Microsoft provides several technologies in and around SQL Server which can be used for casual to serious data science. This presentation provides an authoritative overview of five major options: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, Microsoft Azure Machine Learning, and F#. Also included are tips on working with Python and R. These technologies have been used by the presenter in various companies and industries. This deck includes a sneak preview for SQL Server 2016.
Microsoft Technologies for Data Science sql_saturday_201505Mark Tabladillo
Microsoft provides several technologies in and around SQL Server which can be used for casual to serious data science. This presentation provides an authoritative overview of five major options: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, Microsoft Azure Machine Learning, and F#. Also included are tips on working with Python and R. These technologies have been used by the presenter in various companies and industries. Delivered at SQL Saturday Atlanta.
Delivered to SQL Saturday Columbus, GA
Microsoft provides several technologies which can be used for casual to serious data science. This presentation provides an authoritative overview of two major categories: products and services. The products include: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, SQL Server R Services, Microsoft R Technologies, and F#. The services include Cortana Intelligence and Bing Predicts. These technologies have been used by the presenter in various companies and industries, and he will be speaking toward how Microsoft uses these technologies today for its largest Azure customers.
Many companies are starting or expanding their use of data mining and machine learning. This presentation covers seven practical ideas for encouraging advanced analytics in your organization.
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 2012 tools including SSMS, SSIS, and SSDT.
Window functions are powerful analytic functions built into SQL Server. SQL Server 2005 introduced the core window ranking functions, and SQL Server 2012 added time and statistical percentage window functions. These functions allow for advanced variable creation, and are of direct benefit to people creating features for data science. This talk will also recommend further reading on this topic. The slide deck contains a link to the code on GitHub.
Microsoft Technologies for Data Science 201601Mark Tabladillo
Microsoft provides several technologies in and around SQL Server which can be used for casual to serious data science. This presentation provides an authoritative overview of five major options: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, Microsoft Azure Machine Learning, and F#. Also included are tips on working with Python and R. These technologies have been used by the presenter in various companies and industries.
Microsoft provides several technologies in and around SQL Server which can be used for casual to serious data science. This presentation provides an authoritative overview of five major options: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, Microsoft Azure Machine Learning, and F#. Also included are tips on working with Python and R. These technologies have been used by the presenter in various companies and industries. This deck includes a sneak preview for SQL Server 2016.
Microsoft Technologies for Data Science sql_saturday_201505Mark Tabladillo
Microsoft provides several technologies in and around SQL Server which can be used for casual to serious data science. This presentation provides an authoritative overview of five major options: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, Microsoft Azure Machine Learning, and F#. Also included are tips on working with Python and R. These technologies have been used by the presenter in various companies and industries. Delivered at SQL Saturday Atlanta.
Delivered to SQL Saturday Columbus, GA
Microsoft provides several technologies which can be used for casual to serious data science. This presentation provides an authoritative overview of two major categories: products and services. The products include: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, SQL Server R Services, Microsoft R Technologies, and F#. The services include Cortana Intelligence and Bing Predicts. These technologies have been used by the presenter in various companies and industries, and he will be speaking toward how Microsoft uses these technologies today for its largest Azure customers.
Many companies are starting or expanding their use of data mining and machine learning. This presentation covers seven practical ideas for encouraging advanced analytics in your organization.
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 2012 tools including SSMS, SSIS, and SSDT.
Window functions are powerful analytic functions built into SQL Server. SQL Server 2005 introduced the core window ranking functions, and SQL Server 2012 added time and statistical percentage window functions. These functions allow for advanced variable creation, and are of direct benefit to people creating features for data science. This talk will also recommend further reading on this topic. The slide deck contains a link to the code on GitHub.
Microsoft Technologies for Data Science 201601Mark Tabladillo
Microsoft provides several technologies in and around SQL Server which can be used for casual to serious data science. This presentation provides an authoritative overview of five major options: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, Microsoft Azure Machine Learning, and F#. Also included are tips on working with Python and R. These technologies have been used by the presenter in various companies and industries.
