https://learn.xnextcon.com/event/eventdetails/W20040610
This talk explains how to practically bring the power of convolutional neural networks and deep learning to memory and power-constrained devices like smartphones. You will learn various strategies to circumvent obstacles and build mobile-friendly shallow CNN architectures that significantly reduce the memory footprint and therefore make them easier to store on a smartphone;
The talk also dives into how to use a family of model compression techniques to prune the network size for live image processing, enabling you to build a CNN version optimized for inference on mobile devices. Along the way, you will learn practical strategies to preprocess your data in a manner that makes the models more efficient in the real world.
201905 Azure Certification DP-100: Designing and Implementing a Data Science ...Mark Tabladillo
Microsoft has several Azure certifications including DP-100 (Designing and Implementing a Data Science Solution on Azure). Until this month, the exam had been in beta: however, the presenter has just passed the exam (first try). The purpose of this event is to share a viewpoint on how to study for the exam. Today, there are multiple ways to develop and deliver and deploy R or Python or Spark or deep learning models on Azure. The differences are important for this exam.
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
Material for Azure Machine Learning tutorial lecture, held within Data Mining course of MoS in Engineering in Computer Science at Università degli Studi di Roma "La Sapienza" (A.Y. 2016/2017).
Lecturers:
Fabio Rosato - rosato.1565173@studenti.uniroma1.it
Giacomo Lanciano - lanciano.1487019@studenti.uniroma1.it
Francisco Ferreres Garcia - matakukos@gmail.com
Leonardo Martini - martini.1722989@studenti.uniroma1.it
Simone Caldaro - caldaro.1324152@studenti.uniroma1.it
Na Zhu - nana.zhu@hotmail.com
Github repo: https://github.com/giacomolanciano/Azure-Machine-Learning-tutorial
Video tutorial: https://youtu.be/_zvPX6Kk7z8
I want my model to be deployed ! (another story of MLOps)AZUG FR
Speaker : Paul Peton
Putting machine learning into production remains a challenge even though the algorithms have been around for a very long time. Here are some blocks:
– the choice of programming language
– the difficulty of scaling
– fear of black boxes on the part of users
Azure Machine Learning is a new service that allows to control the deployment steps on the appropriate resources (Web App, ACI, AKS) and specially to automate the whole process thanks to the Python SDK.
How to use Azure Machine Learning service to manage the lifecycle of your models. Azure Machine Learning uses a Machine Learning Operations (MLOps) approach, which improves the quality and consistency of your machine learning solutions.
https://learn.xnextcon.com/event/eventdetails/W20040610
This talk explains how to practically bring the power of convolutional neural networks and deep learning to memory and power-constrained devices like smartphones. You will learn various strategies to circumvent obstacles and build mobile-friendly shallow CNN architectures that significantly reduce the memory footprint and therefore make them easier to store on a smartphone;
The talk also dives into how to use a family of model compression techniques to prune the network size for live image processing, enabling you to build a CNN version optimized for inference on mobile devices. Along the way, you will learn practical strategies to preprocess your data in a manner that makes the models more efficient in the real world.
201905 Azure Certification DP-100: Designing and Implementing a Data Science ...Mark Tabladillo
Microsoft has several Azure certifications including DP-100 (Designing and Implementing a Data Science Solution on Azure). Until this month, the exam had been in beta: however, the presenter has just passed the exam (first try). The purpose of this event is to share a viewpoint on how to study for the exam. Today, there are multiple ways to develop and deliver and deploy R or Python or Spark or deep learning models on Azure. The differences are important for this exam.
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
Material for Azure Machine Learning tutorial lecture, held within Data Mining course of MoS in Engineering in Computer Science at Università degli Studi di Roma "La Sapienza" (A.Y. 2016/2017).
Lecturers:
Fabio Rosato - rosato.1565173@studenti.uniroma1.it
Giacomo Lanciano - lanciano.1487019@studenti.uniroma1.it
Francisco Ferreres Garcia - matakukos@gmail.com
Leonardo Martini - martini.1722989@studenti.uniroma1.it
Simone Caldaro - caldaro.1324152@studenti.uniroma1.it
Na Zhu - nana.zhu@hotmail.com
Github repo: https://github.com/giacomolanciano/Azure-Machine-Learning-tutorial
Video tutorial: https://youtu.be/_zvPX6Kk7z8
I want my model to be deployed ! (another story of MLOps)AZUG FR
Speaker : Paul Peton
Putting machine learning into production remains a challenge even though the algorithms have been around for a very long time. Here are some blocks:
– the choice of programming language
– the difficulty of scaling
– fear of black boxes on the part of users
Azure Machine Learning is a new service that allows to control the deployment steps on the appropriate resources (Web App, ACI, AKS) and specially to automate the whole process thanks to the Python SDK.
