comparison of Excel add-ins and other solutions for implementing data mining or machine learning solutions on the Microsoft stack - includes coverage of XLMiner, Analysis Services Data Mining and PredixionSoftware
Finding new Customers using D&B and Excel Power QueryLynn Langit
Screencast which shows how to use Excel Power Query with D&B APIs to get company DUNS numbers and other company information from the Windows Azure Marketplace.
How R Developers Can Build and Share Data and AI Applications that Scale with...Databricks
Historically it has been challenging for R developers to build and share data products that use Apache Spark. In this talk, learn how you can publish Shiny apps that leverage the scale and speed of Databricks, Spark and Delta Lake, so your stakeholders can better leverage insights from your data in their decision making.
During this presentation, after walking through a few ways to use MLflow on Azure directly, we'll cover how upcoming solutions from our group leverage MLflow for core functionality. BenchML is a new repository that aims to provide consumers of prebuilt ML endpoints visibility into the performance of each public offering for a given dataset as well as comparing results across multiple offerings. Using MLflow, BenchML is able to remain cloud-agnostic and offer a delightful local experience while leveraging the aforementioned integration to provide Azure users with a fully managed experience.
Speaker Bio: Akshaya is an engineer in the AI Platform at Microsoft, having released both GA versions of Azure Machine Learning over the years and the OSS repo MMLSpark. As the recent version of Azure ML pivoted to become more of an open platform rather than a managed product, his focus has shifted outward for open-source platform definitions for cloud-scale implementations and focused on MLflow for the Azure ML managed tracking store.
This talk was presented at the Bay Area MLflow Meetup at Databricks HQs in San Francisco: https://www.meetup.com/Bay-Area-MLflow/events/266614106/
comparison of Excel add-ins and other solutions for implementing data mining or machine learning solutions on the Microsoft stack - includes coverage of XLMiner, Analysis Services Data Mining and PredixionSoftware
Finding new Customers using D&B and Excel Power QueryLynn Langit
Screencast which shows how to use Excel Power Query with D&B APIs to get company DUNS numbers and other company information from the Windows Azure Marketplace.
How R Developers Can Build and Share Data and AI Applications that Scale with...Databricks
Historically it has been challenging for R developers to build and share data products that use Apache Spark. In this talk, learn how you can publish Shiny apps that leverage the scale and speed of Databricks, Spark and Delta Lake, so your stakeholders can better leverage insights from your data in their decision making.
During this presentation, after walking through a few ways to use MLflow on Azure directly, we'll cover how upcoming solutions from our group leverage MLflow for core functionality. BenchML is a new repository that aims to provide consumers of prebuilt ML endpoints visibility into the performance of each public offering for a given dataset as well as comparing results across multiple offerings. Using MLflow, BenchML is able to remain cloud-agnostic and offer a delightful local experience while leveraging the aforementioned integration to provide Azure users with a fully managed experience.
Speaker Bio: Akshaya is an engineer in the AI Platform at Microsoft, having released both GA versions of Azure Machine Learning over the years and the OSS repo MMLSpark. As the recent version of Azure ML pivoted to become more of an open platform rather than a managed product, his focus has shifted outward for open-source platform definitions for cloud-scale implementations and focused on MLflow for the Azure ML managed tracking store.
This talk was presented at the Bay Area MLflow Meetup at Databricks HQs in San Francisco: https://www.meetup.com/Bay-Area-MLflow/events/266614106/
Operationalizing Machine Learning at Scale at StarbucksDatabricks
As ML-driven innovations are propelled by the Self-Service capabilities in the Enterprise Data and Analytics Platform, teams face a significant entry barrier and productivity issues in moving from POCs to Operating ML-powered apps at scale in production.
From Idea to Model: Productionizing Data Pipelines with Apache AirflowDatabricks
When supporting a data science team, data engineers are tasked with building a platform that keeps a wide range of stakeholders happy. Data scientists want rapid iteration, infrastructure engineers want monitoring and security controls, and product owners want their solutions deployed in time for quarterly reports.
