Pandas & Cloudera: Scaling the Python Data Experience
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Ibis is a new open source project that allows Python data analysts and scientists to analyze large datasets using the same Python tools and APIs they already use for smaller datasets. Ibis provides a high-level Python interface for describing analytics and ETL processes that can be executed using Impala for scalability. The goal of Ibis is to enable analyzing big data using Python with no compromises to functionality or usability, at native hardware speeds. The first public release of Ibis is now available through Cloudera Labs.
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This document discusses Dell's solutions for big data and analytics workloads. It describes Dell's portfolio for unstructured analytics including storage, servers, and reference architectures. It also outlines Dell's vision for a unified streaming and batch analytics platform called Project Nautilus that would integrate Isilon storage with real-time stream processing.
Spark Infrastructure Made Easy
BlueData makes on-premises Spark infrastructure easy.
With BlueData, you can spin up virtual Spark clusters within minutes – providing secure, on-demand access to Big Data analytics and infrastructure. You can use Spark with or without the Hadoop ecosystem of tools – using HDFS, Tachyon, or any shared storage system.
You can also build analytical pipelines and create Spark clusters using our RESTful APIs. BlueData’s software platform leverages virtualization and patent-pending innovations to make it simpler, faster, and more cost-effective to deploy Hadoop or Spark infrastructure on-premises.
Learn more at http://www.bluedata.com
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Stephen P Robbins, Timothy A Judge & NeharikaVohra, Organizational Behaviour, 15thed., p. 439-466
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Stephen P Robbins, Timothy A Judge & NeharikaVohra, Organizational Behaviour, 15thed., p. 287-313
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Stephen P Robbins, Timothy A Judge & NeharikaVohra, Organizational Behaviour, 15thed., p. 477-502
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HPCC Systems Engineering Summit: Community Use Case: Because Who Has Time for...
Presenter: John Andleman, Staff Database Engineer, Citrix
In this session, John will share some interesting use cases leveraging the HPCC Systems platform, including those beyond traditional big data uses. John will also share his roadmap of HPCC projects being planned for the next few months and why he feels HPCC Systems is a more suitable solution than Hadoop based on experiences and lessons learned.
NOTE: The video of this presentation is the 3rd one shown in the accompanying YouTube link.
Apache deep learning 101
In my talk I will discuss and show examples of using Apache Hadoop, Apache Hive, Apache MXNet, Apache OpenNLP, Apache NiFi and Apache Spark for deep learning applications.
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This will be an introduction to executing Deep Learning Pipelines in an Apache Big Data environment.
My talk at Data Works Summit Sydney was listed in top 7 -> https://hortonworks.com/blog/7-sessions-dataworks-summit-sydney-see/
Also have speak at and run Future of Data Princeton and at Oracle Code NYC.
Ref:
https://community.hortonworks.com/articles/83100/deep-learning-iot-workflows-with-raspberry-pi-mqtt.html
https://community.hortonworks.com/articles/146704/edge-analytics-with-nvidia-jetson-tx1-running-apac.html
https://dzone.com/refcardz/introduction-to-tensorflow
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Timothy Spann, Solutions Engineer, Hortonworks
IBM Developer Model Asset eXchange
The common perception of applying deep learning is that you take an open source or research model, train it on raw data, and deploy the result as a fully self-contained artefact. The reality is far more complex.
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The goal of the IBM Developer Model Asset eXchange (MAX) is to remove these barriers to entry for developers to obtain, train, and deploy open source deep learning models for their business applications. In building the exchange, we encountered all these challenges and more.
For the training phase, we leverage the Fabric for Deep Learning (FfDL), an open source project providing framework-independent training of deep learning models on Kubernetes. For the deployment phase, MAX provides standardized container-based, fully self-contained model artifacts encompassing the end-to-end deep learning predictive pipeline.
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Pattern Mining: Extracting Value from Log Data
Pattern mining is an unsupervised machine learning technique used to discover frequent patterns and relationships in log data. It involves finding the top frequent sets of items that occur together in the data at least a minimum number of times. There are two main approaches - candidate generation which generates and filters candidate patterns in multiple passes over the data, and pattern growth which constructs conditional databases to avoid multiple full scans. Pattern mining can be used to find commonly purchased itemsets, extract features from log data, and derive rules for recommendations.
