunities. It also shares lessons learned, such as the need for flexible content structures, clear examples over documentation, and ensuring technology and content teams are aligned. Upcoming improvements to the API are also mentioned, such as image cropping and new output formats.
Python Flask Tutorial For Beginners | Flask Web Development Tutorial | Python...Edureka!
This document provides an overview of Flask, a Python-based web application framework. It begins with an introduction to Flask, explaining what Flask is and its advantages like being open source with a large community. It then covers topics like installing Flask, creating Flask applications, routing, templates, static files, the request object, cookies, redirects and errors. It concludes by mentioning some popular Flask extensions that add additional functionality for tasks like email, forms, databases and AJAX. The document appears to be from an online training course on Flask and aims to teach the basics of how to use the Flask framework to build web applications.
In this webinar you'll learn about the best practices for Google BigQuery—and how Matillion ETL makes loading your data faster and easier. Find out from our experts how to leverage one of the largest, fastest, and most capable cloud data warehouses to improve your business and save money.
In this webinar:
- Discover how to work fast and efficiently with Google BigQuery
- Find out the best ways to monitor and control costs
- Learn to leverage Matillion ETL and optimize Google BigQuery
- Get tips and tricks for better performance
PySpark Training | PySpark Tutorial for Beginners | Apache Spark with Python ...Edureka!
** PySpark Certification Training: https://www.edureka.co/pyspark-certification-training **
This Edureka tutorial on PySpark Training will help you learn about PySpark API. You will get to know how python can be used with Apache Spark for Big Data Analytics. Edureka's structured training on Pyspark will help you master skills that are required to become a successful Spark Developer using Python and prepare you for the Cloudera Hadoop and Spark Developer Certification Exam (CCA175).
This document provides an overview of Flask, a microframework for Python. It discusses that Flask is easy to code and configure, extensible via extensions, and uses Jinja2 templating and SQLAlchemy ORM. It then provides a step-by-step guide to setting up a Flask application, including creating a virtualenv, basic routing, models, forms, templates, and views. Configuration and running the application are also covered at a high level.
Big Query - Utilizing Google Data Warehouse for Media Analyticshafeeznazri
This topic will cover the intermediate understanding of Google Big Query and how Media Prima Digital utilizing Big Query as data warehouse for production.
Presentation of the paper "On Using JSON-LD to Create Evolvable RESTful Services" at the 3rd International Workshop on RESTful Design (WS-REST 2012) at WWW2012 in Lyon, France
The Modern Data Team for the Modern Data Stack: dbt and the Role of the Analy...Databricks
A traditional data team has roles including data engineer, data scientist, and data analyst. However, many organizations are finding success by integrating a new role – the analytics engineer. The analytics engineer develops a code-based data infrastructure that can serve both analytics and data science teams. He or she develops re-usable data models using the software engineering practices of version control and unit testing, and provides the critical domain expertise that ensures that data products are relevant and insightful. In this talk we’ll talk about the role and skill set of the analytics engineer, and discuss how dbt, an open source programming environment, empowers anyone with a SQL skillset to fulfill this new role on the data team. We’ll demonstrate how to use dbt to build version-controlled data models on top of Delta Lake, test both the code and our assumptions about the underlying data, and orchestrate complete data pipelines on Apache Spark™.
Apache Calcite is a dynamic data management framework. Think of it as a toolkit for building databases: it has an industry-standard SQL parser, validator, highly customizable optimizer (with pluggable transformation rules and cost functions, relational algebra, and an extensive library of rules), but it has no preferred storage primitives. In this tutorial, the attendees will use Apache Calcite to build a fully fledged query processor from scratch with very few lines of code. This processor is a full implementation of SQL over an Apache Lucene storage engine. (Lucene does not support SQL queries and lacks a declarative language for performing complex operations such as joins or aggregations.) Attendees will also learn how to use Calcite as an effective tool for research.
This document provides guidance on how to structure prompts to get the desired output from AI image generators. It recommends asking yourself questions about the type of image wanted, such as if it's a photo or painting, the subject, desired details, art style, and more. These answers can then be combined into a prompt sentence with keywords separated by commas to help steer the generation. Ordering and presentation of concepts is important to get the intended results.
Presentation given at Coolblue B.V. demonstrating Apache Airflow (incubating), what we learned from the underlying design principles and how an implementation of these principles reduce the amount of ETL effort. Why choose Airflow? Because it makes your engineering life easier, more people can contribute to how data flows through the organization, so that you can spend more time applying your brain to more difficult problems like Machine Learning, Deep Learning and higher level analysis.