Managing your ML lifecycle with Azure Databricks and Azure MLParashar Shah
Machine learning development has new complexities beyond software development. There are a myriad of tools and frameworks which make it hard to track experiments, reproduce results and deploy machine learning models. Learn how you can accelerate and manage your end-to-end machine learning lifecycle on Azure Databricks using MLflow and Azure ML to reliably build, share and deploy machine learning applications using Azure Databricks. This is based on our talk at //build - https://www.youtube.com/watch?v=pe_OH07wAYc and https://mybuild.techcommunity.microsoft.com/sessions/76976
Short introduction to different options for ETL & ELT in the Cloud with Microsoft Azure. This is a small accompanying set of slides for my presentations and blogs on this topic
Machine Learning Data Lineage with MLflow and Delta LakeDatabricks
Many organizations using machine learning are facing challenges storing and versioning their complex ML data as well as a large number of models generated from those data. To simplify this process, organizations tend to start building their customized ‘ML platforms.’
Building Data Lakes with Apache AirflowGary Stafford
Build a simple Data Lake on AWS using a combination of services, including Amazon Managed Workflows for Apache Airflow (Amazon MWAA), AWS Glue, AWS Glue Studio, Amazon Athena, and Amazon S3.
Blog post and link to the video: https://garystafford.medium.com/building-a-data-lake-with-apache-airflow-b48bd953c2b
Integration Monday - Analysing StackExchange data with Azure Data LakeTom Kerkhove
Big data is the new big thing where storing the data is the easy part. Gaining insights in your pile of data is something different.
Based on a data dump of the well-known StackExchange websites, we will store & analyse 150+ GB of data with Azure Data Lake Store & Analytics to gain some insights about their users. After that we will use Power BI to give an at a glance overview of our learnings.
If you are a developer that is interested in big data, this is your time to shine! We will use our existing SQL & C# skills to analyse everything without having to worry about running clusters.
Bridging the Completeness of Big Data on DatabricksDatabricks
Data completeness is key for building any machine learning and deep learning model. The reality is that outliers and nulls widely exist in the data. The traditional methods of using fixed values or statistical metrics (min, max and mean) does not consider the relationship and patterns within the data. Most time it offers poor accuracy and would introduce additional outliers. Also, given our large data size, the computation is an extremely time-consuming process and a lot of time it could be constrained by the limited resource on local computer.
To address those issues, we have developed a new approach that will first leverage the similarity within our data points based on the nature of data source then using a collaborative AI model to fill null values and correct outliers.
In this talk, we will walk through the way we use a distributed framework to partition data by KDB tree for neighbor discovery and a collaborative filtering AI technology to fill the missing values and correct outliers. In addition, we will demonstrate how we reply on delta lake and MLflow for data and model management.
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
Level: Intermediate
Speakers:
Jay Formosa - Solutions Architect, AWS
Aser Moustafa - Data Warehouse Specialist Solutions Architect, AWS
Saunak Chandra - Partner Solutions Architect, Redshift Specialist, AWS
Using Premium Data - for Business AnalystsLynn Langit
Understanding use cases for free and premium data in Big Data scenarios - uses D&B, Melissa, Quandl and others.
Shown using integration with Microsoft Excel and other tools.
Stargate, the gateway for some multi-models data APIData Con LA
Data Con LA 2020
Description
Join us to learn about Stargate! Stargate is a data gateway deployed between client applications and a database. It's built with extensibility as a first-class citizen and makes it easy to use a database for any application workload by adding plugin support for new APIs, data types, and access methods. After detailing the architecture and ideas behind the frameworks we will demo the creation of REST and GraphQL APIs on top of Cassandra through simple configuration. Bring back home a working sample !
Speaker
Cedrick Lunven, Director of Developer Advocacy, Datastax
Hadoop meets Agile! - An Agile Big Data ModelUwe Printz
Big Data projects are a struggle, not only on the technical side but also on the organizational side. In this talk the author shares his experience and opinions from almost 5 years of Big Data projects and develops an Agile Big Data Model which reflects his ideas on how Big Data projects can be successful, even in large companies.
Talk held at the crossover meetup of the "Agile Stammtisch Rhein-Main" and the "Hadoop & Spark User Group Rhein-Main" at codecentric AG on 31.01.2017.
