Amazon Machine Learning - Session of Barbara Pogorzelska,
Technical Program Manager, Amazon Web Services - hold in the AWS Pop-up Loft in Berlin
Find out more about Amazon Machine Learning: https://aws.amazon.com/de/machine-learning/
Amazon Machine Learning: Empowering Developers to Build Smart ApplicationsAmazon Web Services
Amazon Machine Learning (Amazon ML) makes it easy for developers of all skill levels to use machine learning (ML) technology. The powerful Amazon ML algorithms create ML models by finding patterns in your existing data. Then Amazon ML uses these models to process new data and generate predictions for your application. Amazon ML can ingest data from Amazon S3, Amazon Redshift, or Amazon RDS. In this session, we will demonstrate how Amazon ML can be used to build an ML model and deploy it to production, and how to query this model from within a smart application.
Amazon Machine Learning: Empowering Developers to Build Smart ApplicationsAmazon Web Services
Amazon Machine Learning (Amazon ML) makes it easy for developers of all skill levels to use machine learning (ML) technology. The powerful Amazon ML algorithms create ML models by finding patterns in your existing data. Then Amazon ML uses these models to process new data and generate predictions for your application. Amazon ML can ingest data from Amazon S3, Amazon Redshift, or Amazon RDS. In this session, we will demonstrate how Amazon ML can be used to build an ML model and deploy it to production, and how to query this model from within a smart application.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to get predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure.
AWS April Webinar Series - Introduction to Amazon Machine LearningAmazon Web Services
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning’s powerful algorithms create machine learning (ML) models by finding patterns in your existing data. Then, the service uses these models to process new data and generate predictions for your application. Amazon Machine Learning can ingest data from Amazon S3, Amazon Redshift or Amazon RDS. In this webinar, we will demonstrate how Amazon Machine Learning can be used to build an ML model, deploy it to production, and query this model from within a smart application.
Learning Objectives:
• Understanding machine learning technology
• Building machine learning models with Amazon Machine Learning
• Deploying and querying models
• Tips for getting started with Amazon Machine Learning
Who Should Attend: • Developers, Devops Engineers, IT Operations Professionals
In this presentation, learn how an end-to-end smart application can be built in the AWS cloud. We will demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We will then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We will walk you through the process flow and architecture, demonstrate outcomes, and then dive into the code for implementation. In this session, you will learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
Presented by: Guy Ernest, Principal Business Development Manager, Amazon Web Services
Customer Guest: Pim Vernooij, Partner, Lab Digital
Amazon Machine Learning: Empowering Developers to Build Smart ApplicationsAmazon Web Services
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning’s powerful algorithms create machine learning (ML) models by finding patterns in your existing data. Then, the service uses these models to process new data and generate predictions for your application. Amazon Machine Learning can ingest data from Amazon S3, Amazon Redshift or Amazon RDS. In this session, we will demonstrate how Amazon Machine Learning can be used to build an ML model, deploy it to production, and query this model from within a smart application.
(BDT302) Real-World Smart Applications With Amazon Machine LearningAmazon Web Services
Have you always wanted to add predictive capabilities to your application, but haven’t been able to find the time or the right technology to get started? In this session, learn how an end-to-end smart application can be built in the AWS cloud. We demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We walk you through the process flow and architecture, demonstrate outcomes, and then dive into the code for implementation. In this session, you learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
Amazon Machine Learning: Empowering Developers to Build Smart ApplicationsAmazon Web Services
Amazon Machine Learning (Amazon ML) makes it easy for developers of all skill levels to use machine learning (ML) technology. The powerful Amazon ML algorithms create ML models by finding patterns in your existing data. Then Amazon ML uses these models to process new data and generate predictions for your application. Amazon ML can ingest data from Amazon S3, Amazon Redshift, or Amazon RDS. In this session, we will demonstrate how Amazon ML can be used to build an ML model and deploy it to production, and how to query this model from within a smart application.
Amazon Machine Learning: Empowering Developers to Build Smart ApplicationsAmazon Web Services
Amazon Machine Learning (Amazon ML) makes it easy for developers of all skill levels to use machine learning (ML) technology. The powerful Amazon ML algorithms create ML models by finding patterns in your existing data. Then Amazon ML uses these models to process new data and generate predictions for your application. Amazon ML can ingest data from Amazon S3, Amazon Redshift, or Amazon RDS. In this session, we will demonstrate how Amazon ML can be used to build an ML model and deploy it to production, and how to query this model from within a smart application.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to get predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure.
AWS April Webinar Series - Introduction to Amazon Machine LearningAmazon Web Services
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning’s powerful algorithms create machine learning (ML) models by finding patterns in your existing data. Then, the service uses these models to process new data and generate predictions for your application. Amazon Machine Learning can ingest data from Amazon S3, Amazon Redshift or Amazon RDS. In this webinar, we will demonstrate how Amazon Machine Learning can be used to build an ML model, deploy it to production, and query this model from within a smart application.
Learning Objectives:
• Understanding machine learning technology
• Building machine learning models with Amazon Machine Learning
• Deploying and querying models
• Tips for getting started with Amazon Machine Learning
Who Should Attend: • Developers, Devops Engineers, IT Operations Professionals
In this presentation, learn how an end-to-end smart application can be built in the AWS cloud. We will demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We will then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We will walk you through the process flow and architecture, demonstrate outcomes, and then dive into the code for implementation. In this session, you will learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
Presented by: Guy Ernest, Principal Business Development Manager, Amazon Web Services
Customer Guest: Pim Vernooij, Partner, Lab Digital
Amazon Machine Learning: Empowering Developers to Build Smart ApplicationsAmazon Web Services
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning’s powerful algorithms create machine learning (ML) models by finding patterns in your existing data. Then, the service uses these models to process new data and generate predictions for your application. Amazon Machine Learning can ingest data from Amazon S3, Amazon Redshift or Amazon RDS. In this session, we will demonstrate how Amazon Machine Learning can be used to build an ML model, deploy it to production, and query this model from within a smart application.