How to use Azure Machine Learning service to manage the lifecycle of your models. Azure Machine Learning uses a Machine Learning Operations (MLOps) approach, which improves the quality and consistency of your machine learning solutions.
The structure of a Machine Learning code base can have a large impact on effective collaboration and time to production.
In this talk I will present our solution developed for the FutureOps Matching Automation project and talk about lessons learned and best practices.
Delivered at Pittsburgh Tech Fest - 6/10/2017
Knowledge is power, but is it if you're not using it? What if the application you delivered to your customers was extremely intelligent? It could retrieve, analyze and use the massive amounts of data that businesses are generating at an astronomical rate.
It could analyze business deals, predict potential issues, proactively recommend business decisions and estimate profit, loss and risks.
Those things provide direct benefits to your company. Churning through that data by hand doesn't. Enter Azure Machine Learning.
In this session you will learn how to integrate Azure Machine Learning into your existing applications and workflows with REST services. You will learn how to deliver a modular, maintainable solution to your customers that allows them to analyze their data.
You will learn to:
* Numerous ways to abstract business rules, workflows, AI (Machine Learning) and more into your applications
* How to Integrate Azure Machine Learning into your existing Applications and Processes
* Create Azure Machine Learning Experiments
* Retrieve the Score from an Azure Machine Learning Experiment and integrate it into your applications and processes
* Integrate numerous Machine Learning Experiments from the Azure Machine Learning Marketplace into your existing applications and processes
* Learn various concepts for abstracting and managing services and api's.
Metaflow: The ML Infrastructure at NetflixBill Liu
Metaflow was started at Netflix to answer a pressing business need: How to enable an organization of data scientists, who are not software engineers by training, build and deploy end-to-end machine learning workflows and applications independently. We wanted to provide the best possible user experience for data scientists, allowing them to focus on parts they like (modeling using their favorite off-the-shelf libraries) while providing robust built-in solutions for the foundational infrastructure: data, compute, orchestration, and versioning.
Today, the open-source Metaflow powers hundreds of business-critical ML projects at Netflix and other companies from bioinformatics to real estate.
In this talk, you will learn about:
- What to expect from a modern ML infrastructure stack.
- Using Metaflow to boost the productivity of your data science organization, based on lessons learned from Netflix.
- Deployment strategies for a full stack of ML infrastructure that plays nicely with your existing systems and policies.
https://www.aicamp.ai/event/eventdetails/W2021080510
How To Become A Cloud Engineer | Cloud Engineer Salary | Cloud Computing Engi...Simplilearn
This presentation on how to become a Cloud Computing Engineer will help you understand who is a cloud computing engineer, the different roles within cloud computing, the various steps to become a cloud computing engineer, and the salaries they can expect.
Cloud computing engineers are skilled professionals experienced in cloud computing platforms, programming languages, storage, networking, DevOps, and other cloud services.
Let us now take a look at how you can become a skilled cloud computing engineer.
Simplilearn’s Cloud Architect Master’s Program will build your Amazon Web Services (AWS) and Microsoft Azure cloud expertise from the ground up. You’ll learn to master the architectural principles and services of two of the top cloud platforms, design and deploy highly scalable, fault-tolerant applications and develop skills to transform yourself into an AWS and Azure cloud architect.
Why become a Cloud Architect?
With the increasing focus on cloud computing and infrastructure over the last several years, cloud architects are in great demand worldwide. Many organizations have moved to cloud platforms for better scalability, mobility, and security, and cloud solutions architects are among the highest paid professionals in the IT industry.
According to a study by Goldman Sachs, cloud computing is one of the top three initiatives planned by IT executives as they make cloud infrastructure an integral part of their organizations. According to Forbes, enterprise IT architects with cloud computing expertise are earning a median salary of $137,957.
Learn more at https://www.simplilearn.com/cloud-solutions-architect-masters-program-training
Kyrylo Perevozchykov "Continuous delivery for Machine Learning, the future of...Fwdays
MLOps itself is a derivative of DevOps, the thought being that there is an entire industry that exists for “Ops” for normal software, and that such an industry will need to emerge for ML as well. But it hasn’t yet. Various technologies has made it easy for people to build predictive models, so people have lots of predictive models now. But to get value out of models you have to deploy, monitor, and maintain them. Very few people know how to do this, even fewer than know how to build a good model in the first place.
This talk will be dedicated to the plans of what is MLOps, what is cases and how it will develop and evolve into a new industry.
