Machine learning allows us to build predictive analytics solutions of tomorrow - these solutions allow us to better diagnose and treat patients, correctly recommend interesting books or movies, and even make the self-driving car a reality. Microsoft Azure Machine Learning (Azure ML) is a fully-managed Platform-as-a-Service (PaaS) for building these predictive analytics solutions. It is very easy to build solutions with it, helping to overcome the challenges most businesses have in deploying and using machine learning. In this presentation, we will take a look at how to create ML models with Azure ML Studio and deploy those models to production in minutes.
A presentation covers how data science is connected to build effective machine learning solutions. How to build end to end solutions in Azure ML. How to build, model, and evaluate algorithms in Azure ML.
In this talk, we will present an overview of Azure Machine Learning, a fully managed cloud service that enables you to easily build, deploy, and share predictive analytics solutions. We will start with the basics of machine learning and end with a demo that uses real world data.
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.
The session is about creating, training, evaluating and deploying machine learning with no-code approach using Azure AutoML.
* NO MACHINE LEARNING EXPERIENCE REQUIRED *
Agenda:
1. Introduction to Machine Learning
2. What is AutoML (Automated Machine Learning) ?
3. AutoML versus Conventional ML practices
4. Intro to Azure Automated Machine Learning
5. Hands-on demo
6 Contest
6. Learning resources
7. Conclusion
Machine Learning Model Deployment: Strategy to ImplementationDataWorks Summit
This talk will introduce participants to the theory and practice of machine learning in production. The talk will begin with an intro on machine learning models and data science systems and then discuss data pipelines, containerization, real-time vs. batch processing, change management and versioning.
As part of this talk, an audience will learn more about:
• How data scientists can have the complete self-service capability to rapidly build, train, and deploy machine learning models.
• How organizations can accelerate machine learning from research to production while preserving the flexibility and agility of data scientists and modern business use cases demand.
A small demo will showcase how to rapidly build, train, and deploy machine learning models in R, python, and Spark, and continue with a discussion of API services, RESTful wrappers/Docker, PMML/PFA, Onyx, SQLServer embedded models, and
lambda functions.
Speakers
Sagar Kewalramani, Solutions Architect
Cloudera
Justin Norman, Director, Research and Data Science Services
Cloudera Fast Forward Labs
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.
A presentation covers how data science is connected to build effective machine learning solutions. How to build end to end solutions in Azure ML. How to build, model, and evaluate algorithms in Azure ML.
In this talk, we will present an overview of Azure Machine Learning, a fully managed cloud service that enables you to easily build, deploy, and share predictive analytics solutions. We will start with the basics of machine learning and end with a demo that uses real world data.
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.
The session is about creating, training, evaluating and deploying machine learning with no-code approach using Azure AutoML.
* NO MACHINE LEARNING EXPERIENCE REQUIRED *
Agenda:
1. Introduction to Machine Learning
2. What is AutoML (Automated Machine Learning) ?
3. AutoML versus Conventional ML practices
4. Intro to Azure Automated Machine Learning
5. Hands-on demo
6 Contest
6. Learning resources
7. Conclusion
Machine Learning Model Deployment: Strategy to ImplementationDataWorks Summit
This talk will introduce participants to the theory and practice of machine learning in production. The talk will begin with an intro on machine learning models and data science systems and then discuss data pipelines, containerization, real-time vs. batch processing, change management and versioning.
As part of this talk, an audience will learn more about:
• How data scientists can have the complete self-service capability to rapidly build, train, and deploy machine learning models.
• How organizations can accelerate machine learning from research to production while preserving the flexibility and agility of data scientists and modern business use cases demand.
A small demo will showcase how to rapidly build, train, and deploy machine learning models in R, python, and Spark, and continue with a discussion of API services, RESTful wrappers/Docker, PMML/PFA, Onyx, SQLServer embedded models, and
lambda functions.
Speakers
Sagar Kewalramani, Solutions Architect
Cloudera
Justin Norman, Director, Research and Data Science Services
Cloudera Fast Forward Labs
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.
MLOps and Data Quality: Deploying Reliable ML Models in ProductionProvectus
Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure.
For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).
Agenda
- Data Quality and why it matters
- Challenges and solutions of Data Testing
- Challenges and solutions of Model Testing
- MLOps pipelines and why they matter
- How to expand validation pipelines for Data Quality
The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
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
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.