Operationalizing Machine Learning at Scale at StarbucksDatabricks
As ML-driven innovations are propelled by the Self-Service capabilities in the Enterprise Data and Analytics Platform, teams face a significant entry barrier and productivity issues in moving from POCs to Operating ML-powered apps at scale in production.
From Idea to Model: Productionizing Data Pipelines with Apache AirflowDatabricks
When supporting a data science team, data engineers are tasked with building a platform that keeps a wide range of stakeholders happy. Data scientists want rapid iteration, infrastructure engineers want monitoring and security controls, and product owners want their solutions deployed in time for quarterly reports.
Managing your ML lifecycle with Azure Databricks and Azure MLParashar Shah
Machine learning development has new complexities beyond software development. There are a myriad of tools and frameworks which make it hard to track experiments, reproduce results and deploy machine learning models. Learn how you can accelerate and manage your end-to-end machine learning lifecycle on Azure Databricks using MLflow and Azure ML to reliably build, share and deploy machine learning applications using Azure Databricks. This is based on our talk at //build - https://www.youtube.com/watch?v=pe_OH07wAYc and https://mybuild.techcommunity.microsoft.com/sessions/76976
Short introduction to different options for ETL & ELT in the Cloud with Microsoft Azure. This is a small accompanying set of slides for my presentations and blogs on this topic
Machine Learning Data Lineage with MLflow and Delta LakeDatabricks
Many organizations using machine learning are facing challenges storing and versioning their complex ML data as well as a large number of models generated from those data. To simplify this process, organizations tend to start building their customized ‘ML platforms.’
Building Data Lakes with Apache AirflowGary Stafford
Build a simple Data Lake on AWS using a combination of services, including Amazon Managed Workflows for Apache Airflow (Amazon MWAA), AWS Glue, AWS Glue Studio, Amazon Athena, and Amazon S3.
Blog post and link to the video: https://garystafford.medium.com/building-a-data-lake-with-apache-airflow-b48bd953c2b
Integration Monday - Analysing StackExchange data with Azure Data LakeTom Kerkhove
Big data is the new big thing where storing the data is the easy part. Gaining insights in your pile of data is something different.
Based on a data dump of the well-known StackExchange websites, we will store & analyse 150+ GB of data with Azure Data Lake Store & Analytics to gain some insights about their users. After that we will use Power BI to give an at a glance overview of our learnings.
If you are a developer that is interested in big data, this is your time to shine! We will use our existing SQL & C# skills to analyse everything without having to worry about running clusters.
Bridging the Completeness of Big Data on DatabricksDatabricks
Data completeness is key for building any machine learning and deep learning model. The reality is that outliers and nulls widely exist in the data. The traditional methods of using fixed values or statistical metrics (min, max and mean) does not consider the relationship and patterns within the data. Most time it offers poor accuracy and would introduce additional outliers. Also, given our large data size, the computation is an extremely time-consuming process and a lot of time it could be constrained by the limited resource on local computer.
To address those issues, we have developed a new approach that will first leverage the similarity within our data points based on the nature of data source then using a collaborative AI model to fill null values and correct outliers.
In this talk, we will walk through the way we use a distributed framework to partition data by KDB tree for neighbor discovery and a collaborative filtering AI technology to fill the missing values and correct outliers. In addition, we will demonstrate how we reply on delta lake and MLflow for data and model management.
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
Level: Intermediate
Speakers:
Jay Formosa - Solutions Architect, AWS
Aser Moustafa - Data Warehouse Specialist Solutions Architect, AWS
Saunak Chandra - Partner Solutions Architect, Redshift Specialist, AWS
Using Premium Data - for Business AnalystsLynn Langit
Understanding use cases for free and premium data in Big Data scenarios - uses D&B, Melissa, Quandl and others.
Shown using integration with Microsoft Excel and other tools.
Stargate, the gateway for some multi-models data APIData Con LA
Data Con LA 2020
Description
Join us to learn about Stargate! Stargate is a data gateway deployed between client applications and a database. It's built with extensibility as a first-class citizen and makes it easy to use a database for any application workload by adding plugin support for new APIs, data types, and access methods. After detailing the architecture and ideas behind the frameworks we will demo the creation of REST and GraphQL APIs on top of Cassandra through simple configuration. Bring back home a working sample !
