This document provides an agenda and overview for a meetup on machine learning streams with Spark 1.0. The agenda includes sections on machine learning and business analytics, streams and real-time analytics, and a deep dive into MLlib. The MLlib section summarizes descriptive, predictive, and prescriptive algorithms in MLlib, including linear algebra techniques, summary statistics, SVD, PCA, Bayesian classification, logistic regression, SVM, regression, K-means, and matrix factorization. Gradient descent, L-BFGS, and KNN algorithms are also discussed.
Do you know what the first step in building a high converting list of loyal subscribers is? It’s not making the campaign, or the landing page, it’s identifying what your ideal subscriber wants. How are you going to appeal to them if you don’t know what they’re in need of?
The end of the beginning: Private defined benefit pensions and the new normalILC- UK
Held on Wednesday, 18th January 2017 in the House of Lords, this event launched the ILC-UK report 'The end of the beginning? Private defined benefit pensions and the new normal'.
Marketers have seen their jobs transformed over the past ten years. The transformation is happening again — but faster this time. According to The Economist Intelligence Unit's survey of 478 high-level marketing executives worldwide, sponsored by Marketo, more than 80% say they need to restructure marketing to better support the business. And 29% believe the need for change is urgent.
Do you know what the first step in building a high converting list of loyal subscribers is? It’s not making the campaign, or the landing page, it’s identifying what your ideal subscriber wants. How are you going to appeal to them if you don’t know what they’re in need of?
The end of the beginning: Private defined benefit pensions and the new normalILC- UK
Held on Wednesday, 18th January 2017 in the House of Lords, this event launched the ILC-UK report 'The end of the beginning? Private defined benefit pensions and the new normal'.
Marketers have seen their jobs transformed over the past ten years. The transformation is happening again — but faster this time. According to The Economist Intelligence Unit's survey of 478 high-level marketing executives worldwide, sponsored by Marketo, more than 80% say they need to restructure marketing to better support the business. And 29% believe the need for change is urgent.
During 2014, ILC-UK, supported by specialist insurance company, Partnership Assurance Group plc (Partnership), is undertaking a series of events to explore the relationship between our changing demography and public policy.
The second event in the series will explore how much we really know about life expectancy at the highest ages. How many of us are living to 90 and beyond? Why have estimates of life expectancy required revision? What does this tell us about increasing longevity? And what does this trend mean for public policy and long-term population planning?
Secrets of Enterprise Data Mining: SQL Saturday 328 Birmingham ALMark Tabladillo
If you have a SQL Server license (Standard or higher) then you already have the ability to start data mining. In this new presentation, you will see how to scale up data mining from the free Excel 2013 add-in to production use. Aimed at beginning to intermediate data miners, this presentation will show how mining models move from development to production. We will use SQL Server 2014 tools including SSMS, SSIS, and SSDT.
In this talk, you will hear the best practices from analysts at Gartner, engineers at Heroku, and experiences at VSP distilled down into a top ten list of characteristics that applications ought to have to achieve high availability, scalability and flexibility. Target audience includes developers of APIs and web-based applications, the analysts and architects that design them and the infrastructure teams that support them.
Secrets of Enterprise Data Mining: SQL Saturday Oregon 201411Mark Tabladillo
If you have a SQL Server license (Standard or higher) then you already have the ability to start data mining. In this new presentation, you will see how to scale up data mining from the free Excel 2013 add-in to production use. Aimed at beginning to intermediate data miners, this presentation will show how mining models move from development to production. We will use SQL Server 2014 tools including SSMS, SSIS, and SSDT.
.Net development with Azure Machine Learning (AzureML) Nov 2014Mark Tabladillo
Azure Machine Learning provides enterprise-class machine learning and data mining to the cloud. This presenter will cover 1) what AzureML is, 2) technical overview of AzureML for application development, 3) a reminder to consider SQL Server Data Mining, and 4) a recommend path for resources and next steps.
There are patterns for things such as domain-driven design, enterprise architectures, continuous delivery, microservices, and many others.
But where are the data science and data engineering patterns?
Sometimes, data engineering reminds me of cowboy coding - many workarounds, immature technologies and lack of market best practices.