This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/09/mlops-managing-data-and-workflows-for-efficient-model-development-and-deployment-a-presentation-from-airbus/
Konstantinos Balafas, Head of AI Data, and Carlo Dal Mutto, Director of Engineering, both of Airbus, present the “MLOps: Managing Data and Workflows for Efficient Model Development and Deployment” tutorial at the May 2022 Embedded Vision Summit.
Machine learning operations (MLOps) is the engineering field focused on techniques for developing and deploying machine learning solutions at scale. As the name suggests, MLOps is a combination of machine learning development (“ML”) and software/IT operations (“Ops”). Blending these two words is particularly complex, given their diverse nature. ML development is characterized by research and experimental components, dealing with large amounts of data and complex operations, while software and IT operations aim at streamlining software deployment in products.
Typical problems addressed by MLOps include data management (labeling, organization, storage), ML model and pipeline training repeatability, error analysis, model integration and deployment and model monitoring. In this talk, Dal Mutto and Balafas present practical MLOps techniques useful for tackling a variety of MLOps needs. They illustrate these techniques with real-world examples from their work developing autonomous flying capabilities as part of the Wayfinder team at Acubed, the Silicon Valley innovation center of Airbus.
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
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.
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...Databricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size. In this deep-dive session, through a complete ML model life-cycle example, you will walk away with:
MLflow concepts and abstractions for models, experiments, and projects
How to get started with MLFlow
Understand aspects of MLflow APIs
Using tracking APIs during model training
Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
Package, save, and deploy an MLflow model
Serve it using MLflow REST API
What’s next and how to contribute
Regulating Generative AI - LLMOps pipelines with TransparencyDebmalya Biswas
The growing adoption of Gen AI, esp. LLMs, has re-ignited the discussion around AI Regulations — to ensure that AI/ML systems are responsibly trained and deployed. Unfortunately, this effort is complicated by multiple governmental organizations and regulatory bodies releasing their own guidelines and policies with little to no agreement on the definition of terms.
Rather than trying to understand and regulate all types of AI, we recommend a different (and practical) approach in this talk based on AI Transparency —
to transparently outline the capabilities of the AI system based on its training methodology and set realistic expectations with respect to what it can (and cannot) do.
We outline LLMOps architecture patterns and show how the proposed approach can be integrated at different stages of the LLMOps pipeline capturing the model's capabilities. In addition, the AI system provider also specifies scenarios where (they believe that) the system can make mistakes, and recommends a ‘safe’ approach with guardrails for those scenarios.
Purpose of this presentation is to highlight how end to end machine learning looks like in real world enterprise. This is to provide insight to aspiring data scientist who have been through courses or education in ML that mostly focus on ML algorithms and not end to end pipeline.
Architecture and components mentioned in Slide 11 will be discussed in detailed in series of post on LinkedIn over the course of next few month
To get updates on this follow me on LinkedIn or search/follow hashtag #end2endDS. Post will be active in August 2019 and will be posted till September 2019
MLOps (a compound of “machine learning” and “operations”) is a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Similar to the DevOps term in the software development world, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire ML lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics.
To watch the full presentation click here: https://info.cnvrg.io/mlopsformachinelearning
In this webinar, we’ll discuss core practices in MLOps that will help data science teams scale to the enterprise level. You’ll learn the primary functions of MLOps, and what tasks are suggested to accelerate your teams machine learning pipeline. Join us in a discussion with cnvrg.io Solutions Architect, Aaron Schneider, and learn how teams use MLOps for more productive machine learning workflows.
- Reduce friction between science and engineering
- Deploy your models to production faster
- Health, diagnostics and governance of ML models
- Kubernetes as a core platform for MLOps
- Support advanced use-cases like continual learning with MLOps
So you got a handle on what Big Data is and how you can use it to find business value in your data. Now you need an understanding of the Microsoft products that can be used to create a Big Data solution. Microsoft has many pieces of the puzzle and in this presentation I will show how they fit together. How does Microsoft enhance and add value to Big Data? From collecting data, transforming it, storing it, to visualizing it, I will show you Microsoft’s solutions for every step of the way
Think of big data as all data, no matter what the volume, velocity, or variety. The simple truth is a traditional on-prem data warehouse will not handle big data. So what is Microsoft’s strategy for building a big data solution? And why is it best to have this solution in the cloud? That is what this presentation will cover. Be prepared to discover all the various Microsoft technologies and products from collecting data, transforming it, storing it, to visualizing it. My goal is to help you not only understand each product but understand how they all fit together, so you can be the hero who builds your companies big data solution.
MLOps and Data Quality: Deploying Reliable ML Models in ProductionProvectus
Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure.
