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This document provides an overview of a data science bootcamp workshop on analyzing San Francisco traffic accident data using Jupyter notebooks, PixieDust, and IBM's Data Science Experience (DSX) platform. The workshop covers loading and visualizing the data, cleaning it, exploring hypotheses using charts, focusing analysis on a specific police district using SQL, and building a dashboard app with PixieApps. Key lessons learned from visualizing the data include times of day and days of week when most accidents occur.
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Starting with outlining the history of conventional version control before diving into explaining QoDs (Quantitative Oriented Developers) and the unique problems their ML systems pose from an operations perspective (MLOps). With the only status quo solutions being proprietary in-house pipelines (exclusive to Uber, Google, Facebook) and manual tracking/fragile "glue" code for everyone else.
Datmo works to solve this issue by empowering QoDs in two ways: making MLOps manageable and simple (rather than completely abstracted away) as well as reducing the amount of glue code so to ensure more robust pipelines.
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4. More about the telecommunications space than you’ll probably want to know!
5. Learn a new approach to building applications for enterprise and production.
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1. You know Scala – or want to know more about it.
2. You have deployed spark to production at your company or want to
3. You want to learn some neat tricks that may save you tons of time!
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1. Fully functioning spark app – with unit tests!
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This document provides an overview of using KNIME and Python for artificial intelligence (AI) and machine learning (ML) model building. It discusses that KNIME is a user-friendly platform for data science workflows without coding, while Python is widely used for AI/ML due to its many libraries and being platform independent. The document then provides step-by-step instructions on building basic ML models in KNIME, including data import, cleaning, visualization, linear regression training and prediction, and model evaluation. It concludes that both KNIME and Python are good options, with KNIME being more accessible and Python having more extensive library support and community.
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This document outlines an agenda for the Nice Bluemix Meetup #1 on June 30, 2016 hosted by CEEI NCA. The agenda includes an introduction to IBM Bluemix, a presentation on connecting objects to Bluemix using IBM Watson IoT, a live demo of an industrial IoT application using Bluemix, and a presentation of a Smart Garden application developed for a hackathon and hosted on Bluemix. Attendees will also have a Q&A session and networking cocktail.
The document discusses PixieDust, an open source library that simplifies and improves Jupyter Python notebooks. PixieDust provides features like package management, visualizations, cloud integration, a Scala bridge for defining variables in Python and Scala, and extensibility for custom visualizations. It allows users to easily install Spark packages, call visualization options, export data to files or cloud services, and encapsulate analytics into user interfaces.
This document provides an overview of a data science bootcamp workshop on analyzing San Francisco traffic accident data using Jupyter notebooks, PixieDust, and IBM's Data Science Experience (DSX) platform. The workshop covers loading and visualizing the data, cleaning it, exploring hypotheses using charts, focusing analysis on a specific police district using SQL, and building a dashboard app with PixieApps. Key lessons learned from visualizing the data include times of day and days of week when most accidents occur.
Inteligencia artificial, open source e IBM Call for CodeLuciano Resende
Nesta palestra vamos abordar algumas das tendências em Inteligência Artificial e as dificuldades na uso da Inteligência Artificial. Por isso, também apresentaremos algumas ferramentas disponíveis em código livre que podem ajudar a simplificar a adoção da IA. E faremos uma breve introdução ao “Call for Code” que é uma iniciativa da IBM para construir soluções na prevenção e reação a desastres naturais.
Version Control in AI/Machine Learning by DatmoNicholas Walsh
Starting with outlining the history of conventional version control before diving into explaining QoDs (Quantitative Oriented Developers) and the unique problems their ML systems pose from an operations perspective (MLOps). With the only status quo solutions being proprietary in-house pipelines (exclusive to Uber, Google, Facebook) and manual tracking/fragile "glue" code for everyone else.
Datmo works to solve this issue by empowering QoDs in two ways: making MLOps manageable and simple (rather than completely abstracted away) as well as reducing the amount of glue code so to ensure more robust pipelines.
DevOps for Data Engineers - Automate Your Data Science Pipeline with Ansible,...Mihai Criveti
- The document discusses automating data science pipelines with DevOps tools like Ansible, Packer, and Kubernetes.
- It covers obtaining data, exploring and modeling data, and how to automate infrastructure setup and deployment with tools like Packer to build machine images and Ansible for configuration management.