(BDT302) Real-World Smart Applications With Amazon Machine LearningAmazon Web Services
Have you always wanted to add predictive capabilities to your application, but haven’t been able to find the time or the right technology to get started? In this session, learn how an end-to-end smart application can be built in the AWS cloud. We demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We walk you through the process flow and architecture, demonstrate outcomes, and then dive into the code for implementation. In this session, you learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure. More information: https://aws.amazon.com/machine-learning/
Einführung in Amazon Machine Learning - AWS Machine Learning Web DayAWS Germany
Vortrag "Einführung in Amazon Machine Learning " von Oliver Arafat beim AWS Machine Learning Web Day. Alle Videos und Präsentationen finden Sie hier: http://amzn.to/1XP3dz9
In this session from the London AWS Summit 2015 Tech Track Replay, AWS Technical Evangelist Ian Massingham introduces the new Amazon Machine Learning service.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure.
Amazon Machine Learning Case Study: Predicting Customer ChurnAmazon Web Services
We do a deeper dive into Amazon Machine Learning, using a specific business problem as an example – predicting if the customer is about to leave your service, also known as customer churn. We examine several practical aspects of building and using a model, including the use of the recipe language for training data manipulation and modeling the costs of false positive/negative errors.
Vortrag "Real-World Smart Applications with Amazon Machine Learning" von Alex Ingerman beim AWS Machine Learning Web Day. Alle Videos und Präsentationen finden Sie hier: http://amzn.to/1XP3dz9
AWS November Webinar Series - Advanced Analytics with Amazon Redshift and the...Amazon Web Services
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology and Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. The combination of the two can provide a solution to power advanced analytics for not only what has happened in the past, but make intelligent predictions about the future. Please join this webinar to learn how get the most value from your data for your data driven business.
Learning Objectives:
How to scale your Redshift queries with user-defined functions (UDFs)
How to apply Machine learning to historical data in Amazon Redshift
How to visualize your data with Amazon QuickSight
Present a reference architecture for advanced analytics
Who Should Attend:
Application developers looking to add UDFs, or predictive analytics to their applications, database administrators that need to meet the demand of data driven organizations, decision makers looking to derive more insight from their data
From my session at DevTernity in Riga, December 1st 2015. Have you always wanted to add predictive capabilities to your application, but haven’t been able to find the time or the right technology to get started? Everybody wants to build smart apps, but only a few are Data Scientists. We had the same issue inside Amazon, so we created a Machine Learning engine that Developers can easily use. The same approach is now available in the AWS cloud. We demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We walk you through the process flow and architecture, demonstrate outcomes, and then dive into the implementation. In this session, you learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
Exploring the Business Use Cases for Amazon Machine Learning - June 2017 AWS ...Amazon Web Services
Learning Objectives:
- Learn how to integrate Amazon Machine Learning with applications
- Learn how to train a model using Amazon Machine Learning - Learn how to process semi-structured log data in real-time with Amazon Machine Learning
Machine learning has been used to provide more accurate predictions than hardcoded business logic using available data. For our customers, Amazon Machine Learning is being used from helping restaurant owners, as with Upserve, to determine the right staffing level on a night; to providing more accurate cost estimates in the insurance industry, as with BuildFax. In this tech talk, we'll cover the basics of how to get started with Amazon Machine Learning, and go through an example of how to perform real-time classification of log data using Amazon Machine Learning.
Amazon DynamoDB is a fully managed NoSQL database service for applications that need consistent, single-digit millisecond latency at any scale. This talk explores DynamoDB capabilities and benefits in detail and discusses how to get the most out of your DynamoDB database. We go over schema design best practices with DynamoDB across multiple use cases, including gaming, AdTech, IoT, and others. We also explore designing efficient indexes, scanning, and querying, and go into detail on a number of recently released features, including JSON document support, Streams, and more.
AWS Webcast - Amazon Elastic Map Reduce Deep Dive and Best PracticesAmazon Web Services
Amazon Elastic MapReduce (EMR) is one of the largest Hadoop operators in the world. Since its launch five years ago, our customers have launched more than 15 million Hadoop clusters inside of EMR. In this webinar, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters and other Amazon EMR architectural patterns. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We also share best practices to keep your Amazon EMR cluster cost efficient.
This webinar recording will explain how to get started with Amazon Elastic MapReduce (EMR). EMR enables fast processing of large structured or unstructured datasets, and in this webinar we'll demonstrated how to setup an EMR job flow to analyse application logs, and perform Hive queries against it. We'll review best practices around data file organisation on Amazon Simple Storage Service (S3), how clusters can be started from the AWS web console and command line, and how to monitor the status of a Map/Reduce job. The security configuration that allows direct access to the Amazon EMR cluster in interactive mode will be shown, and we'll see how Hive provides a SQL like environment, while allowing you to dynamically grow and shrink the amount of compute used for powerful data processing activities.
Amazon EMR YouTube Recording: http://youtu.be/gSPh6VTBEbY
Amazon Elastic MapReduce is one of the largest Hadoop operators in the world. Since its launch five years ago, AWS customers have launched more than 5.5 million Hadoop clusters.
In this talk, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters and other Amazon EMR architectural patterns. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We also share best practices to keep your Amazon EMR cluster cost efficient.