For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).
Agenda
- Data Quality and why it matters
- Challenges and solutions of Data Testing
- Challenges and solutions of Model Testing
- MLOps pipelines and why they matter
- How to expand validation pipelines for Data Quality
The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
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
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.
This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/09/mlops-managing-data-and-workflows-for-efficient-model-development-and-deployment-a-presentation-from-airbus/
Konstantinos Balafas, Head of AI Data, and Carlo Dal Mutto, Director of Engineering, both of Airbus, present the “MLOps: Managing Data and Workflows for Efficient Model Development and Deployment” tutorial at the May 2022 Embedded Vision Summit.
Machine learning operations (MLOps) is the engineering field focused on techniques for developing and deploying machine learning solutions at scale. As the name suggests, MLOps is a combination of machine learning development (“ML”) and software/IT operations (“Ops”). Blending these two words is particularly complex, given their diverse nature. ML development is characterized by research and experimental components, dealing with large amounts of data and complex operations, while software and IT operations aim at streamlining software deployment in products.
Typical problems addressed by MLOps include data management (labeling, organization, storage), ML model and pipeline training repeatability, error analysis, model integration and deployment and model monitoring. In this talk, Dal Mutto and Balafas present practical MLOps techniques useful for tackling a variety of MLOps needs. They illustrate these techniques with real-world examples from their work developing autonomous flying capabilities as part of the Wayfinder team at Acubed, the Silicon Valley innovation center of Airbus.
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
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.
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...Databricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size. In this deep-dive session, through a complete ML model life-cycle example, you will walk away with:
MLflow concepts and abstractions for models, experiments, and projects
How to get started with MLFlow
Understand aspects of MLflow APIs
Using tracking APIs during model training
Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
Package, save, and deploy an MLflow model
Serve it using MLflow REST API
What’s next and how to contribute
Regulating Generative AI - LLMOps pipelines with TransparencyDebmalya Biswas
The growing adoption of Gen AI, esp. LLMs, has re-ignited the discussion around AI Regulations — to ensure that AI/ML systems are responsibly trained and deployed. Unfortunately, this effort is complicated by multiple governmental organizations and regulatory bodies releasing their own guidelines and policies with little to no agreement on the definition of terms.
Rather than trying to understand and regulate all types of AI, we recommend a different (and practical) approach in this talk based on AI Transparency —
to transparently outline the capabilities of the AI system based on its training methodology and set realistic expectations with respect to what it can (and cannot) do.
We outline LLMOps architecture patterns and show how the proposed approach can be integrated at different stages of the LLMOps pipeline capturing the model's capabilities. In addition, the AI system provider also specifies scenarios where (they believe that) the system can make mistakes, and recommends a ‘safe’ approach with guardrails for those scenarios.
Purpose of this presentation is to highlight how end to end machine learning looks like in real world enterprise. This is to provide insight to aspiring data scientist who have been through courses or education in ML that mostly focus on ML algorithms and not end to end pipeline.
Architecture and components mentioned in Slide 11 will be discussed in detailed in series of post on LinkedIn over the course of next few month
To get updates on this follow me on LinkedIn or search/follow hashtag #end2endDS. Post will be active in August 2019 and will be posted till September 2019
MLOps (a compound of “machine learning” and “operations”) is a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Similar to the DevOps term in the software development world, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire ML lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics.
To watch the full presentation click here: https://info.cnvrg.io/mlopsformachinelearning
In this webinar, we’ll discuss core practices in MLOps that will help data science teams scale to the enterprise level. You’ll learn the primary functions of MLOps, and what tasks are suggested to accelerate your teams machine learning pipeline. Join us in a discussion with cnvrg.io Solutions Architect, Aaron Schneider, and learn how teams use MLOps for more productive machine learning workflows.
- Reduce friction between science and engineering
- Deploy your models to production faster
- Health, diagnostics and governance of ML models
- Kubernetes as a core platform for MLOps
- Support advanced use-cases like continual learning with MLOps
So you got a handle on what Big Data is and how you can use it to find business value in your data. Now you need an understanding of the Microsoft products that can be used to create a Big Data solution. Microsoft has many pieces of the puzzle and in this presentation I will show how they fit together. How does Microsoft enhance and add value to Big Data? From collecting data, transforming it, storing it, to visualizing it, I will show you Microsoft’s solutions for every step of the way
Think of big data as all data, no matter what the volume, velocity, or variety. The simple truth is a traditional on-prem data warehouse will not handle big data. So what is Microsoft’s strategy for building a big data solution? And why is it best to have this solution in the cloud? That is what this presentation will cover. Be prepared to discover all the various Microsoft technologies and products from collecting data, transforming it, storing it, to visualizing it. My goal is to help you not only understand each product but understand how they all fit together, so you can be the hero who builds your companies big data solution.