Intelligent Applications with Machine Learning Toolkits
Shawn Scully from Dato discusses how their machine learning toolkits can help developers quickly build intelligent applications. Their toolkits provide pre-built models for common tasks like recommendation, sentiment analysis, similarity search, churn prediction, and data matching. Developers can easily create applications with just a few lines of code, deploy models as microservices, and iteratively improve applications based on feedback. Dato aims to accelerate innovators by providing agile machine learning tools.
Text Analysis with Machine Learning
The document discusses text analysis with machine learning. It begins with introductions and then covers applications of text analysis like product reviews and social media. The bulk of the document discusses fundamentals of text processing like tokenization and feature engineering. It also discusses machine learning toolkits and task-oriented tools like sentiment analysis. Advanced topics like topic models and word embeddings are briefly introduced. The presentation aims to provide an overview of text analysis and point to further resources.
Machine Learning with GraphLab Create
This document introduces Dato and its machine learning platform. Dato provides intuitive APIs and toolkits that allow developers to easily create intelligent applications for tasks like recommendation, sentiment analysis, churn prediction, and more. It offers scalable data structures, high performance algorithms, and the ability to quickly develop and deploy machine learning models and services. Customers across various industries have been able to build and operationalize intelligent solutions faster using Dato to solve problems in fraud detection, data matching, recommendations, and other domains.
Machine Learning in Production with Dato Predictive Services
The document discusses Dato Predictive Services, a machine learning platform that helps deploy, serve, monitor, and manage machine learning models in production. It provides an overview of key capabilities like deploying models through different options, monitoring model performance and product usage, and evaluating models with online experiments. These capabilities aim to address common challenges of machine learning in production like deploying trained models, monitoring their behavior, and continuously improving them. The presentation includes a demo of a book recommender application built with Dato Predictive Services.
Machine Learning in 2016: Live Q&A with Carlos Guestrin
Live webinar session with Carlos Guestrin, Dato CEO and Amazon Professor of Machine Learning at University of Washington. Carlos reviewed 2015 highlights, previewed the Dato roadmap, and answered real-time questions from participants about use cases, algorithms, and resources.
Scalable data structures for data science
This document discusses scalable out-of-core data structures for data science. It introduces SFrame and SGraph, which allow machine learning on large datasets that exceed memory by using compressed columnar storage and lazy evaluation. SFrame provides a Python API for feature engineering and vectorized operations on tabular data. SGraph supports graph algorithms like PageRank on very large graphs with billions of nodes and edges. These tools are open source and support HDFS, S3, and other storage backends to enable scalable machine learning.
Introduction to Deep Learning for Image Analysis at Strata NYC, Sep 2015
Overview of Deep Learning for Image Analysis, including Convolutional Neural Nets and Deep Features.
Introduction to Recommender Systems
Machine Learning 101 Tutorial at Strata NYC, Sep 2015
Overview of recommender systems. Learn foundations (e.g., collaborative filtering, content-based methods, hybrid methods) and get practical tips for putting models into production.
Machine learning in production
Tutorial for Machine Learning 101 (an all-day tutorial at Strata + Hadoop World, New York City, 2015)
The course is designed to introduce machine learning via real applications like building a recommender image analysis using deep learning.
In this talk we cover deployment of machine learning models.
Overview of Machine Learning and Feature Engineering
Machine Learning 101 Tutorial at Strata NYC, Sep 2015
Overview of machine learning models and features. Visualization of feature space and feature engineering methods.
SFrame
Scalable tabular (SFrame, SArray) and graph (SGraph) data-structures built for out-of-core data analysis.
The SFrame package provides the complete implementation of:
SFrame
SArray
SGraph
The C++ SDK surface area (gl_sframe, gl_sarray, gl_sgraph)
Building Personalized Data Products with Dato
This document discusses building personalized data products and recommender systems using implicit and explicit user data. It describes how recommender systems work by using matrix factorization to learn latent factors about users and items from interaction data in order to predict ratings and rankings to drive personalized recommendations. The document also notes that recommender systems are commonly used by Netflix, Spotify, LinkedIn and Facebook to power personalized experiences and that even small improvements in recommendation quality can lead to significant business value.
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Webinar - Pattern Mining Log Data - Vega (20160426)
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Scaling Up Machine Learning: How to Benchmark GraphLab Create on Huge Datasets
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Intelligent Applications with Machine Learning Toolkits
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Machine Learning in 2016: Live Q&A with Carlos Guestrin
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Introduction to Deep Learning for Image Analysis at Strata NYC, Sep 2015
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HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
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Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
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During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
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Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
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This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
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Key Topics Covered
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- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
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This video focuses on integration of Salesforce with Bonterra Impact Management.