Intro to Airflow: Goodbye Cron, Welcome scheduled workflow managementBurasakorn Sabyeying
This document discusses Apache Airflow, an open-source workflow management platform for authoring, scheduling, and monitoring workflows or pipelines. It provides an overview of Airflow's key features and components, including Directed Acyclic Graphs (DAGs) for defining workflows as Python code, various operators for building tasks, and its rich web UI. The document compares Airflow to traditional cron jobs, noting Airflow can handle task dependencies and failures better than cron. It also outlines how to set up an Airflow cluster on multiple nodes for scaling workflows.
Getting more out of Matplotlib with GRJosef Heinen
Matplotlib is the most popular graphics library for Python. It is the workhorse plotting utility of the scientific Python world. However, depending on the field of application, the software may be reaching its limits. This is the point where the GR framework will help. GR can be used as a backend for Matplotlib applications and significantly improve the performance and expand their capabilities.
This document describes a scalable, versioned document store built within PostgreSQL. It discusses the motivation for moving from multiple data stores and repositories to a single PostgreSQL database. It then covers the design of storing immutable content as Merkle DAG nodes linked by cryptographic hashes, with references and tags allowing different versions. It also explains how the system was implemented using PostgreSQL functions to generate hashes, insert nodes, and handle migrations from the original data model.
This document discusses adding logical replication protocol support to the psycopg2 library to allow Python applications to consume real-time replication streams from PostgreSQL. It provides code examples for connecting to replication slots, consuming change streams and messages, and stopping replication. Docker images are also available to simplify testing logical replication. Physical replication is also supported through a separate connection class. Asynchronous replication connections and keepalive messages are demonstrated as well.
Let Grunt do the work, focus on the fun!Dirk Ginader
The document discusses how Grunt can be used as a JavaScript task runner to automate repetitive tasks like minification. It provides examples of configuring Grunt tasks like Uglify to minify files and integrating it with package.json for an easy setup. Developers are encouraged to use Grunt as it allows configuring tasks in JavaScript instead of XML, is fast to set up, and helps focus on important work instead of repetitive tasks.
Strategies for refactoring and migrating a big old project to be multilingual...benjaoming
The document discusses strategies for refactoring a large project to support multiple databases and languages. It describes migrating the project to use PostgreSQL schemas to separate data for different games into different schemas. It also explains refactoring the code base to split it into separate applications and using django-parler to add multilingual support.
The document discusses the capabilities of HTML5 for building offline applications. It mentions several HTML5 features that enable offline functionality, including application cache, manifest files, and offline events. Application cache allows caching assets defined in a manifest file so the application can work offline. The offline event fires when the browser loses internet connectivity, informing the application it is now offline.
Is html5-ready-workshop-110727181512-phpapp02PL dream
The document discusses the capabilities of HTML5 for building offline applications. It mentions several HTML5 features that enable offline functionality, including application cache, manifest files, and offline events. Application cache allows caching assets defined in a manifest file so the application can work offline. The offline event fires when the browser loses internet connectivity, informing the application it is now offline.
Aviary's customizable SDK powers cross-platform photo editing for over 6,500 partners and over 70 million monthly active users across the globe. Some of our notable partners include Walgreens, Squarespace, Yahoo Mail, Flickr, Photobucket, and Wix. At Aviary, we use node.js for several mission-critical projects in production and have seen extremely positive results. In this talk, we will discuss how we approach some common situations that developers deploying node.js projects will likely need to tackle. We will walk you through our routing mechanism, our automated deployment system, some of our custom middleware, and our testing philosophy.
This document discusses various techniques for debugging Django applications, including:
- Using the development server and enabling debug mode.
- Adding print statements and the Python debugger pdb.
- Logging errors to files using the logging module.
- Installing debugging tools like django-debug-toolbar.
- Using a debugger like Werkzeug in production.
- Writing unit tests for testing.
- Integrating with error monitoring services like Arecibo.
- Configuring error handling for production deployments.
Google App Engine in 40 minutes (the absolute essentials)Python Ireland
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- The document discusses debugging Node.js applications in production environments at Netflix, which has strict uptime requirements. It describes techniques used such as collecting stack traces from running processes using perf and visualizing them in flame graphs to identify performance bottlenecks. It also covers configuring Node.js to dump core files on errors to enable post-mortem debugging without affecting uptime. The techniques help Netflix reduce latency, increase throughput, and fix runtime crashes and memory leaks in production Node.js applications.
This document discusses teaching game development using mini-games to teach programming and design concepts. It provides an overview of setting up the development environment and tools, basic game programming concepts like cameras and matrices, and approaches for simple games like Pong to teach core mechanics.