Big Data, IoT, data lake, unstructured data, Hadoop, cloud, and massively parallel processing (MPP) are all just fancy words unless you can find uses cases for all this technology. Join me as I talk about the many use cases I have seen, from streaming data to advanced analytics, broken down by industry. I’ll show you how all this technology fits together by discussing various architectures and the most common approaches to solving data problems and hopefully set off light bulbs in your head on how big data can help your organization make better business decisions.
Microsoft Fabric is the next version of Azure Data Factory, Azure Data Explorer, Azure Synapse Analytics, and Power BI. It brings all of these capabilities together into a single unified analytics platform that goes from the data lake to the business user in a SaaS-like environment. Therefore, the vision of Fabric is to be a one-stop shop for all the analytical needs for every enterprise and one platform for everyone from a citizen developer to a data engineer. Fabric will cover the complete spectrum of services including data movement, data lake, data engineering, data integration and data science, observational analytics, and business intelligence. With Fabric, there is no need to stitch together different services from multiple vendors. Instead, the customer enjoys end-to-end, highly integrated, single offering that is easy to understand, onboard, create and operate.
This is a hugely important new product from Microsoft and I will simplify your understanding of it via a presentation and demo.
Agenda:
What is Microsoft Fabric?
Workspaces and capacities
OneLake
Lakehouse
Data Warehouse
ADF
Power BI / DirectLake
Resources
Take Action: The New Reality of Data-Driven BusinessInside Analysis
The Briefing Room with Dr. Robin Bloor and WebAction
Live Webcast on July 23, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=360d371d3a49ad256942f55350aa0a8b
The waiting used to be the hardest part, but not anymore. Today’s cutting-edge enterprises can seize opportunities faster than ever, thanks to an array of technologies that enable real-time responsiveness across the spectrum of business processes. Early adopters are solving critical business challenges by enabling the rapid-fire design, development and production of very specific applications. Functionality can range from improved customer engagement to dynamic machine-to-machine interactions.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor, who will tout a new era in data-driven organizations, and why a data flow architecture will soon be critical for industry leaders. He’ll be briefed by Sami Akbay of WebAction, who will showcase his company’s real-time data management platform, which combines all the component parts needed to access, process and leverage data big and small. He’ll explain how this new approach can provide game-changing power to organizations of all types and sizes.
Visit InsideAnlaysis.com for more information.
Meetup Toulouse Microsoft Azure : Bâtir une solution IoTAlex Danvy
Un tour d'horizon des solutions disponibles chez Microsoft pour bâtir une solution IoT. Il est question de Microsoft Azure bien-sûr, mais pas seulement. Windows, Machine Learning, Bots, OCF/AllJoyn, Hololens
IoT - Retour d'expérience de projets clients dans le domaine IoT. Michael Epprecht, Technical Specialist in the Global Black Belt IoT Team at Microsoft. Conférence donnée dans le cadre du Swiss Data Forum, du 24 novembre 2015 à Lausanne
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Enabling Next Gen Analytics with Azure Data Lake and StreamSetsStreamsets Inc.
Big data and the cloud are perfect partners for companies who want to unlock maximum value from all of their unstructured, semi-structured, and structured data. The challenge has been how to create and manage a reliable end-to-end solution that spans data ingestion, storage and analysis in the face of the volume, velocity and variety of big data sources.
In this webinar, we will show you how to achieve big data bliss by combining StreamSets Data Collector, which specializes in creating and running complex any-to-any dataflows, with Microsoft's Azure Data Lake and Azure analytic solutions.
We will walk through an example of how a major bank is using StreamSets to transport their on-premise data to the Azure Cloud Computing Platform and Azure Data Lake to take advantage of analytics tools with unprecedented scale and performance.
We live in a world of unprecedented change. To be successful in this world of change, you will need to develop a data culture, creating an environment where every team and every individual is empowered to do great things because of the data at their fingertips. In this event you will learn how to create a culture of data and how the Microsoft Modern BI platform and tools can help you to can harness the power of data once only reserved for data scientists. Learn about how to tap into the power of natural language, self-service business insights and visualization capabilities – and make insights available to anyone, anywhere, at any time.