- The rise of DevOps and its cultural aspects are discussed as well as how tools like Packer, Ansible, Kubernetes can help automate infrastructure and deploy machine learning models at scale in production environments.
Streaming Trend Discovery: Real-Time Discovery in a Sea of Events with Scott ...Databricks
Time is the one thing we can never get in front of. It is rooted in everything, and “timeliness” is now more important than ever especially as we see businesses automate more and more of their processes. This presentation will scratch the surface of streaming discovery with a deeper dive into the telecommunications space where it is normal to receive billions of events a day from globally distributed sub-systems and where key decisions “must” be automated.
We’ll start out with a quick primer on telecommunications, an overview of the key components of our architecture, and make a case for the importance of “ringing”. We will then walk through a simplified solution for doing windowed histogram analysis and labeling of data in flight using Spark Structured Streaming and mapGroupsWithState. I will walk through some suggestions for scaling up to billions of events, managing memory when using the spark StateStore as well as how to avoid pitfalls with the serialized data stored there.
What you’ll learn:
1. How to use the new features of Spark 2.2.0 (mapGroupsWithState / StateStore)
2. How to bucket and analyze data in the streaming world
3. How to avoid common Serialization mistakes (eg. how to upgrade application code and retain stored state)
4. More about the telecommunications space than you’ll probably want to know!
5. Learn a new approach to building applications for enterprise and production.
Assumptions:
1. You know Scala – or want to know more about it.
2. You have deployed spark to production at your company or want to
3. You want to learn some neat tricks that may save you tons of time!
Take Aways:
1. Fully functioning spark app – with unit tests!
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IBM Bluemix Nice Meetup #1 - CEEI NCA - 20160630 - IBM France Lab
This document outlines an agenda for the Nice Bluemix Meetup #1 on June 30, 2016 hosted by CEEI NCA. The agenda includes an introduction to IBM Bluemix, a presentation on connecting objects to Bluemix using IBM Watson IoT, a live demo of an industrial IoT application using Bluemix, and a presentation of a Smart Garden application developed for a hackathon and hosted on Bluemix. Attendees will also have a Q&A session and networking cocktail.
Bringing an AI Ecosystem to the Domain Expert and Enterprise AI Developer wit...Databricks
We’ve all heard that AI is going to become as ubiquitous in the enterprise as the telephone, but what does that mean exactly?
Everyone in IBM has a telephone; and everyone knows how to use her telephone; and yet IBM isn’t a phone company. How do we bring AI to the same standard of ubiquity — where everyone in a company has access to AI and knows how to use AI; and yet the company is not an AI company?
In this talk, we’ll break down the challenges a domain expert faces today in applying AI to real-world problems. We’ll talk about the challenges that a domain expert needs to overcome in order to go from “I know a model of this type exists” to “I can tell an application developer how to apply this model to my domain.”
We’ll conclude the talk with a live demo that show cases how a domain expert can cut through the five stages of model deployment in minutes instead of days using IBM and other open source tools.
MateriApps LIVE! is a virtual machine containing over 270 materials science applications and tools. It can be run on Windows, Mac, or Linux computers to provide a full computational materials science environment without installation. Key features include pre-installed applications for DFT, quantum chemistry, molecular dynamics, and more. It aims to help researchers easily access and use materials simulation software through a centralized portal.
MateriApps LIVE! is a virtual machine containing over 270 materials science applications and tools that can be run without installation. It aims to promote open source software in computational materials science by forming an online community. Key features include pre-installed applications that can be used for simulations, tutorials, and hands-on sessions. The document provides instructions for setting up MateriApps LIVE! in VirtualBox, sharing files, and accessing resources and applications within the virtual machine.
22nd Athens Big Data Meetup - 1st Talk - MLOps Workshop: The Full ML Lifecycl...Athens Big Data
Title: MLOps Workshop: The Full ML Lifecycle - How to Use ML in Production
Speakers: Spyros Cavadias (https://www.linkedin.com/in/spyros-cavadias/), Konstantinos Pittas (https://www.linkedin.com/in/konstantinos-pittas-83310270/), Thanos Gkinakos (https://www.linkedin.com/in/thanos-gkinakos-03582a128/)
Date: Saturday, December 17, 2022
Event: https://www.meetup.com/athens-big-data/events/289927468/
- Fabric for Deep Learning (FfDL) is an open source project that aims to make deep learning accessible and scalable across multiple frameworks like TensorFlow, Caffe, PyTorch, and Keras.