Basic Overview of Power BI Desktop and Excel and O365
◾What makes Power BI different from the Microsoft Office I have known for years?
◾What are the major Power BI features in Excel 2013 and 2010?
◾What are the major features for Power BI for Office 365?
◾How can our team license Power BI?
Microsoft and Revolution Analytics -- what's the add-value? 20150629Mark Tabladillo
Microsoft has been a leader in the enterprise analytics space for years. In 2014, Microsoft had already created R language functionality within Azure Machine Learning. On April 6, 2015, Microsoft and closed on a deal to acquire Revolution Analytics, a company focusing on scalable processing solutions initiated by the well-known R language. Many data science projects and initial demos do not need high-volume solutions: however, having a high-volume answer for the R language allows for planning or working toward the largest data science solutions.
This presentation describes the add-value for the Revolution Analytics acquisition. The talk covers 1) an overview of current data science technologies from Microsoft; 2) a description of the R language; 3) a brief review of the add-value for R with Azure Machine Learning, and 4) a description of the performance architecture and demo of the language constructs developed by Revolution Analytics. Most of the presentation will be focused on sections two and four. It is anticipated that these technologies will be partially if not fully integrated into SQL Server 2016.
Applied Enterprise Semantic Mining -- Charlotte 201410Mark Tabladillo
Text mining is projected to dominate data mining, and the reasons are evident: we have more text available than numeric data. Microsoft introduced a new technology to SQL Server 2014 called Semantic Search. This session's detailed description and demos give you important information for the enterprise implementation of Tag Index and Document Similarity Index, and will also provide a comparison between what semantic search is and what Delve does. The demos include a web-based Silverlight application, and content documents from Wikipedia. We'll also look at strategy tips for how to best leverage the new semantic technology with existing Microsoft data mining.
R is a free, open-source environment for statistical analysis and graphing. In its almost 20 years of existence, R has remained popular in both academic and business environments. The newer Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. This session outlines functional and performance differences between these two software packages. You’ll see demonstrations of best tips for integrating this software with Windows and walk away with guidelines for working with commercial software. A version of this presentation had 100 attendees at the PASS Business Analytics Conference in Chicago (April 2013), and 40 attendees for the PASS Virtual Business Analytics meeting (May 2013).
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.
How Big Companies plan to use Our Big Data 201610Mark Tabladillo
Underneath the shiny popular apps on tablets, smartphones, and entertainment channels are typically large cloud-based data centers. App developers leverage the cloud to provide advertisers with targeted sales opportunities, which has been accounting for an ongoing shift from paper to online media. This presentation will provide updated trends and statistics for 2016 on big data usage (based on consumer use), statistical concerns with big data, and the Microsoft big data story.
Required for certification, window functions are powerful analytic functions built into SQL Server. SQL Server 2005 introduced the core window ranking functions, and SQL Server 2012 added time and statistical percentage window functions. These functions allow for advanced variable creation, and are of direct benefit to people creating features for data science. This talk will also recommend further reading on this topic.
Data Mining Innovation with SQL Server 2014: SQL Saturday 328 Birmingham ALMark Tabladillo
This presentation includes a complex business analytics solution using elements across the Microsoft Business Intelligence technology. This talk will not have all the steps spelled out. Therefore you should prepare -- for example, by looking at the SQL Server 2014 Developer Kit -- since attendees will be expected to have working knowledge of SQL Server relational database, SSIS, SSAS, SSRS, SSMS and SSDT. At the end of the demonstration, you will be expected to participate in groups to promote one idea which builds on the demo, and everyone will be voting for their favorite groups. Come ready to create and celebrate your peers.