My Slidedeck about Common Data Service and Model. This technology is under development so content is subject to change and based on current service on 4/13/2018
Similar to Overview on Azure Machine Learning (20)
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Data Warehousing Trends, Best Practices, and Future OutlookJames Serra
Over the last decade, the 3Vs of data - Volume, Velocity & Variety has grown massively. The Big Data revolution has completely changed the way companies collect, analyze & store data. Advancements in cloud-based data warehousing technologies have empowered companies to fully leverage big data without heavy investments both in terms of time and resources. But, that doesn’t mean building and managing a cloud data warehouse isn’t accompanied by any challenges. From deciding on a service provider to the design architecture, deploying a data warehouse tailored to your business needs is a strenuous undertaking. Looking to deploy a data warehouse to scale your company’s data infrastructure or still on the fence? In this presentation you will gain insights into the current Data Warehousing trends, best practices, and future outlook. Learn how to build your data warehouse with the help of real-life use-cases and discussion on commonly faced challenges. In this session you will learn:
- Choosing the best solution - Data Lake vs. Data Warehouse vs. Data Mart
- Choosing the best Data Warehouse design methodologies: Data Vault vs. Kimball vs. Inmon
- Step by step approach to building an effective data warehouse architecture
- Common reasons for the failure of data warehouse implementations and how to avoid them
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Power BI Overview, Deployment and GovernanceJames Serra
Deploying Power BI in a large enterprise is a complex task, and one that requires a lot of thought and planning. The purpose of this presentation is to help you make your Power BI deployment a success. After a quick Power BI overview, I’ll discuss deployment strategies, common usage scenarios, how to store and refresh data, prototyping options, how to share externally, and then finish with how to administer and secure Power BI. I’ll outline considerations and best practices for achieving an optimal, well-performing, enterprise level Power BI deployment.
Power BI has become a product with a ton of exciting features. This presentation will give an overview of some of them, including Power BI Desktop, Power BI service, what’s new, integration with other services, Power BI premium, and administration.
Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.
AI for an intelligent cloud and intelligent edge: Discover, deploy, and manag...James Serra
Discover, manage, deploy, monitor – rinse and repeat. In this session we show how Azure Machine Learning can be used to create the right AI model for your challenge and then easily customize it using your development tools while relying on Azure ML to optimize them to run in hardware accelerated environments for the cloud and the edge using FPGAs and Neural Network accelerators. We then show you how to deploy the model to highly scalable web services and nimble edge applications that Azure can manage and monitor for you. Finally, we illustrate how you can leverage the model telemetry to retrain and improve your content.
Power BI for Big Data and the New Look of Big Data SolutionsJames Serra
New features in Power BI give it enterprise tools, but that does not mean it automatically creates an enterprise solution. In this talk we will cover these new features (composite models, aggregations tables, dataflow) as well as Azure Data Lake Store Gen2, and describe the use cases and products of an individual, departmental, and enterprise big data solution. We will also talk about why a data warehouse and cubes still should be part of an enterprise solution, and how a data lake should be organized.
In three years I went from a complete unknown to a popular blogger, speaker at PASS Summit, a SQL Server MVP, and then joined Microsoft. Along the way I saw my yearly income triple. Is it because I know some secret? Is it because I am a genius? No! It is just about laying out your career path, setting goals, and doing the work.
I'll cover tips I learned over my career on everything from interviewing to building your personal brand. I'll discuss perm positions, consulting, contracting, working for Microsoft or partners, hot fields, in-demand skills, social media, networking, presenting, blogging, salary negotiating, dealing with recruiters, certifications, speaking at major conferences, resume tips, and keys to a high-paying career.
Your first step to enhancing your career will be to attend this session! Let me be your career coach!
Is the traditional data warehouse dead?James Serra
With new technologies such as Hive LLAP or Spark SQL, do I still need a data warehouse or can I just put everything in a data lake and report off of that? No! In the presentation I’ll discuss why you still need a relational data warehouse and how to use a data lake and a RDBMS data warehouse to get the best of both worlds. I will go into detail on the characteristics of a data lake and its benefits and why you still need data governance tasks in a data lake. I’ll also discuss using Hadoop as the data lake, data virtualization, and the need for OLAP in a big data solution. And I’ll put it all together by showing common big data architectures.