During 2014, ILC-UK, supported by specialist insurance company, Partnership Assurance Group plc (Partnership), is undertaking a series of events to explore the relationship between our changing demography and public policy.
The second event in the series will explore how much we really know about life expectancy at the highest ages. How many of us are living to 90 and beyond? Why have estimates of life expectancy required revision? What does this tell us about increasing longevity? And what does this trend mean for public policy and long-term population planning?
Secrets of Enterprise Data Mining: SQL Saturday 328 Birmingham ALMark Tabladillo
If you have a SQL Server license (Standard or higher) then you already have the ability to start data mining. In this new presentation, you will see how to scale up data mining from the free Excel 2013 add-in to production use. Aimed at beginning to intermediate data miners, this presentation will show how mining models move from development to production. We will use SQL Server 2014 tools including SSMS, SSIS, and SSDT.
In this talk, you will hear the best practices from analysts at Gartner, engineers at Heroku, and experiences at VSP distilled down into a top ten list of characteristics that applications ought to have to achieve high availability, scalability and flexibility. Target audience includes developers of APIs and web-based applications, the analysts and architects that design them and the infrastructure teams that support them.
Secrets of Enterprise Data Mining: SQL Saturday Oregon 201411Mark Tabladillo
If you have a SQL Server license (Standard or higher) then you already have the ability to start data mining. In this new presentation, you will see how to scale up data mining from the free Excel 2013 add-in to production use. Aimed at beginning to intermediate data miners, this presentation will show how mining models move from development to production. We will use SQL Server 2014 tools including SSMS, SSIS, and SSDT.
.Net development with Azure Machine Learning (AzureML) Nov 2014Mark Tabladillo
Azure Machine Learning provides enterprise-class machine learning and data mining to the cloud. This presenter will cover 1) what AzureML is, 2) technical overview of AzureML for application development, 3) a reminder to consider SQL Server Data Mining, and 4) a recommend path for resources and next steps.
There are patterns for things such as domain-driven design, enterprise architectures, continuous delivery, microservices, and many others.
But where are the data science and data engineering patterns?
Sometimes, data engineering reminds me of cowboy coding - many workarounds, immature technologies and lack of market best practices.
Accelerating Data Lakes and Streams with Real-time AnalyticsArcadia Data
As organizations modernize their data and analytics platforms, the data lake concept has gained momentum as a shared enterprise resource for supporting insights across multiple lines of business. The perception is that data lakes are vast, slow-moving bodies of data, but innovations like Apache Kafka for streaming-first architectures put real-time data flows at the forefront. Combining real-time alerts and fast-moving data with rich historical analysis lets you respond quickly to changing business conditions with powerful data lake analytics to make smarter decisions.
Join this complimentary webinar with industry experts from 451 Research and Arcadia Data who will discuss:
- Business requirements for combining real-time streaming and ad hoc visual analytics.
- Innovations in real-time analytics using tools like Confluent’s KSQL.
- Machine-assisted visualization to guide business analysts to faster insights.
- Elevating user concurrency and analytic performance on data lakes.
- Applications in cybersecurity, regulatory compliance, and predictive maintenance on manufacturing equipment all benefit from streaming visualizations.
Pilveteenuste kasutamine võimaldab minutitega käivitada projekti, millele varem kulus nädalaid. Vajate müügikampaania toetamiseks lehte, mis suudab teenindada 100 tuhat kasutajat – käivita teenus kohe! Vajate terabaitide analüüsiks kiiret platvormi – käivita teenus kohe! Azure on töökindel ja kiire!
The Enterprise Guide to Building a Data Mesh - Introducing SpecMeshIanFurlong4
For organisations to successfully adopt data mesh, setting up and maintaining infrastructure needs to be easy.
We believe the best way to achieve this is to leverage the learnings from building a ‘central nervous system‘, commonly used in modern data-streaming ecosystems. This approach formalises and automates of the manual parts of building a data mesh.