- FfDL provides a consistent way to deploy, train, and visualize deep learning jobs on Kubernetes clusters using microservices. This allows for resilience, scalability, and multi-tenancy.
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Open Data Kit (ODK) is an open-source suite of tools that allows for mobile data collection and submission to an online server. It includes ODK Collect for mobile data entry, ODK Aggregate as a backend server for storage and analysis, and tools for building custom forms like ODK Build. The presentation provides an overview of installing and using the ODK system, including deploying Aggregate on Google App Engine or a local server, designing forms, collecting and analyzing data, and exporting it to formats like CSV, KML, and publishing to Google Fusion Tables. Examples are given of displaying collected data on maps and with charts through the ODK Aggregate interface.
JEEConf 2017 - In-Memory Data Streams With Hazelcast JetNeil Stevenson
Java 8 gave us a new language abstraction, the stream, which is good but not good enough.
We have some nice abstractions that enable us to code succinctly, and the JVM can exploit multi-processor machines for internal parallelisation. But fundamentally, the implementation is not distributed, we can’t pass data from JVM to JVM cleanly for external parallelisation.
In this talk we’ll look at Jet, a new open-source offering from Hazelcast, that solves this very problem.
The core construct is a DAG – directed acyclic graph – that runs across multiple machines and allows you to plumb together a pipeline of data from place to place, so a thread in one JVM can emit Java objects that a thread in another JVM takes as an input stream. All this happens in-memory, so it’s super fast and you can scale across as many JVMs as you need to boost the parallelisation.
For the developer, familiar constructs such as maps and filters are available so it’s easy to get going, and there’s a library of connections being developed so you can plug in existing data sources and data sinks with minimal effort.
First day of slides for @GAFFTA workshop http://www.gaffta.org/2012/07/24/hacking-the-kinect-with-openframeworks/
Part 1 of the live stream : http://www.youtube.com/watch?v=WXfy8Cuje-0&feature=plcp
Part 2 of the live stream :
http://www.youtube.com/watch?v=I80FsOlMPj8&feature=plcp
This document provides instructions for setting up an IoT service using The Things Network (TTN) and Google Sheets. It describes how to create a TTN account and application, program a device to send temperature data to TTN, and configure Google Sheets using a script to retrieve the device data from TTN and populate the sheets. Setting up this end-to-end IoT service allows real-time device data to be stored and viewed in Google Sheets.
MateriApps LIVE! is a virtual machine image containing over 280 materials science applications and tools. It can be run on Windows, Mac, or Linux computers without installation through VirtualBox. The document provides instructions on downloading MateriApps LIVE!, setting up VirtualBox, importing the virtual machine image, and logging in to begin using the pre-installed materials simulation software. Tips are also included on file sharing, changing display settings, and using commands within the virtual machine.
TensorFlow 16: Building a Data Science Platform Seldon
1. The document discusses building a data science platform on DC/OS to operationalize machine learning models. It outlines challenges at each stage of the ML pipeline and how DC/OS addresses them with distributed computing capabilities and services for data storage, processing, model training and deployment.
2. Key stages covered include data preparation, distributed training using frameworks like TensorFlow, model management with storage of trained models, and low-latency model serving for production with TensorFlow Serving.
3. DC/OS provides a full-stack platform to operationalize ML at scale through distributed computing resources, container orchestration, and integration of open source data and ML services.
The document discusses building machine learning solutions with Google Cloud. It describes Nexxworks as a team of data engineers, data scientists, and machine learning engineers who help close the gap between having lots of data and lacking insights by building robust and agile machine learning solutions through Google Cloud's scalable APIs. The document provides examples of use cases like predictive maintenance, logistics optimization, customer service chatbots, and medical image classification. It also discusses techniques like deep learning, word embeddings, convolutional neural networks, and reinforcement learning.
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Glynn Bird – Cloudant – Building applications for success.
All too often, web applications are built to work in development but are not capable of scaling when success arrives. Whether the application is a log aggregator that can't deal with the throughput, a blog that can't handle traffic when it hits the heights of Google's rankings or a mobile game that goes viral, an application can become the victim of its own success. By building with Cloudant from the outset, and architecting the application to scale by design, we can build apps that scale as the traffic, data-volumes and users arrive. Using several real-life use cases, this talk will detail how Cloudant can solve an application's data storage, search and retrieval needs, scaling easily with success!