Document Classification using DMX in SQL Server Analysis ServicesMark Tabladillo
Presentation for SQL Saturday Raleigh NC, Septmber 18, 2010
Overview of using DMX (Data Mining Extensions) in Excel, SSMS (SQL Server Management Studio), BIDS (Business Intelligence Development Studio), and PowerShell
SQL Saturday 119 Chicago -- Enterprise Data Mining with SQL ServerMark Tabladillo
This presentation covers SQL Server 2012 data mining -- mostly demo (therefore not in slides) -- an earlier version is on video at http://channel9.msdn.com/Events/TechEd/NorthAmerica/2011/DBI326
SQL Saturday 109 -- Enterprise Data Mining with SQL ServerMark Tabladillo
Delivered at SQL Saturday 109 Silicon Valley, CA -- This presentation introduces SQL Server Data Mining (SSDM) for SQL Server Professionals based on the speaker's past presentation for Microsoft TechEd. Starting with SQL Server Management Studio (SSMS), the demo includes the interfaces important for professional development, including Business Intelligence Development Studio (BIDS), highlighting Integration Services, and PowerShell. The interactive demos are based on Microsoft's Contoso Retail sample data. Finally we will evaluate where Microsoft data mining can help you in a practical business environment, which may include Oracle and SAS.
SQL Saturday 108 -- Enterprise Data Mining with SQL ServerMark Tabladillo
Presented at SQL Saturday 108 Redmond, WA -- This presentation introduces SQL Server Data Mining (SSDM) for SQL Server Professionals based on the speaker's past presentation for Microsoft TechEd. Starting with SQL Server Management Studio (SSMS), the demo includes the interfaces important for professional development, including Business Intelligence Development Studio (BIDS), highlighting Integration Services, and PowerShell. The interactive demos are based on Microsoft's Contoso Retail sample data. Finally we will evaluate where Microsoft data mining can help you in a practical business environment, which may include Oracle and SAS.
SQL Saturday 79 Enterprise Data Mining for SQL Server 2008 R2Mark Tabladillo
This presentation introduces SQL Server Data Mining (SSDM) for SQL Server Professionals based on the speaker's past presentation for Microsoft TechEd. Starting with SQL Server Management Studio (SSMS), the demo includes the interfaces important for professional development, including Business Intelligence Development Studio (BIDS), highlighting Integration Services, and PowerShell. The interactive demos are based on Microsoft's Contoso Retail sample data. Finally we will evaluate where Microsoft data mining can help you in a practical business environment, which may include Oracle and SAS.
Microsoft Data Science Technologies: Back Office EditionMark Tabladillo
Microsoft provides several technologies in and around SQL Server which can be used for casual to serious data science. This presentation provides an authoritative overview of five major options: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, Microsoft Azure Machine Learning, and F#. Also included are tips on working with Python and R. These technologies have been used by the presenter in various companies and industries. This presentation will emphasize the back office story for supporting big data processing.
Microsoft Data Science Technologies: Architecture Edition 201509Mark Tabladillo
Microsoft provides several technologies in and around SQL Server which can be used for casual to serious data science. This presentation provides an authoritative overview of five major options: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, Microsoft Azure Machine Learning, and F#. Also included are tips on working with Python and R. These technologies have been used by the presenter in various companies and industries.
Why you should be mining your data and how to actually do it. Every company needs a rock star. We want it to be you. This session will give real world examples of data mining successes as well as walk you through how to get started down the path of data enlightenment, so that you too can say "I Am A Data Miner℠".
24 Hours of PASS -- Enterprise Data Mining with SQL ServerMark Tabladillo
This presentation introduces SQL Server Data Mining (SSDM) for SQL Server Professionals based on the speaker's past presentation for Microsoft TechEd. Starting with SQL Server Management Studio (SSMS), the demo includes the interfaces important for professional development, including Business Intelligence Development Studio (BIDS), highlighting Integration Services, and PowerShell. The interactive demos are based on Microsoft's Contoso Retail sample data. Finally we will evaluate where Microsoft data mining can help you in a practical business environment, which may include Oracle and SAS.