Webinar GLUGNet - Machine Learning.Net and Windows Machine LearningBruno Capuano
Slides used during the webinar session on Machine Learning.Net and Windows Machine Learning on 2019 02 21 for the GLUGnet User Group for .NET, Web, Mobile, Database
Getting Started with Machine Learning.Net & Windows Machine LearningBruno Capuano
Slides used on the event [Getting Started with Machine Learning.Net & Windows Machine Learning] hosted on November 22, in the Mississauga .NET User Group
Using MLOps to Bring ML to Production/The Promise of MLOpsWeaveworks
In this final Weave Online User Group of 2019, David Aronchick asks: have you ever struggled with having different environments to build, train and serve ML models, and how to orchestrate between them? While DevOps and GitOps have made huge traction in recent years, many customers struggle to apply these practices to ML workloads. This talk will focus on the ways MLOps has helped to effectively infuse AI into production-grade applications through establishing practices around model reproducibility, validation, versioning/tracking, and safe/compliant deployment. We will also talk about the direction for MLOps as an industry, and how we can use it to move faster, with more stability, than ever before.
The recording of this session is on our YouTube Channel here: https://youtu.be/twsxcwgB0ZQ
Speaker: David Aronchick, Head of Open Source ML Strategy, Microsoft
Bio: David leads Open Source Machine Learning Strategy at Azure. This means he spends most of his time helping humans to convince machines to be smarter. He is only moderately successful at this. Previously, David led product management for Kubernetes at Google, launched GKE, and co-founded the Kubeflow project. David has also worked at Microsoft, Amazon and Chef and co-founded three startups.
Sign up for a free Machine Learning Ops Workshop: http://bit.ly/MLOps_Workshop_List
Weaveworks will cover concepts such as GitOps (operations by pull request), Progressive Delivery (canary, A/B, blue-green), and how to apply those approaches to your machine learning operations to mitigate risk.
Highly-scalable Reinforcement Learning RLlib for Real-world ApplicationsBill Liu
website: https://learn.xnextcon.com/event/eventdetails/W20051110
video: https://www.youtube.com/watch?v=8tG8PJC6oaU
In reinforcement learning (RL), an agent learns how to optimize performance solely by collecting experience in the real world or via a simulator. RL is being applied to problems such as decision making, process optimization (e.g., manufacturing and supply chains), ad serving, recommendations, self-driving cars, and algorithmic trading.
In this talk, I will discuss RLlib, a reinforcement learning library built on Ray with a strong focus on large-scale execution and scalability, ease-of-use for general users, as well as customizability for developers and researchers.
RLlib offers autonomous task-learning via many common RL algorithms and it scales from a laptop to a cluster with hundreds of machines. It is used by dozens of organizations, from startups to research labs to large organizations. You will see RLlib in action with a live demo.
MLOps and Reproducible ML on AWS with Kubeflow and SageMakerProvectus
Looking to implement MLOps using AWS services and Kubeflow? Come and learn about machine learning from the experts of Provectus and Amazon Web Services (AWS)!
Businesses recognize that machine learning projects are important but go beyond just building and deploying models, which is mostly done by organizations. Successful ML projects entail a complete lifecycle involving ML, DevOps, and data engineering and are built on top of ML infrastructure.
AWS and Amazon SageMaker provide a foundation for building infrastructure for machine learning while Kubeflow is a great open source project, which is not given enough credit in the AWS community. In this webinar, we show how to design and build an end-to-end ML infrastructure on AWS.
Agenda
- Introductions
- Case Study: GoCheck Kids
- Overview of AWS Infrastructure for Machine Learning
- Provectus ML Infrastructure on AWS
- Experimentation
- MLOps
- Feature Store
Intended Audience
Technology executives & decision makers, manager-level tech roles, data engineers & data scientists, ML practitioners & ML engineers, and developers
Presenters
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Qingwei Li, ML Specialist Solutions Architect, AWS
Feel free to share this presentation with your colleagues and don't hesitate to reach out to us at info@provectus.com if you have any questions!
REQUEST WEBINAR: https://provectus.com/webinar-mlops-and-reproducible-ml-on-aws-with-kubeflow-and-sagemaker-aug-2020/
Infuse your apps, websites and bots with intelligent algorithms to see, hear, speak, understand and interpret your user needs through natural methods of communication. Azure Cognitive Services are APIs, SDKs, and services available to help developers build intelligent applications without having direct AI or data science skills or knowledge.
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
In this session, we will take a deep-dive into the DevOps process that comes with Azure Machine Learning service, a cloud service that you can use to track as you build, train, deploy and manage models. We zoom into how the data science process can be made traceable and deploy the model with Azure DevOps to a Kubernetes cluster.
At the end of this session, you will have a good grasp of the technological building blocks of Azure machine learning services and can bring a machine learning project safely into production.