Differentiate Big Data vs Data Warehouse use cases for a cloud solutionJames Serra
It can be quite challenging keeping up with the frequent updates to the Microsoft products and understanding all their use cases and how all the products fit together. In this session we will differentiate the use cases for each of the Microsoft services, explaining and demonstrating what is good and what isn't, in order for you to position, design and deliver the proper adoption use cases for each with your customers. We will cover a wide range of products such as Databricks, SQL Data Warehouse, HDInsight, Azure Data Lake Analytics, Azure Data Lake Store, Blob storage, and AAS as well as high-level concepts such as when to use a data lake. We will also review the most common reference architectures (“patterns”) witnessed in customer adoption.
Azure SQL Database Managed Instance is a new flavor of Azure SQL Database that is a game changer. It offers near-complete SQL Server compatibility and network isolation to easily lift and shift databases to Azure (you can literally backup an on-premise database and restore it into a Azure SQL Database Managed Instance). Think of it as an enhancement to Azure SQL Database that is built on the same PaaS infrastructure and maintains all it's features (i.e. active geo-replication, high availability, automatic backups, database advisor, threat detection, intelligent insights, vulnerability assessment, etc) but adds support for databases up to 35TB, VNET, SQL Agent, cross-database querying, replication, etc. So, you can migrate your databases from on-prem to Azure with very little migration effort which is a big improvement from the current Singleton or Elastic Pool flavors which can require substantial changes.
Microsoft Data Platform - What's includedJames Serra
The pace of Microsoft product innovation is so fast that even though I spend half my days learning, I struggle to keep up. And as I work with customers I find they are often in the dark about many of the products that we have since they are focused on just keeping what they have running and putting out fires. So, let me cover what products you might have missed in the Microsoft data platform world. Be prepared to discover all the various Microsoft technologies and products for collecting data, transforming it, storing it, and visualizing it. My goal is to help you not only understand each product but understand how they all fit together and there proper use case, allowing you to build the appropriate solution that can incorporate any data in the future no matter the size, frequency, or type. Along the way we will touch on technologies covering NoSQL, Hadoop, and open source.
Learning to present and becoming good at itJames Serra
Have you been thinking about presenting at a user group? Are you being asked to present at your work? Is learning to present one of the keys to advancing your career? Or do you just think it would be fun to present but you are too nervous to try it? Well take the first step to becoming a presenter by attending this session and I will guide you through the process of learning to present and becoming good at it. It’s easier than you think! I am an introvert and was deathly afraid to speak in public. Now I love to present and it’s actually my main function in my job at Microsoft. I’ll share with you journey that lead me to speak at major conferences and the skills I learned along the way to become a good presenter and to get rid of the fear. You can do it!
Choosing technologies for a big data solution in the cloudJames Serra
Has your company been building data warehouses for years using SQL Server? And are you now tasked with creating or moving your data warehouse to the cloud and modernizing it to support “Big Data”? What technologies and tools should use? That is what this presentation will help you answer. First we will cover what questions to ask concerning data (type, size, frequency), reporting, performance needs, on-prem vs cloud, staff technology skills, OSS requirements, cost, and MDM needs. Then we will show you common big data architecture solutions and help you to answer questions such as: Where do I store the data? Should I use a data lake? Do I still need a cube? What about Hadoop/NoSQL? Do I need the power of MPP? Should I build a "logical data warehouse"? What is this lambda architecture? Can I use Hadoop for my DW? Finally, we’ll show some architectures of real-world customer big data solutions. Come to this session to get started down the path to making the proper technology choices in moving to the cloud.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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!
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
2. About Me
Microsoft, Big Data Evangelist
In IT for 30 years, worked on many BI and DW projects
Worked as desktop/web/database developer, DBA, BI and DW architect and developer, MDM
architect, PDW/APS developer
Been perm employee, contractor, consultant, business owner
Presenter at PASS Business Analytics Conference, PASS Summit, Enterprise Data World conference
Certifications: MCSE: Data Platform, Business Intelligence; MS: Architecting Microsoft Azure
Solutions, Design and Implement Big Data Analytics Solutions, Design and Implement Cloud Data
Platform Solutions
Blog at JamesSerra.com
Former SQL Server MVP
Author of book “Reporting with Microsoft SQL Server 2012”
4. Fully
managed
Integrated Best in Class
Algorithms
Deploy in
minutes
No software to install,
no hardware to manage,
and one portal to view
and update.
Simple drag, drop and
connect interface for
Data Science. No need
for programming for
common tasks.
Built-in collection of
best of breed
algorithms. Support for
R and Python for
extensibility.
Operationalize models
with a single click.
Monetize in Machine
Learning Marketplace.
10. This is Karl.
Karl owns a company that
operates vending machines in
Washington state.