Basic Overview of Power BI Desktop and Excel and O365
◾What makes Power BI different from the Microsoft Office I have known for years?
◾What are the major Power BI features in Excel 2013 and 2010?
◾What are the major features for Power BI for Office 365?
◾How can our team license Power BI?
Microsoft and Revolution Analytics -- what's the add-value? 20150629Mark Tabladillo
Microsoft has been a leader in the enterprise analytics space for years. In 2014, Microsoft had already created R language functionality within Azure Machine Learning. On April 6, 2015, Microsoft and closed on a deal to acquire Revolution Analytics, a company focusing on scalable processing solutions initiated by the well-known R language. Many data science projects and initial demos do not need high-volume solutions: however, having a high-volume answer for the R language allows for planning or working toward the largest data science solutions.
This presentation describes the add-value for the Revolution Analytics acquisition. The talk covers 1) an overview of current data science technologies from Microsoft; 2) a description of the R language; 3) a brief review of the add-value for R with Azure Machine Learning, and 4) a description of the performance architecture and demo of the language constructs developed by Revolution Analytics. Most of the presentation will be focused on sections two and four. It is anticipated that these technologies will be partially if not fully integrated into SQL Server 2016.
Applied Enterprise Semantic Mining -- Charlotte 201410Mark Tabladillo
Text mining is projected to dominate data mining, and the reasons are evident: we have more text available than numeric data. Microsoft introduced a new technology to SQL Server 2014 called Semantic Search. This session's detailed description and demos give you important information for the enterprise implementation of Tag Index and Document Similarity Index, and will also provide a comparison between what semantic search is and what Delve does. The demos include a web-based Silverlight application, and content documents from Wikipedia. We'll also look at strategy tips for how to best leverage the new semantic technology with existing Microsoft data mining.
R is a free, open-source environment for statistical analysis and graphing. In its almost 20 years of existence, R has remained popular in both academic and business environments. The newer Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. This session outlines functional and performance differences between these two software packages. You’ll see demonstrations of best tips for integrating this software with Windows and walk away with guidelines for working with commercial software. A version of this presentation had 100 attendees at the PASS Business Analytics Conference in Chicago (April 2013), and 40 attendees for the PASS Virtual Business Analytics meeting (May 2013).
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.
How Big Companies plan to use Our Big Data 201610Mark Tabladillo
Underneath the shiny popular apps on tablets, smartphones, and entertainment channels are typically large cloud-based data centers. App developers leverage the cloud to provide advertisers with targeted sales opportunities, which has been accounting for an ongoing shift from paper to online media. This presentation will provide updated trends and statistics for 2016 on big data usage (based on consumer use), statistical concerns with big data, and the Microsoft big data story.
Required for certification, window functions are powerful analytic functions built into SQL Server. SQL Server 2005 introduced the core window ranking functions, and SQL Server 2012 added time and statistical percentage window functions. These functions allow for advanced variable creation, and are of direct benefit to people creating features for data science. This talk will also recommend further reading on this topic.
Data Mining Innovation with SQL Server 2014: SQL Saturday 328 Birmingham ALMark Tabladillo
This presentation includes a complex business analytics solution using elements across the Microsoft Business Intelligence technology. This talk will not have all the steps spelled out. Therefore you should prepare -- for example, by looking at the SQL Server 2014 Developer Kit -- since attendees will be expected to have working knowledge of SQL Server relational database, SSIS, SSAS, SSRS, SSMS and SSDT. At the end of the demonstration, you will be expected to participate in groups to promote one idea which builds on the demo, and everyone will be voting for their favorite groups. Come ready to create and celebrate your peers.