This document provides instructions for installing and using the INET framework for OMNeT++ simulations. It describes how to automatically or manually install INET, explore example simulations, familiarize yourself with OMNeT++, and create a new project using INET components. A wireless networking tutorial is presented that demonstrates creating a simple wireless network simulation by defining a topology and configuration file. The document also provides guidance on running the simulation and analyzing results using sequence charts and output visualization tools in the OMNeT++ IDE.
IBM has been working on AI for decades, with early pioneers like Nathan Rochester. Currently, IBM is focusing on making AI more accessible through open source projects like CODAIT and Model Asset eXchange. IBM contributes to many open source projects related to AI and machine learning like Apache Spark. The future of AI involves continuing to build better basic building blocks for tasks like perception, reasoning and social skills. Ensuring AI is developed responsibly to benefit humanity is important as the technology progresses.
This document discusses virtualizing big data in the cloud using Delphix data virtualization software. It begins with an introduction of the presenter and their background. It then discusses trends in cloud adoption, including how most enterprises now use a hybrid cloud strategy. It also discusses how big data projects are increasingly being deployed in the cloud. The document demonstrates how Delphix can be used to virtualize flat files containing big data, eliminating duplication and enabling features like snapshots and cloning. It shows how files can be provisioned from a source to targets, including the cloud, and refreshed or rewound when needed. In summary, the document illustrates how Delphix virtualizes big data files to simplify deployment and management in cloud environments.
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How to measure the performance of the CS-Cart project simply and without technical knowledge :) And of course, why all the online-performance-testing services lie, or dont provides a clear view ;)
https://www.simtechdev.com/cloud-hosting
---
Cloud hosting for CS-Cart, Multi-Vendor, WordPress, and Magento
by Simtech Development - AWS and CS-Cart certified hosting provider
free installation & migration | free 24/7 server monitoring | free daily backups | free SSL | and more...
Our application speaks, time series are one of their languages. During this talk I will share how to use the open source Tick Stack to spin up a modern monitoring system for your application and your infrastructure. DevOps, cloud computing and containers changed how we are writing and running our applications. This talk shows what InfluxData and the community is building to have a modern and flexible monitoring toolkit.
[drupalday2017] - Speed-up your Drupal instance!DrupalDay
Perchè la tua istanza Drupal non performa e cosa puoi fare per invertire la rotta. D'altronde è una questione complessa: i moduli, la qualità del codice, l'uso delle cache, ma anche la versione di PHP, il proxy-cacher, il tuo hosting e, in ultimo, le cavallette...
di Daniele Piaggesi
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
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- FfDL provides a consistent way to deploy, train, and visualize deep learning jobs on Kubernetes clusters using microservices. This allows for resilience, scalability, and multi-tenancy.
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Open Data Kit (ODK) is an open-source suite of tools that allows for mobile data collection and submission to an online server. It includes ODK Collect for mobile data entry, ODK Aggregate as a backend server for storage and analysis, and tools for building custom forms like ODK Build. The presentation provides an overview of installing and using the ODK system, including deploying Aggregate on Google App Engine or a local server, designing forms, collecting and analyzing data, and exporting it to formats like CSV, KML, and publishing to Google Fusion Tables. Examples are given of displaying collected data on maps and with charts through the ODK Aggregate interface.
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Java 8 gave us a new language abstraction, the stream, which is good but not good enough.
We have some nice abstractions that enable us to code succinctly, and the JVM can exploit multi-processor machines for internal parallelisation. But fundamentally, the implementation is not distributed, we can’t pass data from JVM to JVM cleanly for external parallelisation.
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First day of slides for @GAFFTA workshop http://www.gaffta.org/2012/07/24/hacking-the-kinect-with-openframeworks/
Part 1 of the live stream : http://www.youtube.com/watch?v=WXfy8Cuje-0&feature=plcp
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http://www.youtube.com/watch?v=I80FsOlMPj8&feature=plcp
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2. Key stages covered include data preparation, distributed training using frameworks like TensorFlow, model management with storage of trained models, and low-latency model serving for production with TensorFlow Serving.
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Perchè la tua istanza Drupal non performa e cosa puoi fare per invertire la rotta. D'altronde è una questione complessa: i moduli, la qualità del codice, l'uso delle cache, ma anche la versione di PHP, il proxy-cacher, il tuo hosting e, in ultimo, le cavallette...
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