The structure of a Machine Learning code base can have a large impact on effective collaboration and time to production.
In this talk I will present our solution developed for the FutureOps Matching Automation project and talk about lessons learned and best practices.
Delivered at Pittsburgh Tech Fest - 6/10/2017
Knowledge is power, but is it if you're not using it? What if the application you delivered to your customers was extremely intelligent? It could retrieve, analyze and use the massive amounts of data that businesses are generating at an astronomical rate.
It could analyze business deals, predict potential issues, proactively recommend business decisions and estimate profit, loss and risks.
Those things provide direct benefits to your company. Churning through that data by hand doesn't. Enter Azure Machine Learning.
In this session you will learn how to integrate Azure Machine Learning into your existing applications and workflows with REST services. You will learn how to deliver a modular, maintainable solution to your customers that allows them to analyze their data.
You will learn to:
* Numerous ways to abstract business rules, workflows, AI (Machine Learning) and more into your applications
* How to Integrate Azure Machine Learning into your existing Applications and Processes
* Create Azure Machine Learning Experiments
* Retrieve the Score from an Azure Machine Learning Experiment and integrate it into your applications and processes
* Integrate numerous Machine Learning Experiments from the Azure Machine Learning Marketplace into your existing applications and processes
* Learn various concepts for abstracting and managing services and api's.
Metaflow: The ML Infrastructure at NetflixBill Liu
Metaflow was started at Netflix to answer a pressing business need: How to enable an organization of data scientists, who are not software engineers by training, build and deploy end-to-end machine learning workflows and applications independently. We wanted to provide the best possible user experience for data scientists, allowing them to focus on parts they like (modeling using their favorite off-the-shelf libraries) while providing robust built-in solutions for the foundational infrastructure: data, compute, orchestration, and versioning.
Today, the open-source Metaflow powers hundreds of business-critical ML projects at Netflix and other companies from bioinformatics to real estate.
In this talk, you will learn about:
- What to expect from a modern ML infrastructure stack.
- Using Metaflow to boost the productivity of your data science organization, based on lessons learned from Netflix.
- Deployment strategies for a full stack of ML infrastructure that plays nicely with your existing systems and policies.
https://www.aicamp.ai/event/eventdetails/W2021080510
How To Become A Cloud Engineer | Cloud Engineer Salary | Cloud Computing Engi...Simplilearn
This presentation on how to become a Cloud Computing Engineer will help you understand who is a cloud computing engineer, the different roles within cloud computing, the various steps to become a cloud computing engineer, and the salaries they can expect.
Cloud computing engineers are skilled professionals experienced in cloud computing platforms, programming languages, storage, networking, DevOps, and other cloud services.
Let us now take a look at how you can become a skilled cloud computing engineer.
Simplilearn’s Cloud Architect Master’s Program will build your Amazon Web Services (AWS) and Microsoft Azure cloud expertise from the ground up. You’ll learn to master the architectural principles and services of two of the top cloud platforms, design and deploy highly scalable, fault-tolerant applications and develop skills to transform yourself into an AWS and Azure cloud architect.
Why become a Cloud Architect?
With the increasing focus on cloud computing and infrastructure over the last several years, cloud architects are in great demand worldwide. Many organizations have moved to cloud platforms for better scalability, mobility, and security, and cloud solutions architects are among the highest paid professionals in the IT industry.
According to a study by Goldman Sachs, cloud computing is one of the top three initiatives planned by IT executives as they make cloud infrastructure an integral part of their organizations. According to Forbes, enterprise IT architects with cloud computing expertise are earning a median salary of $137,957.
Learn more at https://www.simplilearn.com/cloud-solutions-architect-masters-program-training
Kyrylo Perevozchykov "Continuous delivery for Machine Learning, the future of...Fwdays
MLOps itself is a derivative of DevOps, the thought being that there is an entire industry that exists for “Ops” for normal software, and that such an industry will need to emerge for ML as well. But it hasn’t yet. Various technologies has made it easy for people to build predictive models, so people have lots of predictive models now. But to get value out of models you have to deploy, monitor, and maintain them. Very few people know how to do this, even fewer than know how to build a good model in the first place.
This talk will be dedicated to the plans of what is MLOps, what is cases and how it will develop and evolve into a new industry.