Speakers:
Ian Meyers, AWS Solutions Architect
Ian McDonald, IT Director, SwiftKey
Database migration simple, cross-engine and cross-platform migrations with ...Amazon Web Services
Learn how you can migrate databases with minimal downtime from on-premises and Amazon EC2 environments to Amazon RDS, Amazon Redshift, Amazon Aurora and EC2 databases using AWS Database Migration Service. We'll discuss homogeneous (e.g. Oracle-to-Oracle, PostgreSQL-to-PostgreSQL, etc.) and heterogeneous (e.g. Oracle to Aurora, SQL Server to MariaDB) database migrations. We'll also talk about the new AWS Schema Conversion Tool that saves you development time when migrating your Oracle and SQL Server database schemas, including PL/SQL and T-SQL procedural code, to their MySQL, MariaDB and Aurora equivalents. Best of all, we'll spend most of the time demonstrating the product and showing use cases designed to help your business.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Warum ist Cloud-Sicherheit und Compliance wichtig?AWS Germany
Wer seine IT-Projekte in die Cloud bringen möchte, muss auf ein paar Fallstricke achten. Herausforderungen finden Sie vor allem im Bereich der Sicherheit. Ihre Daten müssen vor dem Zugriff Unberechtigter absolut sicher sein. Trotzdem muss das Zugriffsmanagement für Ihre Mitarbeiter gut funktionieren. Zu diesen technischen Aufgaben kommen handfeste Vorgaben aus Ihren betrieblichen Richtlinien sowie wichtige gesetzliche Auflagen hinzu. Diese Compliance-Fragen sollten Sie unbedingt kennen und zuverlässig erfüllen. Denn nur, wenn Sie alle Compliance-Vorgaben korrekt einhalten, kann Ihr Cloud-Projekt ein voller Erfolg werden.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure. More information: https://aws.amazon.com/machine-learning/
Einführung in Amazon Machine Learning - AWS Machine Learning Web DayAWS Germany
Vortrag "Einführung in Amazon Machine Learning " von Oliver Arafat beim AWS Machine Learning Web Day. Alle Videos und Präsentationen finden Sie hier: http://amzn.to/1XP3dz9
In this session from the London AWS Summit 2015 Tech Track Replay, AWS Technical Evangelist Ian Massingham introduces the new Amazon Machine Learning service.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure.
Amazon Machine Learning Case Study: Predicting Customer ChurnAmazon Web Services
We do a deeper dive into Amazon Machine Learning, using a specific business problem as an example – predicting if the customer is about to leave your service, also known as customer churn. We examine several practical aspects of building and using a model, including the use of the recipe language for training data manipulation and modeling the costs of false positive/negative errors.
Vortrag "Real-World Smart Applications with Amazon Machine Learning" von Alex Ingerman beim AWS Machine Learning Web Day. Alle Videos und Präsentationen finden Sie hier: http://amzn.to/1XP3dz9
AWS November Webinar Series - Advanced Analytics with Amazon Redshift and the...Amazon Web Services
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology and Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. The combination of the two can provide a solution to power advanced analytics for not only what has happened in the past, but make intelligent predictions about the future. Please join this webinar to learn how get the most value from your data for your data driven business.
Learning Objectives:
How to scale your Redshift queries with user-defined functions (UDFs)
How to apply Machine learning to historical data in Amazon Redshift
How to visualize your data with Amazon QuickSight
Present a reference architecture for advanced analytics
Who Should Attend:
Application developers looking to add UDFs, or predictive analytics to their applications, database administrators that need to meet the demand of data driven organizations, decision makers looking to derive more insight from their data
From my session at DevTernity in Riga, December 1st 2015. Have you always wanted to add predictive capabilities to your application, but haven’t been able to find the time or the right technology to get started? Everybody wants to build smart apps, but only a few are Data Scientists. We had the same issue inside Amazon, so we created a Machine Learning engine that Developers can easily use. The same approach is now available in the AWS cloud. We demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We walk you through the process flow and architecture, demonstrate outcomes, and then dive into the implementation. In this session, you learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
Exploring the Business Use Cases for Amazon Machine Learning - June 2017 AWS ...Amazon Web Services
Learning Objectives:
- Learn how to integrate Amazon Machine Learning with applications
- Learn how to train a model using Amazon Machine Learning - Learn how to process semi-structured log data in real-time with Amazon Machine Learning
Machine learning has been used to provide more accurate predictions than hardcoded business logic using available data. For our customers, Amazon Machine Learning is being used from helping restaurant owners, as with Upserve, to determine the right staffing level on a night; to providing more accurate cost estimates in the insurance industry, as with BuildFax. In this tech talk, we'll cover the basics of how to get started with Amazon Machine Learning, and go through an example of how to perform real-time classification of log data using Amazon Machine Learning.
Amazon DynamoDB is a fully managed NoSQL database service for applications that need consistent, single-digit millisecond latency at any scale. This talk explores DynamoDB capabilities and benefits in detail and discusses how to get the most out of your DynamoDB database. We go over schema design best practices with DynamoDB across multiple use cases, including gaming, AdTech, IoT, and others. We also explore designing efficient indexes, scanning, and querying, and go into detail on a number of recently released features, including JSON document support, Streams, and more.
AWS Webcast - Amazon Elastic Map Reduce Deep Dive and Best PracticesAmazon Web Services
Amazon Elastic MapReduce (EMR) is one of the largest Hadoop operators in the world. Since its launch five years ago, our customers have launched more than 15 million Hadoop clusters inside of EMR. In this webinar, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters and other Amazon EMR architectural patterns. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We also share best practices to keep your Amazon EMR cluster cost efficient.
This webinar recording will explain how to get started with Amazon Elastic MapReduce (EMR). EMR enables fast processing of large structured or unstructured datasets, and in this webinar we'll demonstrated how to setup an EMR job flow to analyse application logs, and perform Hive queries against it. We'll review best practices around data file organisation on Amazon Simple Storage Service (S3), how clusters can be started from the AWS web console and command line, and how to monitor the status of a Map/Reduce job. The security configuration that allows direct access to the Amazon EMR cluster in interactive mode will be shown, and we'll see how Hive provides a SQL like environment, while allowing you to dynamically grow and shrink the amount of compute used for powerful data processing activities.
Amazon EMR YouTube Recording: http://youtu.be/gSPh6VTBEbY
Amazon Elastic MapReduce is one of the largest Hadoop operators in the world. Since its launch five years ago, AWS customers have launched more than 5.5 million Hadoop clusters.
In this talk, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters and other Amazon EMR architectural patterns. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We also share best practices to keep your Amazon EMR cluster cost efficient.