Speaker
Cedrick Lunven, Director of Developer Advocacy, Datastax
Hadoop meets Agile! - An Agile Big Data ModelUwe Printz
Big Data projects are a struggle, not only on the technical side but also on the organizational side. In this talk the author shares his experience and opinions from almost 5 years of Big Data projects and develops an Agile Big Data Model which reflects his ideas on how Big Data projects can be successful, even in large companies.
Talk held at the crossover meetup of the "Agile Stammtisch Rhein-Main" and the "Hadoop & Spark User Group Rhein-Main" at codecentric AG on 31.01.2017.
Developing and deploying AI solutions on the cloud using Team Data Science Pr...Debraj GuhaThakurta
Presented at: Global Big AI Conference, Santa Clara, Jan 2018 Developing and deploying AI solutions on the cloud using Team Data Science Process (TDSP) and Azure Machine Learning (AML)
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...Ali Alkan
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi | Automating Machine Learning, Artificial Intelligence, and Data Science | Guided Analytics
Talk by Borys Biletskyy at Data Science Amsterdam and Data Science Utrecht. The talk is dedicated to the role of Machine Learning Engineer and how it can improve the success rate of Data Science projects.
How to build your own Delve: combining machine learning, big data and SharePointJoris Poelmans
You are experiencing the benefits of machine learning everyday through product recommendations on Amazon & Bol.com, credit card fraud prevention, etc… So how can we leverage machine learning together with SharePoint and Yammer. We will first look into the fundamentals of machine learning and big data solutions and next we will explore how we can combine tools such as Windows Azure HDInsight, R, Azure Machine Learning to extend and support collaboration and content management scenarios within your organization.
Building enterprise advance analytics platformHaoran Du
By Raymond Fu - Practice Architect
This lecture talks about the best practices in building an advanced analytics platform to help companies apply machine learning, deep learning and data science to their structured and unstructured data.
At Southern California Data Science Conference Sept.25.2016 at USC
http://socaldatascience.org/
http://www.datalaus.com/en/
A practical guidance of the enterprise machine learning Jesus Rodriguez
This session provides an analysis of the machine learning market in the enterprise. The analysis includes vendors, platforms and best practices that should be considered by companies implementing data science solutions at an enterprise scale
Predictive Analysis using Microsoft SQL Server R ServicesFisnik Doko
R is rapidly becoming the leading language in Data Science and statistics.
This session will show how Microsoft SQL Server can help meet an increasingly “predictive” world by supporting the R language inside the database.
Demonstration using R and SQL Server Services in rental industry.
The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
Watch: https://bit.ly/2DYsUhD
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spent most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Attend this webinar and learn:
- How data virtualization can accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- How popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc. integrate with Denodo
- How you can use the Denodo Platform with large data volumes in an efficient way
- How Prologis accelerated their use of Machine Learning with data virtualization
R+Hadoop - Ask Bigger (and New) Questions and Get Better, Faster AnswersRevolution Analytics
The business cases for Hadoop can be made on the tremendous operational cost savings that it affords. But why stop there? The integration of R-powered analytics in Hadoop presents a totally new value proposition. Organizations can write R code and deploy it natively in Hadoop without data movement or the need to write their own MapReduce. Bringing R-powered predictive analytics into Hadoop will accelerate Hadoop’s value to organizations by allowing them to break through performance and scalability challenges and solve new analytic problems. Use all the data in Hadoop to discover more, grow more quickly, and operate more efficiently. Ask bigger questions. Ask new questions. Get better, faster results and share them.
Data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data.