Webinar GLUGNet - Machine Learning.Net and Windows Machine LearningBruno Capuano
Slides used during the webinar session on Machine Learning.Net and Windows Machine Learning on 2019 02 21 for the GLUGnet User Group for .NET, Web, Mobile, Database
Getting Started with Machine Learning.Net & Windows Machine LearningBruno Capuano
Slides used on the event [Getting Started with Machine Learning.Net & Windows Machine Learning] hosted on November 22, in the Mississauga .NET User Group
Using MLOps to Bring ML to Production/The Promise of MLOpsWeaveworks
In this final Weave Online User Group of 2019, David Aronchick asks: have you ever struggled with having different environments to build, train and serve ML models, and how to orchestrate between them? While DevOps and GitOps have made huge traction in recent years, many customers struggle to apply these practices to ML workloads. This talk will focus on the ways MLOps has helped to effectively infuse AI into production-grade applications through establishing practices around model reproducibility, validation, versioning/tracking, and safe/compliant deployment. We will also talk about the direction for MLOps as an industry, and how we can use it to move faster, with more stability, than ever before.
The recording of this session is on our YouTube Channel here: https://youtu.be/twsxcwgB0ZQ
Speaker: David Aronchick, Head of Open Source ML Strategy, Microsoft
Bio: David leads Open Source Machine Learning Strategy at Azure. This means he spends most of his time helping humans to convince machines to be smarter. He is only moderately successful at this. Previously, David led product management for Kubernetes at Google, launched GKE, and co-founded the Kubeflow project. David has also worked at Microsoft, Amazon and Chef and co-founded three startups.
Sign up for a free Machine Learning Ops Workshop: http://bit.ly/MLOps_Workshop_List
Weaveworks will cover concepts such as GitOps (operations by pull request), Progressive Delivery (canary, A/B, blue-green), and how to apply those approaches to your machine learning operations to mitigate risk.
Highly-scalable Reinforcement Learning RLlib for Real-world ApplicationsBill Liu
website: https://learn.xnextcon.com/event/eventdetails/W20051110
video: https://www.youtube.com/watch?v=8tG8PJC6oaU
In reinforcement learning (RL), an agent learns how to optimize performance solely by collecting experience in the real world or via a simulator. RL is being applied to problems such as decision making, process optimization (e.g., manufacturing and supply chains), ad serving, recommendations, self-driving cars, and algorithmic trading.
In this talk, I will discuss RLlib, a reinforcement learning library built on Ray with a strong focus on large-scale execution and scalability, ease-of-use for general users, as well as customizability for developers and researchers.
RLlib offers autonomous task-learning via many common RL algorithms and it scales from a laptop to a cluster with hundreds of machines. It is used by dozens of organizations, from startups to research labs to large organizations. You will see RLlib in action with a live demo.
MLOps and Reproducible ML on AWS with Kubeflow and SageMakerProvectus
Looking to implement MLOps using AWS services and Kubeflow? Come and learn about machine learning from the experts of Provectus and Amazon Web Services (AWS)!
Businesses recognize that machine learning projects are important but go beyond just building and deploying models, which is mostly done by organizations. Successful ML projects entail a complete lifecycle involving ML, DevOps, and data engineering and are built on top of ML infrastructure.
AWS and Amazon SageMaker provide a foundation for building infrastructure for machine learning while Kubeflow is a great open source project, which is not given enough credit in the AWS community. In this webinar, we show how to design and build an end-to-end ML infrastructure on AWS.
Agenda
- Introductions
- Case Study: GoCheck Kids
- Overview of AWS Infrastructure for Machine Learning
- Provectus ML Infrastructure on AWS
- Experimentation
- MLOps
- Feature Store
Intended Audience
Technology executives & decision makers, manager-level tech roles, data engineers & data scientists, ML practitioners & ML engineers, and developers
Presenters
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Qingwei Li, ML Specialist Solutions Architect, AWS
Feel free to share this presentation with your colleagues and don't hesitate to reach out to us at info@provectus.com if you have any questions!
REQUEST WEBINAR: https://provectus.com/webinar-mlops-and-reproducible-ml-on-aws-with-kubeflow-and-sagemaker-aug-2020/
Infuse your apps, websites and bots with intelligent algorithms to see, hear, speak, understand and interpret your user needs through natural methods of communication. Azure Cognitive Services are APIs, SDKs, and services available to help developers build intelligent applications without having direct AI or data science skills or knowledge.
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
In this session, we will take a deep-dive into the DevOps process that comes with Azure Machine Learning service, a cloud service that you can use to track as you build, train, deploy and manage models. We zoom into how the data science process can be made traceable and deploy the model with Azure DevOps to a Kubernetes cluster.
At the end of this session, you will have a good grasp of the technological building blocks of Azure machine learning services and can bring a machine learning project safely into production.
Machine Learning operations brings data science to the world of devops. Data scientists create models on their workstations. MLOps adds automation, validation and monitoring to any environment including machine learning on kubernetes. In this session you hear about latest developments and see it in action.