His job is to make sure that his 100
vending machines are selling drinks
& obtaining revenue.
Karl wants revenue to always
be high & his business to
be profitable
11. Sadly, vending machine will
occasionally break & may take up
to 7 days to fix, thus hurting sales.
To eliminate this occurrence, Karl must maintain
operations & figure out the best way to utilize
resources in order to optimize revenue.
12. Azure Cloud Services + Machine Learning to the Rescue!
1. Which Machines Have Failed?
2. Which Machines Will Soon Fail?
13. • Damage is reported by customer
or during weekly restocking routes
• Technician must be scheduled
to investigate
• Process take up to 8 days to fix
a broken machine
• Sensor data is used to monitor
cooler condition in real-time
• Broken coolers are identified
at time of failure
• Lost sales remain due to
maintenance lead teams
(parts & repair technicians)
• Azure ML predicts where, when,
& what failures will occur based
on sensor data
• Spare parts & repairs can be
scheduled before machines shut
down leading to no lost sales
CURRENT SCENARIO REAL-TIME SENSORS SENSORS&MACHINELEARNING
Days: Days:Days:
21. • Accessible through a web browser, no software
to install;
• Collaborative work with anyone, anywhere via
Azure workspace;
• Visual composition with end2end support for
data science workflow;
• Best in class ML algorithms; Immutable library of
models, search discover and reuse;
• Extensible, support for R & Python;
• Rapidly try a range of features, ML algorithms
and modeling strategies
22. Cortana Intelligence Suite
Integrated as part of an end-to-end suite
Action
People
Automated
Systems
Apps
Web
Mobile
Bots
Intelligence
Dashboards &
Visualizations
Cortana
Bot
Framework
Cognitive
Services
Power BI
Information
Management
Event Hubs
Data Catalog
Data Factory
Machine Learning
and Analytics
HDInsight
(Hadoop and
Spark)
Stream Analytics
Intelligence
Data Lake
Analytics
Machine
Learning
Big Data Stores
SQL Data
Warehouse
Data Lake Store
Data
Sources
Apps
Sensors
and
devices
Data
23. Event Hub
Stores Streaming
Data
Stream Analytics
processes events as they
arrive in the EventHub
AML Model
Web Service
BES endpoint
Power BI / D3
Dashboard
Data for
Real-time
Processing
Aggregations
External Data Azure Services
Azure SQL
Contains Historical Energy
Consumption Data
Real time
data stats
Azure Data Factory
Pipeline invokes AML
Web Service
RealTimeBatch
Example Architecture
Real Time
Telemetry
Data
Azure Data Factory
Pipeline Moves Data
Batch updates
of predictions
AML Model
Web Service
RRS endpoint
24. Real Time Energy
Consumption Data
(Public Source)
Event Hub
Stores Streaming
Data
Stream Analytics
processes events as they
arrive in the EventHub
AML Model
Web Service
BES endpoint
Power BI / D3
Dashboard
Data for
Real-time
Processing
Data Stream
Job
Hourly Prediction
Updates
External Data Azure Services
Copy to Azure SQL
for batch predictions
Scrape Data
5 mins
Azure WebJob
Runs jobs to scrape
data from public source
Azure SQL
Contains Historical Energy
Consumption Data
Real time
data stats
Azure Data Factory
Pipeline invokes AML
Web Service
RealTimeBatch
25. Q & A ?
James Serra, Big Data Evangelist
Email me at: JamesSerra3@gmail.com
Follow me at: @JamesSerra
Link to me at: www.linkedin.com/in/JamesSerra
Visit my blog at: JamesSerra.com (where this slide deck is posted via the
“Presentations” link on the top menu)
Editor's Notes
Machine learning allows us to build predictive analytics solutions of tomorrow - these solutions allow us to better diagnose and treat patients, correctly recommend interesting books or movies, and even make the self-driving car a reality. Microsoft Azure Machine Learning (Azure ML) is a fully-managed Platform-as-a-Service (PaaS) for building these predictive analytics solutions. It is very easy to build solutions with it, helping to overcome the challenges most businesses have in deploying and using machine learning. In this presentation, we will take a look at how to create ML models with Azure ML Studio and deploy those models to production in minutes.