Speakers:
Ian Meyers, AWS Solutions Architect
Ian McDonald, IT Director, SwiftKey
Database migration simple, cross-engine and cross-platform migrations with ...Amazon Web Services
Learn how you can migrate databases with minimal downtime from on-premises and Amazon EC2 environments to Amazon RDS, Amazon Redshift, Amazon Aurora and EC2 databases using AWS Database Migration Service. We'll discuss homogeneous (e.g. Oracle-to-Oracle, PostgreSQL-to-PostgreSQL, etc.) and heterogeneous (e.g. Oracle to Aurora, SQL Server to MariaDB) database migrations. We'll also talk about the new AWS Schema Conversion Tool that saves you development time when migrating your Oracle and SQL Server database schemas, including PL/SQL and T-SQL procedural code, to their MySQL, MariaDB and Aurora equivalents. Best of all, we'll spend most of the time demonstrating the product and showing use cases designed to help your business.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Warum ist Cloud-Sicherheit und Compliance wichtig?AWS Germany
Wer seine IT-Projekte in die Cloud bringen möchte, muss auf ein paar Fallstricke achten. Herausforderungen finden Sie vor allem im Bereich der Sicherheit. Ihre Daten müssen vor dem Zugriff Unberechtigter absolut sicher sein. Trotzdem muss das Zugriffsmanagement für Ihre Mitarbeiter gut funktionieren. Zu diesen technischen Aufgaben kommen handfeste Vorgaben aus Ihren betrieblichen Richtlinien sowie wichtige gesetzliche Auflagen hinzu. Diese Compliance-Fragen sollten Sie unbedingt kennen und zuverlässig erfüllen. Denn nur, wenn Sie alle Compliance-Vorgaben korrekt einhalten, kann Ihr Cloud-Projekt ein voller Erfolg werden.
This introductory seminar explains Cloud Computing and Amazon Web Services (AWS) in great detail.
The presenter, Simone Brunozzi (@simon), is an AWS Technology Evangelist.
Recommended for business/technical audiences.
Build a Recommendation Engine using Amazon Machine Learning in Real-timeAmazon Web Services
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. In this session, we will introduce how to use Amazon Machine Learning to create a data model, and use it to generate the real-time prediction for your application.
Amazon Machine Learning (Amazon ML) is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning’s powerful algorithms create machine learning models by finding patterns in your existing data. The service uses these models to process new data and generate predictions for your application. In this session, we will show you how to use machine learning with the data you already have to arrive at accurate and actionable predictions - to create smart applications. You will learn how to use and integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
Amazon Machine Learning (Amazon ML) is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning’s powerful algorithms create machine learning models by finding patterns in your existing data. The service uses these models to process new data and generate predictions for your application. In this session, we will show you how to use machine learning with the data you already have to arrive at accurate and actionable predictions - to create smart applications. You will learn how to use and integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning’s powerful algorithms create machine learning (ML) models by finding patterns in your existing data. Then, the service uses these models to process new data and generate predictions for your application. Amazon Machine Learning can ingest data from Amazon S3, Amazon Redshift or Amazon RDS. In this webinar, we will demonstrate how Amazon Machine Learning can be used to build an ML model, deploy it to production, and query this model from within a smart application. AWS services to be covered include: Amazon Machine Learning, Amazon Elastic MapReduce, Amazon Redshift, Amazon S3,Amazon Relational Database Service, RDS, and Amazon DynamoDB.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. In this session, we will introduce how to use S3 as a Data Lake to collect device information via AWS IoT, and then generate prediction for your application.
AWS ML and SparkML on EMR to Build Recommendation Engine Amazon Web Services
Machine Learning
A managed supervised learning environment to build different models, including Binary Classification / Multi-class classification / Regression ML. The demos will show a dataset of banking customers with demographics, predicting the likelihood of whether they are going to default using binary classification. Second one will be predicting a UK bike rental shop traffic using linear regression, and third one for predicting a rainforest soil type using multi-class classification.
Benefits: Managed and on-demand environment for supervised learning algorithm, available as batch processing or real-time API.
Spark ML Cluster
Running spark on AWS managed cluster, storing data on HDFS / S3 persistent storage, modules include MLib and Zeppelin (Web Notebook), to build a movie recommendation engine based on “Collaborative Filtering”. The dataset contains 10M ratings provided by grouplens from MovieLens website.
Benefits: Fully managed clusters, with HA, Scalability, Elasticity and Spot instance pricing
AWS January 2016 Webinar Series - Building Smart Applications with Amazon Mac...Amazon Web Services
In this presentation, learn how an end-to-end smart application can be built in the AWS cloud. We will demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We will then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We will walk you through the process flow and architecture, demonstrate outcomes, and then dive into the code for implementation. In this session, you will learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
Learning Objectives:
Learn about AWS services needed to build smart applications on AWS, e.g. Amazon Kinesis, AWS Lambda, Amazon Mechanical Turk, Amazon SNS
Learn how to deploy such implementation
Get the code on GitHub for you to use immediately
Who Should Attend:
Developers, Engineers, Solutions Architects
Speech deliverd on 20 June 2020 at TR.AI Meetup, Istanbul
TR.AI Türkiye Yapay Zeka İnisiyatifi
AI/ML PoweredPersonalized Recommendations in Gaming Industry
Amazon Web Services - AWS
A business level introduction to Artificial Intelligence - Louis Dorard @ PAP...PAPIs.io
Artificial Intelligence and Machine Learning are becoming increasingly accessible. Starting from example use cases, I’ll aim at demystifying how they work and how they improve businesses in 3 areas: increasing the number of customers, serving them better, and serving them more efficiently. I’ll show how machines can use data to automatically learn business rules and make predictions, that can then be used to make better decisions. I’ll introduce the main concepts of ML, its possibilities, its limitations, and I’ll give tips on framing the right problems for your company to tackle.
Louis Dorard is the author of Bootstrapping Machine Learning, a co-founder of PAPIs, and an independent consultant. His goal is to help people use new machine learning technologies to make their apps and businesses smarter. He does this by writing, speaking and teaching.