Introduction to Machine learning and Deep LearningNishan Aryal
Overview of Machine Learning and Deep Learning. Brief introduction to different types of BI Reporting tools like Power BI, SSMS, Cortana, Azure ML, TenserFlow and other tools.
In this session we will delve into the world of Azure Databricks and analyze why it is becoming a tool for data Scientist and/or fundamental data Engineer in conjunction with Azure services
I am an instructor of the MLOps workshop for some anonymous startup incubation program where the objectives are (1) to orchestrate and deploy updates to the application and the deep learning model in a unified way. (2) To design a DevOps pipeline to coordinate retrieving the latest best model from the model registry, packaging the web application, deploying the web application and inferencing web service.
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI.
This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
This presentation is the fourth of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
Big Data Advanced Analytics on Microsoft Azure 201904Mark Tabladillo
This talk summarizes key points for big data advanced analytics on Microsoft Azure. First, there is a review of the major technologies. Second, there is a series of technology demos (focusing on VMs, Databricks and Azure ML Service). Third, there is some advice on using the Team Data Science Process to help plan projects. The deck has web resources recommended. This presentation was delivered at the Global Azure Bootcamp 2019, Atlanta GA location (Alpharetta Avalon).
Complete No code solution to Machine Learning using Azure ML Studio. The aim of this presentation is to discuss the capability of Azure ML Studio in enabling any novice to perform ML experiments.
201906 02 Introduction to AutoML with ML.NET 1.0Mark Tabladillo
ML.NET 1.0 release is the first major milestone of a great journey that started in May 2018 when we released ML.NET 0.1 as open source. ML.NET is an open-source and cross-platform machine learning framework for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Recommendation, Image Classification and more.
“Automated ML” is a collection of new technologies from Microsoft to enhance the data science development process. Still in preview, Auto ML for ML.NET 1.0 will be demonstrated in a Deep Learning Virtual Machine running Windows Server 2016. Code examples are in C# and run in Visual Studio Community 2019.
This presentation is the second of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
The breath and depth of Azure products that fall under the AI and ML umbrella can be difficult to follow. In this presentation I’ll first define exactly what AI, ML, and deep learning is, and then go over the various Microsoft AI and ML products and their use cases.
2018 11 14 Artificial Intelligence and Machine Learning in AzureBruno Capuano
Slides used during my session "Artificial Intelligence and Machine Learning in Azure" for The Azure Group (Canada's Azure User Community) on November 14 2018.
Public group
Workshop - Preparing for AI-100 Microsoft Certification Exam Designing and Im...Luis Beltran
These are the slides of the workshop that I presented at the Global AI Bootcamp Munich 2019 in which I discuss the important elements to consider in order to become an Azure AI Engineer Associate certified by Microsoft.
Big Data Adavnced Analytics on Microsoft AzureMark Tabladillo
This presentation provides a survey of the advanced analytics strengths of Microsoft Azure from an enterprise perspective (with these organizations being the bulk of big data users) based on the Team Data Science Process. The talk also covers the range of analytics and advanced analytics solutions available for developers using data science and artificial intelligence from Microsoft Azure.
Global AI on Virtual Tour Oslo - Anomaly Detection using ML.Net on a drone te...Bruno Capuano
Slides used during the session "Anomaly Detection using ML.Net on a drone telemetry from Azure IoT" for the Global AI on Virtual Tour - Oslo on June 2021
2020 08 06 Global XR Talks - Lessons Learned creating a multiplatform AI proj...Bruno Capuano
Slides used during the session "Lessons Learned creating a multiplatform AI project for Azure Kinect and Hololens 2" for the Global XR Talks on the 2020 08 06
2020 06 13 Best of Build 2020 - Canada Community Edition - Artificial Intelli...Bruno Capuano
Slides used during the "Best of Build 2020 - Canada Community Edition" for the Artificial Intelligence session. Shared session with Frank Boucheros. More information on my blog.
Global Azure AI Tour Buenos Aires Argentina, Drones and AIBruno Capuano
Slides used during my session "How to fly a drone with 20 lines of code and use some AI" for the Global AI Tour event. Virtual Mode for Buenos Aires, Argentina.
2020 04 18 Global AI On Tour Monterrey - Program a Drone using AIBruno Capuano
Slides used in my online session "¡Vamos a programar a un dron para que siga rostros!" for the Global AI On Tour Monterrey.
El próximo 18 de Abril estará hablando de drones, Inteligencia Artificial, Docker, y otras sorpresas para el evento gratuito de Global AI On Tour Monterrey !
2020 04 09 Global AI Community Virtual Tour - Drones and AIBruno Capuano
Slides used during my session "Let’s code a drone to follow faces! Using AI, Python, containers and more. As a bonus we will some Enterprise scenarios." as part of the Global AI Community Virtual Tour.