Document Classification using DMX in SQL Server Analysis ServicesMark Tabladillo
Presentation for SQL Saturday Raleigh NC, Septmber 18, 2010
Overview of using DMX (Data Mining Extensions) in Excel, SSMS (SQL Server Management Studio), BIDS (Business Intelligence Development Studio), and PowerShell
SQL Saturday 119 Chicago -- Enterprise Data Mining with SQL ServerMark Tabladillo
This presentation covers SQL Server 2012 data mining -- mostly demo (therefore not in slides) -- an earlier version is on video at http://channel9.msdn.com/Events/TechEd/NorthAmerica/2011/DBI326
SQL Saturday 109 -- Enterprise Data Mining with SQL ServerMark Tabladillo
Delivered at SQL Saturday 109 Silicon Valley, CA -- This presentation introduces SQL Server Data Mining (SSDM) for SQL Server Professionals based on the speaker's past presentation for Microsoft TechEd. Starting with SQL Server Management Studio (SSMS), the demo includes the interfaces important for professional development, including Business Intelligence Development Studio (BIDS), highlighting Integration Services, and PowerShell. The interactive demos are based on Microsoft's Contoso Retail sample data. Finally we will evaluate where Microsoft data mining can help you in a practical business environment, which may include Oracle and SAS.
SQL Saturday 108 -- Enterprise Data Mining with SQL ServerMark Tabladillo
Presented at SQL Saturday 108 Redmond, WA -- This presentation introduces SQL Server Data Mining (SSDM) for SQL Server Professionals based on the speaker's past presentation for Microsoft TechEd. Starting with SQL Server Management Studio (SSMS), the demo includes the interfaces important for professional development, including Business Intelligence Development Studio (BIDS), highlighting Integration Services, and PowerShell. The interactive demos are based on Microsoft's Contoso Retail sample data. Finally we will evaluate where Microsoft data mining can help you in a practical business environment, which may include Oracle and SAS.
SQL Saturday 79 Enterprise Data Mining for SQL Server 2008 R2Mark Tabladillo
This presentation introduces SQL Server Data Mining (SSDM) for SQL Server Professionals based on the speaker's past presentation for Microsoft TechEd. Starting with SQL Server Management Studio (SSMS), the demo includes the interfaces important for professional development, including Business Intelligence Development Studio (BIDS), highlighting Integration Services, and PowerShell. The interactive demos are based on Microsoft's Contoso Retail sample data. Finally we will evaluate where Microsoft data mining can help you in a practical business environment, which may include Oracle and SAS.
Microsoft Data Science Technologies: Back Office EditionMark Tabladillo
Microsoft provides several technologies in and around SQL Server which can be used for casual to serious data science. This presentation provides an authoritative overview of five major options: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, Microsoft Azure Machine Learning, and F#. Also included are tips on working with Python and R. These technologies have been used by the presenter in various companies and industries. This presentation will emphasize the back office story for supporting big data processing.
Microsoft Data Science Technologies: Architecture Edition 201509Mark Tabladillo
Microsoft provides several technologies in and around SQL Server which can be used for casual to serious data science. This presentation provides an authoritative overview of five major options: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, Microsoft Azure Machine Learning, and F#. Also included are tips on working with Python and R. These technologies have been used by the presenter in various companies and industries.
Why you should be mining your data and how to actually do it. Every company needs a rock star. We want it to be you. This session will give real world examples of data mining successes as well as walk you through how to get started down the path of data enlightenment, so that you too can say "I Am A Data Miner℠".
24 Hours of PASS -- Enterprise Data Mining with SQL ServerMark Tabladillo
This presentation introduces SQL Server Data Mining (SSDM) for SQL Server Professionals based on the speaker's past presentation for Microsoft TechEd. Starting with SQL Server Management Studio (SSMS), the demo includes the interfaces important for professional development, including Business Intelligence Development Studio (BIDS), highlighting Integration Services, and PowerShell. The interactive demos are based on Microsoft's Contoso Retail sample data. Finally we will evaluate where Microsoft data mining can help you in a practical business environment, which may include Oracle and SAS.