Financial Services companies are using machine learning to reduce fraud, streamline processes, and improve their bottom line. AWS provides tools that help them easily use AI tools like MXNet and Tensor Flow to perform predictive analytics, clustering, and more advanced data analyses. In this session, you'll hear how IHS Markit has used Machine Learning on AWS to help global banking institutions manage their commodities portfolios. You will also learn how the Amazon Machine Learning Service can take the hassle out of AI.
Recomendaciones, predicciones y detección de fraude usando servicios de intel...javier ramirez
La implementación de modelos de aprendizaje automático para resolver desafíos de negocios complejos, como detección de fraude, recomendaciones o predicción de series de datos es difícil si se quiere partir desde cero. Sin embargo, utilizando herramientas de AWS, implementar esos modelos está al alcance de cualquier empresa que sea capaz de subir un fichero a la nube, y llamar a un API cuando quiera obtener resultados. Basados en la tecnología de aprendizaje automático que se perfeccionó gracias a años de uso en Amazon.com, Amazon Forecast, Amazon Personalize, y Amazon Fraud Detector permiten a cualquiera sin experiencia previa en aprendizaje automático integrar estas tecnologías en sus aplicaciones. En este video aprenderás cuáles son las dificultades de crear modelos de predicción para los casos ya mencionados, verás como AWS acelera el difícil trabajo que se necesita para diseñar, entrenar e implementar un modelo personalizado para tus datos, y te contaremos todo lo que necesitas para poder empezar a integrar estos modelos en tu aplicación. Por supuesto, veremos demos de cómo funcionan
Leveraging data to become more customer-centric is a key factor for online retail sales. Using a host of Machine learning techniques like recommender systems, image analytics, customer churn and demand prediction- can impact sales, customer loyalty & improve revenues
Machine learning is starting to become a standard part of everyday operation in corporations across various industries. In this sense, companies are nowadays starting to use standardized tools which, in combination with cloud technologies, provide model development in an increasingly understandable, user-friendly environments, allowing for more and more decision making to be based on machine learning algorithms. This lecture will show how sports and energy industries are leveraging cloud-based ML tools to gain some interesting and business-critical insights. Apart from the technical part, the talk will entail discussion about how Microsoft Development Center Serbia is increasingly using data science for the development of cloud services.
Analytics Web Day | From Theory to Practice: Big Data Stories from the FieldAWS Germany
Listen to this session to get some insights from two recent implementations of cloud-based Big Data clusters. The purpose of the first solution is DWH Offloading and Machine Learning in the telecommunications industry. The session will cover how we established the data transfer between on-premises server and cloud services. In addition, we will talk about Spark jobs on EMR-cluster, Hive with GlueCatalog to query data stored in S3, quick analytics with Athena, hosting and testing Exasol on EC2-Instances and the provisioning of the cloud infrastructure with CloudFormation. Looking at an earlier phase in the AWS adoption lifecycle, we will also talk about an insurance company finding their way into the AWS cloud. Their goal is to complement their existing enterprise DWH with more agile and data science oriented tools from the cloud, aiming at machine learning and artifical intelligence to complement their claims workflow. In this part we will cover topics like security setup in IAM, connectivity configuration in EC2 and EMR, all complemented with S3 for their storage needs.
Speakers: Roland Wammers, Matthias Diekstall, Manuel Marowski, Opitz Consulting Deutschland GmbH
Analytics Web Day | Query your Data in S3 with SQL and optimize for Cost and ...AWS Germany
The previous presentation showed how events can be ingested and analyzed continuously in real time. One of Big Data's principles is to store raw data as long as possible - to be able to answer future questions. If the data is permanently stored in Amazon Simple Storage Service (S3), it can be queried at any time with Amazon Athena without spinning up a database.
This session shows step by step how the data should be structured so that both costs and response times are reduced when using Athena. The details and effects of compression, partitions, and column storage formats are compared. Finally, AWS Glue is used as a fully managed service for Extract Transform Load (ETL) to derive optimized views from the raw data for frequently issued queries.
Speaker: Steffen Grunwald, Senior Solutions Architect, AWS
Modern Applications Web Day | Impress Your Friends with Your First Serverless...AWS Germany
"Build and run applications without thinking about servers". You want it? You get it! We will start this session with a motivation why serverless applications are a thing. Once we got there, we will actually start building one, of course with making use of a serverless CI/CD pipeline. After we will have looked into how we can still test it locally, we shall also dive into analyzing and debugging our app - of course in a serverless manner.
Speaker: Dirk Fröhner, Senior Solutions Architect, AWS
Modern Applications Web Day | Manage Your Infrastructure and Configuration on...AWS Germany
It's easy to say - "Hey I will use the cloud and be scalable and elastic!" - But it is not easy managing all that at scale, and keeping it flexible! Let's talk about Infrastructure as Code and Configuration as Code! This session will help you grasp the available toolset and best practices when it comes to managing your infrastructure and configuration on AWS. It will show you how can you make any changes to your workload with a single 'git push origin master'
Speaker: Darko Meszaros, Solutions Architect, AWS
Modern Applications Web Day | Container Workloads on AWSAWS Germany
Containers gained strong traction since day one for both enterprises and startups. Today AWS customers are launching hundreds of millions of new containers – each week. Join us as we cover the state of containerized application development and deployment trends. This session will dive deep on new container capabilities that help customers deploying and running container-based workloads for web services and batches.
Speaker: Steffen Grunwald, Senior Solutions Architect, AWS & Sascha Möllering, Senior Solutions Architect, AWS
Modern Applications Web Day | Continuous Delivery to Amazon EKS with SpinnakerAWS Germany
With more and more application workloads moving to Kubernetes, the interest in managed Kubernetes services in enterprises is increasing. While Amazon EKS will make operations easier, an efficient and transparent delivery pipeline becomes more important than ever. This will provide an increased application development velocity that will directly convert into a competitive advantage with fast paced digital services. While established tools such as Jenkins can be used quite efficiently for CI tasks, modern cloud-native tools like Spinnaker are gaining attention by focusing more in the continuous delivery process. We will show you how Spinnaker and its new Kubernetes v2 provider can be utilized together with Amazon EKS to streamline your application deployments.