Slides used during the virtual conference, NetCoreConf on April 04, 2020. The session was a introduction to Machine Learning for .Net developers, using ML.Net as the main framework.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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/
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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
3. @CONOSUR.TECH
• Azure Machine Learning
• Tutorial: Predict automobile price with
the designer
• Tutorial: Deploy a machine learning
model with the designer
• Tutorial: Get started with Azure Machine
Learning in your development environment
• How Azure Machine Learning works:
Architecture and concepts
• MLOps with Azure ML
• Azure DataBricks, Notebooks
8. @CONOSUR.TECH
Comment Toxic? (Sentiment)
==RUDE== Dude, you are rude … 1
== OK! == IM GOING TO VANDALIZE … 1
I also found use of the word "humanists” confusing … 0
Oooooh thank you Mr. DietLime … 0
Wikipedia detox data at https://figshare.com/articles/Wikipedia_Talk_Labels_Personal_Attacks/4054689
Features (input) Label (output)
Sentiment Analysis
9. @CONOSUR.TECH
Is this A or B? Is this a toxic comment?
Yes or no
Sentiment analysis explained
10. Example
Comment Toxic? (Sentiment)
==RUDE== Dude, you are rude … 1
== OK! == IM GOING TO VANDALIZE … 1
I also found use of the word "humanists” confusing … 0
Oooooh thank you Mr. DietLime … 0
Important concepts: Data
Prepare Your Data
11. Prepare Your Data
Text Featurizer
Featurized Text
[0.76, 0.65, 0.44, …]
[0.98, 0.43, 0.54, …]
[0.35, 0.73, 0.46, …]
[0.39, 0, 0.75, …]
Example
Text
==RUDE== Dude, you are rude …
== OK! == IM GOING TO VANDALIZE …
I also found use of the word "humanists” …
Oooooh thank you Mr. DietLime …
Important concepts: Transformer
12. Build & Train
Example
Estimator
Comment Toxic? (Sentiment)
==RUDE== Dude, you … 1
== OK! == IM GOING … 1
I also found use of the … 0
Oooooh thank you Mr. … 0
Important concepts: Trainer
13. Comment
==RUDE== Dude, you …
Prediction Function
Predicted Label – Toxic? (Sentiment)
1
Run
Example
Important concepts: Model
14. Azure ML Hello World !
MakeMagicHappen();
https://www.avanade.com/AI
16. @CONOSUR.TECH
Machine Learning on Azure
Domain Specific Pretrained Models
To reduce time to market
Azure
Databricks
Machine
Learning VMs
Popular Frameworks
To build machine learning and deep learning solutions TensorFlowPyTorch ONNX
Azure Machine
Learning
LanguageSpeech
…
SearchVision
Productive Services
To empower data science and development teams
Powerful Hardware
To accelerate deep learning
Scikit-Learn
PyCharm Jupyter
Familiar Data Science Tools
To simplify model development Visual Studio Code Command line
CPU GPU FPGA
From the Intelligent Cloud to the Intelligent Edge@CONOSUR.TECH
17. @CONOSUR.TECH
Building blocks for a Data Science Project
Data
sources
Classical ML
Deep learning
Build and train
models
Experimentation
and pipelines
Hyperparameter
tuning
DevOps for data
scienceDeployment
19. @CONOSUR.TECH
How much is the taxi fare for 1 passenger going from Burlington to Toronto?
Automated Machine Learning
20. @CONOSUR.TECH
Criterion
Loss
Min Samples Split
Min Samples Leaf
XYZ
Parameter 1
Parameter 2
Parameter 3
Parameter 4
…
Distance
Trip time
Car type
Passengers
Time of day
…
Gradient Boosted
Nearest Neighbors
SGD
Bayesian Regression
LGBM
…
Distance Gradient Boosted
Model
Car type
Passengers
Getting started w/machine learning can be hard
Which algorithm? Which parameters?Which features?
Getting started with ML can be
hard
21. @CONOSUR.TECH
N Neighbors
Weights
Metric
P
ZYX
Criterion
Loss
Min Samples Split
Min Samples Leaf
XYZ
Which algorithm? Which parameters?Which features?
Distance
Trip time
Car type
Passengers
Time of day
…
Gradient Boosted
Nearest Neighbors
SGD
Bayesian Regression
LGBM
…
Nearest Neighbors
Model
Iterate
Gradient BoostedDistance
Car brand
Year of make
Car type
Passengers
Trip time
Getting started w/machine learning can be hardGetting started with ML can be
hard
22. @CONOSUR.TECH
Which algorithm? Which parameters?Which features?