MySQL 8.0 is the latest Generally Available version of MySQL. This session will help you upgrade from older versions, understand what utilities are available to make the process smoother and also understand what you need to bear in mind with the new version and considerations for possible behavior changes and solutions.
The goal of this session to demystify and clarify the BizTalk Server migration process and help both implementation teams as decision-makers to be aware of all the considerations that need to be taken into account when planning your migration to BizTalk Server 2020 and hopefully helping you carry out these tasks.
How to find low-cost or free data science resources 202006Mark Tabladillo
There are many free or low-cost resources to become better trained in data science. None of these options equals a formal degree: but short of that scope, these other resources are helpful at least for keeping up with technology. This presentation will provide specific recommendations on free or low-cost resources based on the Team Data Science Process framework (business understanding, data engineering, modeling, deployment).
This presentation covers some of the major data science and AI announcements from the May 2020 Microsoft Build conference. Included in this talk are 1) Azure Synapse Link, 2) Responsible AI, 3) Project Bonsai & Project Moab, and 4) AI Models at Scale (deep learning with billions of parameters).
Microsoft has released Automated ML technologies for developers through ML.NET, Azure ML Service, and Azure Databricks. This presenter is a data scientist and Microsoft architect, and will give a comprehensive overview of the utility and use case of this automated technology for production solutions. The presentation includes code you can try now.
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
ML.NET 1.0 release is the first major milestone of a great journey that started in May 2018 when we released ML.NET 0.1 as open source. ML.NET is an open-source and cross-platform machine learning framework for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Recommendation, Image Classification and more.
This presentation provides an overview of the technology with demos run in a Deep Learning Virtual Machine running Windows Server 2016. Code examples are in C# and F# and run in Visual Studio Community 2019. This technology is ready for production implementation and runs on .NET Core.
This presentation is the first of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI.
This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
This presentation is the fourth of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
NimbusML enables data scientists to use ML.NET to train models in Azure Machine Learning or anywhere else they use Python. NimbusML provides state-of-the-art ML algorithms, transforms and components, aiming to make them useful for all developers, data scientists, and information workers and helpful in all products, services and devices. The components are authored by the team members, as well as numerous contributors from MSR, CISL, Bing and other teams at Microsoft. NimbusML is interoperable with scikit-learn estimators and transforms, while adding a suite of highly optimized algorithms written in C++ and C# for speed and performance.
The trained machine learning model can be used in a .NET application with ML.NET. This presentation will outline the features of NimbusML and provide a notebook-based demonstration using Azure Notebooks.
This presentation is the third of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
201906 02 Introduction to AutoML with ML.NET 1.0Mark Tabladillo
ML.NET 1.0 release is the first major milestone of a great journey that started in May 2018 when we released ML.NET 0.1 as open source. ML.NET is an open-source and cross-platform machine learning framework for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Recommendation, Image Classification and more.
“Automated ML” is a collection of new technologies from Microsoft to enhance the data science development process. Still in preview, Auto ML for ML.NET 1.0 will be demonstrated in a Deep Learning Virtual Machine running Windows Server 2016. Code examples are in C# and run in Visual Studio Community 2019.
This presentation is the second of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
This presentation focuses on the value proposition for Azure Databricks for Data Science. First, the talk includes an overview of the merits of Azure Databricks and Spark. Second, the talk includes demos of data science on Azure Databricks. Finally, the presentation includes some ideas for data science production.
201905 Azure Certification DP-100: Designing and Implementing a Data Science ...Mark Tabladillo
Microsoft has several Azure certifications including DP-100 (Designing and Implementing a Data Science Solution on Azure). Until this month, the exam had been in beta: however, the presenter has just passed the exam (first try). The purpose of this event is to share a viewpoint on how to study for the exam. Today, there are multiple ways to develop and deliver and deploy R or Python or Spark or deep learning models on Azure. The differences are important for this exam.