Speaker: Jukka Forsgren, nordcloud
The most common way to start developing for Alexa is with custom skills while not too many of us except for device manufacturers get in touch with Smart Home skills on Alexa. This session introduces and demonstrates the power of Smart Home skills and it takes a look behind the technical scene of what happens in between an “Alexa, turn on the lights” and Alexa´s final “Ok” confirmation. Once you are familiar with the concept of Smart Home skills you will find out that it’s not just for implementing large-scale Smart Home solutions as the Smart Home API is also a great playground for your next Do it Yourself project. At the end of this session you’ve learned about the probably simplest way to build a Smart Home project with Raspberry Pi and AWS IoT – and you will be equipped with essential knowledge on how to build your own voice-controlled “thing”.
Hotel or Taxi? "Sorting hat" for travel expenses with AWS ML infrastructureAWS Germany
Automating the boring task of submitting travel expenses we developed ML model for classifying recipes. Using AWS EC2, Lambda, S3, SageMaker, Rekognition we evaluated different ways of training model and serving predictions as well as different model approaches (classical ML vs. Deep Learning).
Wild Rydes with Big Data/Kinesis focus: AWS Serverless WorkshopAWS Germany
This is a hands-on workshop where every participant will not only learn how to architect and implement a serverless application on Amazon Web Services using nothing but serverless resources for all layers in theory, but actually do it in practice, with all the necessary support from the speakers. Serverless computing allows you to build and run applications and services without thinking about servers. Serverless applications don't require you to provision, scale, and manage any servers. You can build them for nearly any type of application or backend service, and everything required to run and scale your application with high availability is handled for you. Building serverless applications means that developers can focus on their core product instead of worrying about managing and operating servers or runtimes. This reduced overhead lets developers reclaim time and energy that can be spent on developing great products which scale and that are reliable.
Nearly everything in IT - servers, applications, websites, connected devices, and other things - generate discrete, time-stamped records of events called logs. Processing and analyzing these logs to gain actionable insights is log analytics. We'll look at how to use centralized log analytics across multiple sources with Amazon Elasticsearch Service.
Deep Dive into Concepts and Tools for Analyzing Streaming Data on AWS AWS Germany
Querying streaming data with SQL to derive actionable insights at the point of impact in a timely and continuous fashion offers various benefits over querying data in a traditional database. However, although it is desirable for many use cases to transition to a stream based paradigm, stream processing systems and traditional databases are fundamentally different: in a database, the data is (more or less) fixed and the queries are executed in an ad-hoc manner, whereas in stream processing systems, the queries are fixed and the data flows through the system in real-time. This leads to different primitives that are required to model and query streaming data.
In this session, we will introduce basic stream processing concepts and discuss strategies that are commonly used to address the challenges that arise from querying of streaming data. We will discuss different time semantics, processing guarantees and elaborate how to deal with reordering and late arriving of events. Finally, we will compare how different streaming use cases can be implemented on AWS by leveraging Amazon Kinesis Data Analytics and Apache Flink.
Zehntausende gemeinnützige und nichtstaatliche Organisationen weltweit verwenden AWS, damit sie sich auf ihre eigentliche Mission konzentrieren können, statt ihre IT-Infrastruktur zu verwalten. Die Anwendungsgebiete von Nonprofits und NGOs sind dabei genauso vielfältig, wie bei Enterprise oder Start-up oder anderen AWS-Anwendern im öffentlichen Sektor. Gemeinnützige Organisationen und NGOs nutzen AWS z.B. um hochverfügbare und hochskalierbare Websites zu erstellen, um ihre Spendenaktionen und Öffentlichkeitsarbeit effizient zu verwalten, oder um Nutzen aus Big Data Anwendungen zu ziehen.
In dieser Sitzung werden wir einen Blick auf die verschiedenen AWS-Programme werfen, die gemeinnützigen Organisationen den Einstige in AWS und die Umsetzung ihrer IT-Projekte erleichtern. Insbesondere informieren wir auch über das Angebote mit Stifter-Helfen.de - dem deutschen TechSoup-Partner. Dieses Angebot stellt den begünstigten Organisationen pro Jahr $2.000 in AWS Credit Codes zu Verfügung.
Die Session richtet sich an alle, die sich für einen guten Zweck engagieren wollen und dabei nicht auf innovative Cloud-Services zur Umsetzung ihrer IT-Projekte verzichten wollen. Für die Teilnahme and der Session sind keine technischen Vorkenntnisse notwendig
Serverless vs. Developers – the real crashAWS Germany
With serverless things are getting really different. Commodity building blocks from our cloud providers, functional billing, serverless marketplaces etc. are going to hit the usual “Not invented here”3 syndrome in organizations.
Many beloved things have to be un- or re-learned by software developers. How can we prepare our organizations and people for unlearning old patterns and behaviours? Let’s have a look from a knowledge management perspective.
Objective of the talk:
Intro into systemic knowledge management
Query your data in S3 with SQL and optimize for cost and performanceAWS Germany
Streaming services allow you to ingest and analyze events continuously in real time. One of Big Data's principles is to store raw data as long as possible - to be able to answer future questions. If the data is permanently stored in Amazon Simple Storage Service (S3), it can be queried at any time with Amazon Athena without spinning up a database.
This session shows step by step how the data should be structured so that both costs and response times are reduced when using Athena. The details and effects of compression, partitions, and column storage formats are compared. Finally, AWS Glue is used as a fully managed service for Extract Transform Load (ETL) to derive optimized views from the raw data for frequently issued queries.
Secret Management with Hashicorp’s VaultAWS Germany
When running a Kubernetes Cluster in AWS there are secrets like AWS and Kubernetes credentials, access information for databases or integration with the company LDAP that need to be stored and managed.
HashiCorp’s Vault secures, stores, and controls access to tokens, passwords, certificates, API keys, and other secrets . It handles leasing, key revocation, key rolling, and auditing.
This talk will give an overview of secret management in general and Vault’s concepts. The talk will explain how to make use of Vault’s extensive feature set and show patterns that implement integration between Kubernetes applications and Vault.