For More Details Visit: https://datamites.com/data-science-course-training-chennai/
Similar to Microsoft Machine Learning Smackdown (20)
deck from talk at YOW Data in Sydney, covers VariantSpark, custom Apache Spark Machine Learning library and also GT-Scan2 using AWS Lambda architecture for bioinformatics
VariantSpark - a Spark library for genomicsLynn Langit
VariantSpark a customer Apache Spark library for genomic data. Customer wide random forest machine learning algorithm, designed for workloads with millions of features.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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:
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
3. Agenda
Goal: Survey ML tools/methods that you can actually use on the Microsoft stack
• Definitions
• Tools I – Understanding 3rd party Excel Machine Learning Add-ins
• Tools II – Using the Microsoft SQL Server SSAS & Data Mining Add-ins
• Tools III – Using Predixion Software
• Recap and Call To Action
3
4. Terms
Goal: Create common definitions of key terms
• Business Analytics
• Query
• Aggregation
• Predictive Analytics
• Machine Learning
• Statistics
• Unsupervised Data Mining
• Supervised Data Mining
• Other
4
5. What does the market look like now?
5
57%28%
10%
5%
Regular Analytics
Unsupervised DM
Supervised DM
Machine Learning
10. About 3rd party Excel Machine Learning Add-ins
What are they?
Toolbars in Excel – many different offerings
• XLMiner
• StatsMiner
• XLStat
• RExcel
10
Important: All of these tools assume expert statistical knowledge
12. About the Data Mining Add-ins For Excel
What is it?
Free add-ins which add menus to use SSAS Analysis Services Data Mining
• Table Analysis Tools for Excel
• Use mining models with Excel data or external data
• Data Mining Client for Excel
• Create/test/explore/manage Mining Models
• Data Mining Templates for Visio
• Render/share mining models as Visio Drawings
12
Important: Use requires connection to SQL Server 2012 SSAS
14. Checking Understanding…
Data Mining Structures
• Containers for cleansed source data
Data Mining Models
• Child containers for source data plus one
mining algorithm
• SSAS Algorithms - Clustering, Time
Series Prediction, Market-Basket
Analysis, Text Mining and Neural
Networks
Model Verification, Processing and Usage
Tools
• Model query, Model processing
14
15. About Predixion Software
What is it?
Suite of tools for predictive analytics
• Insight Now
• Use mining models with Excel data or external data
• Insight Analytics
• Create/test/explore/manage Mining Models
• Insight Workbench
• Prepare data for model creation
• Web-based Viewers and Tools
15
Important: Runs as EITHER connected to SSAS on premise OR
Connected to Predixion’s cloud-based servers
18. Understanding options…
18
Add-in
Server
Required
Complexity
of install
Other
Cost of
Add-in
Cost of
Solution
XLMiner none easy Assumes stats expertise $$ $$
RExcel none easy Assumes R expertise $ $
Data Mining Add-ins SQL Server SSAS medium Designed for single user 0 $$$
Predixion on premise SQL Express easy Requires local R install 0 $$-$$$
Predixion on premise SQL Server SSAS medium Your data is stored locally 0 $$$$
Predixion cloud none easy Supports SSAS Data
Mining AND R Language
0 $$-$$$
19. 19
Machine Learning Skills
Data Scientist
Store
Clean
Aggregate
ML Engineer
Selects Libraries
Applies
Algorithms
Creates
Solutions
ML Researcher
Creates Algorithms
20. Learning Paths – ML Developers
• Learn a language… DMX, PAX, R, Mahout, Julia
• Pick your IDE, tools… SSAS, Predixion, R-Studio, Weka
• Pick a problem space… Marketing, Health, Financial
• Find (purchase)/gather/prepare some data…
GO!
(Visualize results)
20
21. Call to Action – ML Decision Makers
• Pick one or more solutions
• Gather source data
• Prepare source data
• Try out some data mining
algorithms
Evaluate it Understand it
• Tooling
• Learning
• Data gathering/ preparation
• Storage / hosting
• Results
21
24. Session Evaluations
Submit by 5pmFriday May
9 to WIN prizes
Your feedback is
important and valuable.
ways to access
Go to
passbac2014/evals
Download the PASS EVENT
App from your App Store
and search: PASS BAC
2014
Follow the QR code link
displayed on session
signage throughout the
conference venue and in
the program guide
25. for attending this session and
the PASS Business Analytics
Conference 2014
May 7-9, 2014 | San Jose, CA
Thank
You
SoCalDevGal on