This presentation introduces SpecMesh; a methodology and supporting developer toolkit to enable business to build the foundations of their data mesh.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Consolidation, cloud privé, cloud public, SQL As A Service etc. sont autant de scénarios de virtualisation possibles avec SQL Server. Cette session reposera les règles de bon usage de ce type de déploiement et les scénarios clés. Nous reviendrons sur quelques-unes des « Lessons learned from Azure ».
Consolidation, cloud privé, cloud public, SQL As A Service etc. sont autant de scénarios de virtualisation possibles avec SQL Server. Cette session reposera les règles de bon usage de ce type de déploiement et les scénarios clés. Nous reviendrons sur quelques-unes des « Lessons learned from Azure ».
Similar to Machine Learning Streams with Spark 1.0 (20)
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
10. Forecasting and Time Series
http://akorra.com/2012/06/06/top-10-creatures-that-influenced-martial-arts/
• Input of measure over time and related series
• Predictions generated for short term trends
• Based on cycles and events
Examples
§ Workforce Optimization
§ Timing Purchasing Decisions
§ Optimizing Maintenance Windows
§ Material Cost Planning
§ Equipment Usage Planning
Demand Sensing
15. The Challenges of Scaling Analytics
Classes of Analytics Complexity
Spark vs. Storm, etc.
Stream Paradigms and Spark
AGENDA
Streams and Real Time Analytics
18. Hype vs. Reality in Scaling Data Science
http://www.kdnuggets.com/2013/04/poll-results-largest-dataset-analyzed-data-mined.html
19. 2009 vs. 2014 Scaling Data Science
http://www.kdnuggets.com
20. Spectrum of Stream Based Analytics
Latency
Events/Sec
Months
Days
Hours
Minutes
Seconds
100 ms
< 1 ms
0 10 102 103 104 105 106
Big Data
NoSQL
RDBMS
Business
Monitoring
Machine
Monitoring
Real Time
Monitoring
Web Analytics
EDW
Analytics
Operational
Analytics
http://www.cs.ucr.edu/~mueen/ppt/StreamInsigh%205%20SLIDE%20DEMO.pptx
21. Challenges of Stream Based Applications
http://www.cs.ucr.edu/~mueen/ppt/StreamInsigh%205%20SLIDE%20DEMO.pptx
Devices
Sensors
Web
servers
Feeds
Complex Analytics &
Mining
22. Challenges of Stream Based Applications
http://www.cs.ucr.edu/~mueen/ppt/StreamInsigh%205%20SLIDE%20DEMO.pptx
Hopping Windows
Tumbling Windows
Event Synchronization
Latency
Time Window Management
29. MLlib Descriptive Analytics – Summary Statistics
http://halobi.com/wp-content/uploads/Blog-1-1024x600.png
Sample size
Maximum value of each column
Sample mean vector
Minimum value of each column
Number of nonzero elements
Sample variance vector
30. MLlib Descriptive Analytics - SVD
http://public.lanl.gov/mewall/kluwer2002.html
Singular Value Decomposition
Can Collapse Sparse Matrices to Denser Forms
34. MLlib Predictive Analytics – Logistic Regression
http://halobi.com/wp-content/uploads/Blog-1-1024x600.png
Grandaddy of Algorithms
Coefficients from states or exact values
Small scores can make big changes
35. MLlib Predictive Analytics - SVM
http://www.youtube.com/watch?v=3liCbRZPrZA http://www.projectrho.com/public_html/rocket/fasterlight.php
Linear Support Vector Machine for classifiers
Behold the “kernel trick”
36. MLlib Predictive Analytics – Regression
http://halobi.com/wp-content/uploads/Blog-1-1024x600.png
Linear
Ridge
Least
Absolute
Shrinkage &
Selection
Operator
43. MLlib Predictive Analytics – K Nearest Neighbor
http://www.youtube.com/watch?v=3liCbRZPrZA http://www.projectrho.com/public_html/rocket/fasterlight.php
Variation for classifiers
44. MLlib – A Call to Action
http://www.fanpop.com/clubs/voltron/images/2172709/title/original-fanart http://adventuretime.wikia.com/wiki/Princess_Monster_Wife
Coming Soon
• Decision Trees
• Model Performance Tools
It Takes A Village
• Time Series
• Ensemble MLI