Running more than one containerized application in production makes teams look for solutions to quickly deploy and orchestrate containers. One of the most popular options is the open-source project Kubernetes. With the release of the Amazon Elastic Container Service for Kubernetes (EKS), engineering teams now have access to a fully managed Kubernetes control plane and time to focus on building applications. This workshop will deliver hands-on labs to support you getting familiar with Amazon's EKS.
Our challenge is to provide a container cluster as part of the Cloud Platform at Scout24. Our goal is to support all the different applications with varying requirements the Scout24 dev teams can throw at us. Up until now, we have run all of them on the same ECS cluster with the same parameters. As we get further into our AWS migration, we have learned this does not scale. We combat this by introducing categories in one cluster with different configurations for the service. We will introduce how we tune each category differently, with different resource limits, different scaling approaches and more…
Containers gained strong traction since day one for both enterprises and startups. Today AWS customers are launching hundreds of millions of new containers – each week. Join us as we cover the state of containerized application development and deployment trends. This session will dive deep on new container capabilities that help customers deploying and running container-based workloads for web services and batches.
Deploying and Scaling Your First Cloud Application with Amazon LightsailAWS Germany
Are you looking to move to the cloud, but aren’t sure quite where to start? Are you already using AWS, and are looking for ways to simplify some of your workflows? If you answered “yes” (or even “maybe”) to either one of those questions, this session / hands-on workshop is for you. We’re going to take you through using Amazon Lightsail, an AWS service that provides the quickest way to get started in the cloud, to deploy and scale an application on AWS.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
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.
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
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
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
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.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
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!
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.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
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
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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.
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.
2. Agenda
• Machine learning and the data ecosystem
• Smart applications by example (and counter-example)
• Amazon Machine Learning features and benefits
• Developing with Amazon ML
• Q&A
3. Three types of data-driven development
Retrospective
analysis and
reporting
Amazon Redshift
Amazon RDS
Amazon S3
Amazon EMR
4. Three types of data-driven development
Retrospective
analysis and
reporting
Here-and-now
real-time processing
and dashboards
Amazon Kinesis
Amazon EC2
AWS Lambda
Amazon Redshift,
Amazon RDS
Amazon S3
Amazon EMR
5. Three types of data-driven development
Retrospective
analysis and
reporting
Here-and-now
real-time processing
and dashboards
Predictions
to enable smart
applications
Amazon Kinesis
Amazon EC2
AWS Lambda
Amazon Redshift,
Amazon RDS
Amazon S3
Amazon EMR
6. Machine learning and smart applications
Machine learning is the technology that automatically finds
patterns in your data and uses them to make predictions for
new data points as they become available
7. Machine learning and smart applications
Machine learning is the technology that automatically finds
patterns in your data and uses them to make predictions for
new data points as they become available
Your data + machine learning = smart applications
8. Smart applications by example
Based on what you
know about the user:
Will they use your
product?
9. Smart applications by example
Based on what you
know about the user:
Will they use your
product?
Based on what you
know about an order:
Is this order
fraudulent?
10. Smart applications by example
Based on what you
know about the user:
Will they use your
product?
Based on what you
know about an order:
Is this order
fraudulent?
Based on what you know
about a news article:
What other articles are
interesting?
11. And a few more examples…
Fraud detection Detecting fraudulent transactions, filtering spam emails,
flagging suspicious reviews, …
Personalization Recommending content, predictive content loading, improving
user experience, …
Targeted marketing Matching customers and offers, choosing marketing
campaigns, cross-selling and up-selling, …
Content classification Categorizing documents, matching hiring managers and
resumes, …
Churn prediction Finding customers who are likely to stop using the service,
free-tier upgrade targeting, …
Customer support Predictive routing of customer emails, social media listening,
…
12. Building smart applications – a counter-pattern
Dear Alex,
This awesome quadcopter is on sale
for just $49.99!
13. Smart applications by counter-example
SELECT c.ID
FROM customers c
LEFT JOIN orders o
ON c.ID = o.customer
GROUP BY c.ID
HAVING o.date > GETDATE() – 30
We can start by sending the
offer to all customers who
placed an order in the last 30
days
14. Smart applications by counter-example
SELECT c.ID
FROM customers c
LEFT JOIN orders o
ON c.ID = o.customer
GROUP BY c.ID
HAVING
AND o.date > GETDATE() – 30
… let’s narrow it down to just
customers who bought toys
15. Smart applications by counter-example
SELECT c.ID
FROM customers c
LEFT JOIN orders o
ON c.ID = o.customer
GROUP BY c.ID
HAVING o.category = ‘toys’
AND
(COUNT(*) > 2
AND SUM(o.price) > 200
AND o.date > GETDATE() – 30)
)
… and expand the query to customers who
purchased other toy helicopters recently
16. Smart applications by counter-example
SELECT c.ID
FROM customers c
LEFT JOIN orders o
ON c.ID = o.customer
LEFT JOIN products p
ON p.ID = o.product
GROUP BY c.ID
HAVING o.category = ‘toys’
AND ((p.description LIKE ‘% %’
AND o.date > GETDATE() - 60)
OR (COUNT(*) > 2
AND SUM(o.price) > 200
AND o.date > GETDATE() – 30)
)
… but what about
quadcopters?