Big Data Advanced Analytics on Microsoft Azure 201904Mark Tabladillo
This talk summarizes key points for big data advanced analytics on Microsoft Azure. First, there is a review of the major technologies. Second, there is a series of technology demos (focusing on VMs, Databricks and Azure ML Service). Third, there is some advice on using the Team Data Science Process to help plan projects. The deck has web resources recommended. This presentation was delivered at the Global Azure Bootcamp 2019, Atlanta GA location (Alpharetta Avalon).
This presentation anchors best practices for Enterprise Data Science based on Microsoft's "Team Data Science Process". The talk includes introducing the concepts, describing some real-world advice for project planning, and discusses typical titles of professionals who make enterprise data science successful. These techniques also apply for AI (artificial intelligence), deep learning, machine learning, and advanced analytics.
Training of Python scikit-learn models on AzureMark Tabladillo
This intermediate-level presentation covers latest Azure technology for deploying Python sci-kit models on Azure. The presentation is a demo using a Microsoft Data Science Virtual Machine (DSVM), Visual Studio Code, Azure Machine Learning Service, Azure Machine Learning Compute, Azure Storage Blobs, and Azure Container Registry to train a model from a Python 3 Anaconda environment.
The presentation will include an architectural diagram and downloadable code from Github.
YouTube recording at https://www.youtube.com/watch?v=HyzbxHBpAbg&feature=youtu.be
Big Data Adavnced Analytics on Microsoft AzureMark Tabladillo
This presentation provides a survey of the advanced analytics strengths of Microsoft Azure from an enterprise perspective (with these organizations being the bulk of big data users) based on the Team Data Science Process. The talk also covers the range of analytics and advanced analytics solutions available for developers using data science and artificial intelligence from Microsoft Azure.
Power BI has become an increasingly important data analytics tool. This presentation focuses on the advanced analytics options currently available in Power BI. Attendees to this talk will see:
· Microsoft’s perspective on advanced analytics development: the Team Data Science Process
· What the general options are for advanced analytics on Azure
· What the specific native advanced analytics capabilities are in Power BI
· Some ideas on pairing Power BI with other technologies in advanced analytics architectures
Microsoft Cognitive Toolkit (Atlanta Code Camp 2017)Mark Tabladillo
The Microsoft Cognitive Toolkit (CNTK) is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. In this directed graph, leaf nodes represent input values or network parameters, while other nodes represent matrix operations upon their inputs.
The objectives of this presentation is to 1) describe what CNTK is, 2) present a comparative evaluation with similar technologies, 3) outline potential applications, and 4) demonstrate the technology with Jupyter Python examples.
Machine learning services with SQL Server 2017Mark Tabladillo
SQL Server 2017 introduces Machine Learning Services with two independent technologies: R and Python. The purpose of this presentation is 1) to describe major features of this technology for technology managers; 2) to outline use cases for architects; and 3) to provide demos for developers and data scientists.
Microsoft Technologies for Data Science 201612Mark Tabladillo
Delivered to SQL Saturday BI Edition -- Atlanta, GA
Microsoft provides several technologies in and around Azure which can be used for casual to serious data science. This presentation provides an overview of the major Microsoft options for both on-premise and cloud-based data science (and hybrid). These technologies have been used by the presenter in various companies and industries, both as a Microsoft consultant and previously independent consultant. As well, the speaker provides insights into data science careers, information which helps imply where the business will likely be for consultants and partners.
Insider's guide to azure machine learning 201606Mark Tabladillo
Presented for the Atlanta Azure Meetup group.
Microsoft has introduced a new technology for developing analytics applications in the cloud. The presenter has an insider's perspective, having actively provided feedback to the Microsoft team which has been developing this technology over the past 2 years.
This session will
1) provide an introduction to the Azure technology including licensing
2) provide demos of using Microsoft R Open with AzureML
3) provide demos of Python 3 and AzureML
4) provide best practices for developing applications with Azure Machine Learning
This technology is now within "Cortana Intelligence".
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
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.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
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.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.