1. The document discusses various steps and tools for troubleshooting real production problems related to CPU spikes, thread dumps, memory leaks, and garbage collection issues.
2. It provides guidance on using tools like 'top', 'jstack', 'jmap', 'jcmd', Eclipse MAT and HeapHero to analyze thread dumps, capture heap dumps, and diagnose memory leaks.
3. The document also emphasizes the importance of enabling GC logs and capturing the right system metrics like thread states, file descriptors, and GC throughput to detect problems early.
This document provides examples of functional JavaScript code using point-free style and typeclasses. It includes code snippets demonstrating:
- Composing functions using point-free style to remove unnecessary variables
- Implementing common typeclass methods like map, chain, and ap for a Container type
- Deriving typeclass instances for custom types to gain functionality like Functor, Applicative, Foldable
- Using typeclasses to compose functions operating on different container types in a uniform way
The document provides code samples but does not explain concepts in detail. It focuses on demonstrating point-free code and typeclass patterns through examples rather than performing in-depth explanations or performance analysis. Questions are provided at the end to prompt
This document discusses using the C to Go translation tool c2go to translate C code implementing quicksort algorithms into Go code. It provides examples of translating simple quicksort C code, improving the translation by using a configuration file, and how c2go handles standard C functions like qsort by translating them to their Go equivalents. The examples demonstrate how c2go can generate valid Go code from C code but may require some manual fixes or configuration to handle certain data structures or language differences.
This is a 15-20 minute tech talk designed for those who wish to use Google APIs, but don't know how to get started quickly. This session introduces developers to two distinct ways of accessing our APIs, the standard HTTP-based REST APIs or Google Apps Script, a customized JS environment which provides Google API access via objects. It concludes with some inspirational app ideas of what you can build using Google Cloud APIs (covering both GCP & G Suite).
Glow is a compiler and execution engine for neural networks created by Facebook. It takes a high-level graph representation of a neural network and compiles it into efficient machine code for different hardware backends like CPU and OpenCL. The key steps in Glow include loading a model, optimizing the graph, lowering it to a low-level IR, scheduling operations to minimize memory usage, generating instructions for the backend, and performing optimizations specific to the target. Glow aims to provide a portable way to deploy neural networks across different hardware platforms.
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Dr. Sean Tan, Head of Data Science, Changi Airport Group
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In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
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The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
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* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
5. Flight plans
● Test flights for each plane
○ Hello world
○ Show data
○ Plot
○ Predict
○ Cruising altitude & turbulence (pros & cons)
● Grab your luggage (takeaways)
● Post-flight data (Q&A and questionnaire)
● Disembark
Jeff Hale ✈ @discdiver 5
6. Flight plan materials
● GitHub repo:
https://github.com/discdiver/dsdc-deploy-models
Jeff Hale ✈ @discdiver 6
18. Gradio #3: Plot it
def plot_pens(place_holder):
"""scatter plot penguin chars using matplotlib"""
df_pens = pd.read_csv("https://raw.githubuser…/penguins.csv")
fig = plt.figure()
plt.scatter(
x=df_pens["bill_length_mm"], y=df_pens["bill_depth_mm"])
return fig
Jeff Hale ✈ @discdiver 18
19. Gradio #3: Plot it
iface = gr.Interface(
fn=plot_pens,
layout="vertical",
inputs=["checkbox"],
outputs=["plot"],
title="Scatterplot of Palmer Penguins",
description="Let's talk pens. Click to see a plot.",
article="Talk more about Penguins here, shall we?",
theme="peach",
live=True,
).launch()
Jeff Hale ✈ @discdiver 19
30. Streamlit #1: Hello world
import streamlit as st
name = "Jeff"
st.title(f"Hello from Streamlit, {name}!")