17. Smart applications by counter-example
SELECT c.ID
FROM customers c
LEFT JOIN orders o
ON c.ID = o.customer
LEFT JOIN products p
ON p.ID = o.product
GROUP BY c.ID
HAVING o.category = ‘toys’
AND ((p.description LIKE ‘%copter%’
AND o.date > GETDATE() - )
OR (COUNT(*) > 2
AND SUM(o.price) > 200
AND o.date > GETDATE() – 30)
)
… maybe we should go back further in time
18. Smart applications by counter-example
SELECT c.ID
FROM customers c
LEFT JOIN orders o
ON c.ID = o.customer
LEFT JOIN products p
ON p.ID = o.product
GROUP BY c.ID
HAVING o.category = ‘toys’
AND ((p.description LIKE ‘%copter%’
AND o.date > GETDATE() - 120)
OR (COUNT(*) > 2
AND SUM(o.price) > 200
AND o.date > GETDATE() – )
)
… tweak the query more
19. Smart applications by counter-example
SELECT c.ID
FROM customers c
LEFT JOIN orders o
ON c.ID = o.customer
LEFT JOIN products p
ON p.ID = o.product
GROUP BY c.ID
HAVING o.category = ‘toys’
AND ((p.description LIKE ‘%copter%’
AND o.date > GETDATE() - 120)
OR (COUNT(*) > 2
AND SUM(o.price) >
AND o.date > GETDATE() – 40)
)
… again
20. Smart applications by counter-example
SELECT c.ID
FROM customers c
LEFT JOIN orders o
ON c.ID = o.customer
LEFT JOIN products p
ON p.ID = o.product
GROUP BY c.ID
HAVING o.category = ‘toys’
AND ((p.description LIKE ‘%copter%’
AND o.date > GETDATE() - )
OR (COUNT(*) > 2
AND SUM(o.price) > 150
AND o.date > GETDATE() – 40)
)
… and again
21. Smart applications by counter-example
SELECT c.ID
FROM customers c
LEFT JOIN orders o
ON c.ID = o.customer
LEFT JOIN products p
ON p.ID = o.product
GROUP BY c.ID
HAVING o.category = ‘toys’
AND ((p.description LIKE ‘%copter%’
AND o.date > GETDATE() - )
OR (COUNT(*) > 2
AND SUM(o.price) > 150
AND o.date > GETDATE() – 40)
)
Use machine learning
technology to learn
your business rules
from data!
22. Why aren’t there more smart applications?
1. Machine learning expertise is rare
2. Building and scaling machine learning technology is hard
3. Closing the gap between models and applications is time-consuming and
expensive
23. Building smart applications today
Expertise Technology Operationalization
Limited supply of
data scientists
Many choices, few
mainstays
Complex and error-
prone data workflows
Expensive to hire
or outsource
Difficult to use and scale Custom platforms and
APIs
Many moving pieces lead
to custom solutions
every time
Reinventing the model
lifecycle management
wheel
25. Introducing Amazon ML
Easy to use, managed machine learning service built
for developers
Robust, powerful machine learning technology
based on Amazon’s internal systems
Create models using your data already stored in the
AWS cloud
Deploy models to production in seconds
26. Easy to use and developer-friendly
Use the intuitive, powerful service console to build and
explore your initial models
• Data retrieval
• Model training, quality evaluation, fine-tuning
• Deployment and management
Automate model lifecycle with fully featured APIs and SDKs
• Java, Python, .NET, JavaScript, Ruby, PHP
Easily create smart iOS and Android applications with AWS
Mobile SDK
27. Powerful machine learning technology
Based on Amazon’s battle-hardened internal systems
Not just the algorithms:
• Smart data transformations
• Input data and model quality alerts
• Built-in industry best practices
Grows with your needs
• Train on up to 100 GB of data
• Generate billions of predictions
• Obtain predictions in batches or real-time
28. Integrated with AWS Data Ecosystem
Access data that is stored in S3, Amazon Redshift, or
MySQL databases in RDS
Output predictions to S3 for easy integration with your data
flows
Use AWS Identity and Access Management (IAM) for fine-
grained data-access permission policies
29. Fully-managed model and prediction services
End-to-end service, with no servers to provision and
manage
One-click production model deployment
Programmatically query model metadata to enable
automatic retraining workflows
Monitor prediction usage patterns with Amazon
CloudWatch metrics
30. Pay-as-you-go and inexpensive
Data analysis, model training, and evaluation:
$0.42/instance hour
Batch predictions: $0.10/1000
Real-time predictions: $0.10/1000
+ hourly capacity reservation charge
31. Three Supported Types of Predictions
Binary Classification: predict the answer to a yes/no question
• Is this order fraudulent?
• Will this customer convert?
• Which article should I show next?
Multi-class classification: predict the correct category from list:
• What is the genre of this movie?
• What is the root cause of this customer contact?
Regression: predict the value of a numeric variable
• How many units of this item will sell next week?
• How long will this user session last?
36. Train your model
>>> import boto
>>> ml = boto.connect_machinelearning()
>>> model = ml.create_ml_model(
ml_model_id=’my_model',
ml_model_type='REGRESSION',
training_data_source_id='my_datasource')
42. Batch predictions
Asynchronous, large-volume prediction generation
Request through service console or API
Best for applications that deal with batches of data records
>>> import boto
>>> ml = boto.connect_machinelearning()
>>> model = ml.create_batch_prediction(
batch_prediction_id = 'my_batch_prediction’
batch_prediction_data_source_id = ’my_datasource’
ml_model_id = ’my_model',
output_uri = 's3://examplebucket/output/’)
43. Real-time predictions
Synchronous, low-latency, high-throughput prediction generation
Request through service API or server or mobile SDKs
Best for interaction applications that deal with individual data records
>>> import boto
>>> ml = boto.connect_machinelearning()
>>> ml.predict(
ml_model_id=’my_model',
predict_endpoint=’example_endpoint’,
record={’key1':’value1’, ’key2':’value2’})
{
'Prediction': {
'predictedValue': 13.284348,
'details': {
'Algorithm': 'SGD',
'PredictiveModelType': 'REGRESSION’
}
}
}
45. Batch predictions with EMR
Query for predictions with
Amazon ML batch API
Process data with
EMR
Raw data in S3
Aggregated data
in S3
Predictions
in S3 Your application
46. Batch predictions with Amazon Redshift
Structured data
In Amazon Redshift
Load predictions into Amazon
Redshift
-or-
Read prediction results directly
from S3
Predictions
in S3
Query for predictions with
Amazon ML batch API
Your application
47. Real-time predictions for interactive applications
Your application
Query for predictions with
Amazon ML real-time API
48. Adding predictions to an existing data flow
Your application
Amazon
DynamoDB
+
Trigger event with Lambda
+
Query for predictions with
Amazon ML real-time API