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Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Artificial Intelligence for XMLDevelopment
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.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
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.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
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https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
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Pandas & Cloudera: Scaling the Python Data Experience
- 1. 1 © Cloudera, Inc. All rights reserved. Ibis: Scaling the Python Data Experience Wes McKinney Marcel Kornacker JusFn Erickson Silvius Rus
- 2. 2 © Cloudera, Inc. All rights reserved. Wes McKinney • A key person in building today’s open source Python data community • Creator of pandas, a standard Python data wrangling and analyFcs toolkit used by data scienFsts • Author of best-‐selling canonical text Python for Data Analysis (2012) • Formerly Founder/CEO of DataPad (acquired by Cloudera in 2014)
- 3. 3 © Cloudera, Inc. All rights reserved. Python is popular… • Python has become a standard language of data science • Why is it popular? • Maximizes producFvity for data engineers and data scienFsts • Build robust so[ware and do interacFve data analysis with 100% Python code • Easy-‐to-‐learn and makes happy and producFve data teams • Large, diverse open source development community • Comprehensive libraries: data wrangling, ML, visualizaFon, etc. • Main use case: data science & engineering swiss army knife on small-‐to-‐medium size data
- 4. 4 © Cloudera, Inc. All rights reserved. …but Python does not scale today • Python ecosystem confined to single-‐node analysis • Great for smaller data sets • Requires sampling or aggregaFons for larger data • Distributed tools compromise in various ways • ExtracFng samples or aggregaFons for larger data means: • “Scales” by losing more fidelity • AddiFonal ETL overhead to extract samples/aggregaFons • Loss of producFvity with mulFple languages, tools, etc • Blocks certain analysis and use cases
- 5. 5 © Cloudera, Inc. All rights reserved. Ibis: Same Python, now at scale • Target user: • Data scienFsts and data engineers (“Python data users”) • Goals: • Mirrors single-‐node Python experience • Scales to any node and data size • No compromise in funcFonality or usability • InteracFve experience at naFve hardware speeds
- 6. 6 © Cloudera, Inc. All rights reserved. What’s announced? • First public release of Ibis • hgp://ibis-‐project.org • Beta release to Cloudera Labs • InviFng usage and community development • Apache-‐licensed open-‐source
- 7. 7 © Cloudera, Inc. All rights reserved. Ibis’s Vision • Uncompromised Python experience • 100% Python end-‐to-‐end user workflows • Enable integraFon with the exisFng Python data ecosystem (pandas, scikit-‐ learn, NumPy, etc) • InteracFve at big data scale • Full-‐fidelity analysis without extracFons • Scalability for big data • NaFve hardware speeds for a broad set of use cases
- 8. 8 © Cloudera, Inc. All rights reserved.
- 9. 9 © Cloudera, Inc. All rights reserved. Advantages of our approach • Analyze big data 100% in Python, with the same ease as small/medium data on the local filesystem • Full-‐fidelity data access • Familiar Python experience and integraFon with exisFng Python data libraries • Provide a means for Python high performance compuFng tools to be leveraged at Hadoop-‐scale
- 10. 10 © Cloudera, Inc. All rights reserved. Beta 0.3 release • High level Python API for describing analyFcs and ETL that can be executed by Impala • Familiar API for users of pandas • Comprehensive coverage of operaFons expressible as relaFonal data flows • Integrated tools for managing data in HDFS • Simple workflows to query data files in several formats (Parquet, Avro, Text) • pandas data interchange
- 11. 11 © Cloudera, Inc. All rights reserved. Ibis/Impala Joint Roadmap • More natural data modeling • Complex types support • IntegraFon with full Python data ecosystem • Advanced analyFcs + machine learning • Enable use of performance compuFng tools • User extensibility with naFve performance • In-‐memory columnar format • Python-‐to-‐LLVM IR compilaFon • Workflow and usability tools
- 12. 12 © Cloudera, Inc. All rights reserved. Benefits of Ibis • Maximize developer producFvity • Mirrors single-‐node Python experience • Solve big data problems without leaving Python • Leverage Python skills, ecosystem, and tools • Python as first-‐class language for Hadoop • Full-‐fidelity analysis without extracFons • Python analysis at any scale • NaFve hardware speeds for a broad set of use cases
- 13. 13 © Cloudera, Inc. All rights reserved. Thank you wes@cloudera.com