- pip install streamlit
- streamlit run streamlit_hello.py
Jeff Hale ✈ @discdiver 30
33. Streamlit #2: Show data
import streamlit as st
import pandas as pd
st.title("Streamlit with pandas")
show = st.checkbox("Show dataframe")
df_pens = pd.read_csv( "https://raw.githubusercontent…/penguins.csv")
if show:
df_pens
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42. Streamlit #4: Predict - prettier
st.header("Story time")
st.image("https://cdn.pixabay.com/photo/2017/07/12/19/03/highway-2497900_960_720.jpg")
col1, col2 = st.columns(2)
with col1:
input_text = st.text_area("Enter your text here:")
with st.spinner("Generating story..."):
generator = pipeline("text-generation", model="gpt2")
if input_text:
generated_text = generator(input_text, max_length=60)
st.success("Here's your story:")
generated_text[0]["generated_text"]
with col2:
st.header("I'm in another column") Jeff Hale ✈ @discdiver 42
43. Streamlit Serving Options
- Serve from Streamlit’s servers for free: bit.ly/st-6plots
- Serve from Hugging Face Spaces or Heroku for free 🤗
- Pay Streamlit for more/better hosting 💰
- Host elsewhere
Jeff Hale ✈ @discdiver 43
44. Streamlit Pros
● Quick websites for many Python use cases 🐍
● Many intuitive interactive widgets ✅
● Caching ⏱
● Nice hosting options 🎁
● Thoughtful docs 📄
● Strong development cadence & team 💪
Jeff Hale ✈ @discdiver 44
45. Streamlit Cons
● Some customizations cumbersome ✨
● Single page only (without hacks) 1⃣
Jeff Hale ✈ @discdiver 45
47. FastAPI
Boeing 737
Commercial grade, fast, smart!
Jeff Hale ✈ @discdiver 47
Jeff Hitchcock, CC BY 2.0 <https://creativecommons.org/licenses/by/2.0>, via Wikimedia Commons
63. FastAPI #4: Predict: HTML Template
<body>
<h1>Enter characteristics of your property in Ames, Iowa</h1>
<form method='POST' enctype="multipart/form-data">
<label for="OverallQual">Overall House Quality</label><br>
<input type="number" name="OverallQual">
<label for="FullBath">Number of Full Bathrooms</label><br>
<input type="number" name="FullBath">
<label for="GarageArea">Garage Area</label><br>
<input type="number" name="GarageArea">
<label for="LotArea">Lot Area</label><br>
<input type="number" name="LotArea">
<p><button type="submit" value="Submit">Submit</button></p>
</form>
</body> Jeff Hale ✈ @discdiver 63
64. FastAPI #4: Predict: pydantic schema for form
class FeaturesForm(BaseModel):
"""pydantic model to get the form input we want"""
OverallQual: int
FullBath: int
GarageArea: int
LotArea: int
@classmethod
def as_form(cls, OverallQual: int = Form(...), FullBath: int = Form(...),
GarageArea: int = Form(...), LotArea: int = Form(...)):
return cls(OverallQual=OverallQual, FullBath=FullBath,
GarageArea=GarageArea, LotArea=LotArea)
Jeff Hale ✈ @discdiver 64
66. FastAPI #4: Predict: process
@app.post("/form", response_class=HTMLResponse)
async def make_prediction(
request: Request,
user_input: FeaturesForm=Depends(FeaturesForm.as_form)
):
"""accept form submission and make prediction"""
…load model and make prediction
return templates.TemplateResponse(
"results.html", {"request": request, "prediction": pred}
) Jeff Hale ✈ @discdiver 66
67. FastAPI #4: Display prediction
<body>
<div class='container'>
<div class='row'>
<div class='col-lg text-center'>
<h2> Your 🏡 house is worth
{{"${:,.2f}".format(prediction) }}.<h2>
<h3> Cool! 🎉 </h3 </div>
</div>
</div>
</body>
Jeff Hale ✈ @discdiver 67
68. FastAPI Pros
● Fastest Python API framework - async ASGI 🏎
● Automatic API documentation ⭐
● Extensive docs 📃
● Nice test client 👌
● Nice dependency injection 💉
● Data validation with pydantic / SQL Model integration ↔
● Security / authentication support 🔐
Jeff Hale ✈ @discdiver 68
70. FastAPI Cons
● Reliant on a single maintainer 😬
● Overriding uvicorn logging is a bit of a pain 🌳
● HTML templating more painful than Flask ⌨
Jeff Hale ✈ @discdiver 70
75. What about flask?
● Huge inspiration for FastAPI
● Now has more async support, but not easily full async
● FastAPI is faster
● FastAPI leverages typing
● FastAPI winning mindshare
● Gradio just switched to FastAPI backend
● Flask is nicer for quick web app
Jeff Hale ✈ @discdiver 75
84. Disembark
Thank you for flying the deployment skies!
Jeff Hale
● linkedin.com/in/-jeffhale/
● @discdiver
● jeffhale.medium.com/
Jeff Hale ✈ @discdiver 84
85. Versions used
● Gradio 2.8.7
● Streamlit 1.5.1
● FastAPI 0.75.0
Jeff Hale ✈ @discdiver 85
86. Python Tools to Deploy
Your Machine Learning
Models Faster 🛩
Jeff Hale