Fluff, but point is I bring real work experience to the session
My goal is to give you a high level overview of all the technologies so you know where to start Make you a hero
Advanced Analytics, or Business Analytics, refers to future-oriented analysis that can be used to help drive changes and improvements in business practices. It is made up of four phases:
Descriptive Analytics: What is generally referred to as “business intelligence”, this phase is where a lot of digital information is captured. Then this big data is condensed into smaller, more useful nuggets of information, creating an understanding of the correlations between those nuggets to find out why something is happening (“Diagnostic Analytics”). In short, you are providing insight into what has happened to uncover trends and patterns. An example is Netflix using historic sales and customer data to improve their recommendation engine.
Predictive analytics: Utilizes a variety of statistical, modeling, data mining, and machine learning techniques to study recent and historical data, thereby allowing analysts to make predictions, or forecasts, about the future. In short, it helps model and forecast what might happen. For example, taking sales data, social media data, and weather data to forecast the product demand for a certain region and to adjust production. Or you can use predictive analytics to determine outcomes such as whether a customer will “leave or stay” or “buy or not buy.”
Prescriptive analytics: Goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the decision maker the implications of each decision option. Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. The output is a decision using simulation and optimization. In short, it seeks to determine the best solution or preferred course of action among various choices. For example, airlines sift thought millions of flight itineraries to set an optimal price at any given time based on supply and demand. Also, prescriptive analytics in healthcare can be used to guide clinician actions by making treatment recommendations based on models that use relevant historical intervention and outcome data.
Our embrace of open source continues with our GA release by now adding to our full support of R with new first class support for SQLite and Python. Just as you can with R, you can drop your custom Python code directly into ML Studio.
You can then combine your custom work with our world-class algorithms such as learning with counts, which allows you to digest terabytes of data – truly a Big Data solution in the cloud.
And, as I’ve mentioned, no other vendor offers the ability to deploy with one click – you can literally see the button here. It is simply powerful to be able to put your models into production and increase their usability for businesses, the community and the world.
And we’re enabling the community of Azure ML users by offering an in-product Gallery where you can discover solutions built by others, drop those directly into your workspace for experimentation and learning, as well as easily share your own work in the gallery with the same ease of use you experience with web service production. You can try this out by logging on to azure.com/ml and clicking “get started” to try our perpetually free version.
Microsoft’s Big Data vision in the cloud is to enable organizations to solve large, complex problems end-to-end, from storing and managing TBs of data without investing in hardware and software, to seamless integration with the 1 billion users of Excel. As part of this vision, Microsoft offers Azure Machine Learning, designed to democratize the complex task of advanced analytics.
Advanced analytics is using products like Azure Machine Learning to find new and actionable insights that traditional approaches to business intelligence are unlikely to discover. An easy way to think about this is thinking about a dashboard. Today when confined by only BI tools without a connection to machine learning, it is solely the job of the human looking at the spreadsheet to gain insights and react to the data. But a human can only consume so many variables. A computer, on the other hand, can consume a great deal more variables to provide much deeper insight on the data. Humans can then react to the data to make decisions that drive competitive advantage, as well as program the computer further to recognize important patterns in the future. This is why we say beyond business intelligence – machine intelligence.
The accessibility of our solution starts with set up. Previously you needed to provision your workspace on-premises for machine learning, also thinking about server space and a host of other considerations. Today you can get started with just a browser. With only an Azure subscription, you can take advantage of the full functionality of Azure Machine Learning within minutes. Taking a test drive is even easier, click Get Started off azure.com/ml and with simply a Microsoft ID you’re off to the races.
Another limit with other machine learning solutions are siloed environments that only allow for one programming language or make changing from one algorithm to another time consuming and complex. With Azure ML, you can experience the power of choice. That choice expands to language, with both Python and R being first class citizens of Azure ML, or algorithm. You can choose from hundreds of algorithms, including business-tested ones running our Microsoft businesses today. And swapping out algorithms to land on the right one for you is done with a click. Additionally you can drop in custom R and Python code – your “special sauce” – and mix and match that with the other options in the tool.
Most revolutionary of all you can deploy solutions in minutes as a web service, which is simply a url which can connect to any data, anywhere – including on-premises or in another cloud environment. The ability to put a model into production almost immediately, as well as revise it easily, is unique to Microsoft and allows companies to stay on top of the changing business landscape more effectively than is offered by any other provider today.
We even take that a step further, allowing model developers to connect to the world with our Machine Learning Marketplace, where they can publish finished solutions and APIs with their own brand and business model. Developers can also discover machine learning solutions there without any machine learning skills needed – the data science is inside. Check it out at https://datamarket.azure.com/.
Offer structures: A la carte, Data Intensive, Analytics Intensive, Stream Intensive, All-inclusive