Iterate
Getting started w/machine learning can be hardGetting started with ML can be
hard
24. @CONOSUR.TECH
70%95% Feature importance
Distance
Trip time
Car type
Passengers
Time of day
0 1
Model B (70%)
Distance
0 1
Trip time
Car type
Passengers
Time of day
Feature importance Model A (95%)
ML.NET accelerates model development
with model explainability
AutoML accelerates model
development
27. @CONOSUR.TECH
DevOps loop for data science
01010100101001011101101001001001011
01010010001000111101001010101001010
01010100101001011101101001001001011
01010010001000111101001010101001010
00101001011101101001001001011010100
10001000111101001010101001010010101
00101001011101101001
Prepare
Data
Prepare
Register and
Manage Model
Build
Image
…
Build model
(your favorite IDE)
Deploy Service
Monitor Model
Train &
Test Model
28. @CONOSUR.TECH
Model Management in detail
Create/Retrain Model
Enable DevOps with full CI/CD
integration with VSTS
Register Model
Track model versions with a
central model registry
Monitor
Oversea deployments through
Azure AppInsights
29. @CONOSUR.TECH
Experimentation
Use leaderboards, side by side run
comparison and model selection
Conduct a hyperparameter search on
traditional ML or DNN
Leverage service-side capture of run
metrics, output logs and models
Manage training jobs locally, scaled-up or
scaled-out
95
%
80%
75%
90%
85%
31. @CONOSUR.TECH
Azure Machine Learning Pipelines
Prepare data Build & train models Deploy & predict
Data storage
locations
Data ingestion
Data Preparation Model building & training Model deployment
Normalization
Transformation
Validation
Featurization
Hyper-parameter tuning
Automatic model selection
Model testing
Model validation
Deployment
Batch scoring
32. @CONOSUR.TECH
Prepare data Build & train models Deploy & predict
Data storage
locations
Data ingestion
Data Preparation Model building & training Model deployment
Normalization
Transformation
Validation
Featurization
Hyper-parameter tuning
Automatic model selection
Model testing
Model validation
Deployment
Batch scoring
Normalization
Transformation
Validation
Featurization
Hyper-parameter tuning
Automatic model selection
Model testing Testing error
Azure Machine Learning Pipelines
33. @CONOSUR.TECH
Prepare data Build & train models Deploy & predict
Data storage
locations
Data ingestion
Data Preparation Model building & training Model deployment
Normalization
Transformation
Validation
Featurization
Hyper-parameter tuning
Automatic model selection
Model testing
Model validation
Deployment
Batch scoring
Normalization
Transformation
Validation
Featurization
Hyper-parameter tuning
Automatic model selection
Model testing Testing errorModel testing
Model validation
Deployment
Batch scoring
Error
resolved
Azure Machine Learning Pipelines
34. @CONOSUR.TECH
Azure Machine Learning Pipelines with
new data
Prepare data Build & train models Deploy & predict
Data storage
locations
Data ingestion
Data Preparation Model building & training Model deployment
Normalization
Transformation
Validation
Featurization
Hyper-parameter tuning
Automatic model selection
Model testing
Model validation
Deployment
Batch scoring
Normalization
Transformation
Validation
Featurization
Hyper-parameter tuning
Automatic model selection
Model testing
Model validation
New
data
Deployment
Batch scoring
35. @CONOSUR.TECH
Advantages of Azure ML Pipelines
Unattended runs
Schedule a few steps to run in parallel or
in sequence to focus on other tasks while
your pipeline runs
Mixed and diverse compute
Use multiple pipelines that are reliably
coordinated across heterogeneous and
scalable computes and storages
Reusability
Create templates of pipelines for specific
scenarios such as retraining and batch
scoring
Tracking and versioning
Name and version your data sources,
inputs and outputs with the pipelines SDK
37. @CONOSUR.TECH
Popular Frameworks
Use your favorite machine learning frameworks without getting locked into one framework
ONNX
Community project created by Facebook and Microsoft
Use the best tool for the job. Train in one framework and
transfer to another for inference
TensorFlow PyTorch Scikit-Learn
MXNet Chainer Keras
40. @CONOSUR.TECH
Flexible Deployment
Deploy and manage models on intelligent cloud and edge
Train & deploy Train & deploy
Deploy
Model optimization for cloud & edge
Manage models in production
Capture model telemetry
Retrain models
41. @CONOSUR.TECH
Deploy Azure ML models at scale
Azure Machine Learning Service
Azure Machine
Learning
Experimentation
Cognitive
Services
External
Model
Model Registry
Register Model
Cloud Service
Heavy Edge
Light Edge
Image
Registry
Create &
Register Image
Your IDE
Scoring File
Image
Registry
Deployment & Model
Monitoring