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.
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.
Robust MLOps with Open-Source: ModelDB, Docker, Jenkins, and PrometheusManasi Vartak
These are slides from Manasi Vartak's Strata Talk in March 2020 on Robust MLOps with Open-Source.
* Introduction to talk
* What is MLOps?
* Building an MLOps Pipeline
* Real-world Simulations
* Let’s fix the pipeline
* Wrap-up
Introdution to Dataops and AIOps (or MLOps)Adrien Blind
This presentation introduces the audience to the DataOps and AIOps practices. It deals with organizational & tech aspects, and provide hints to start you data journey.
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
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
This presentation will cover Cloud history and Microsoft Azure Data Analytics capabilities. Moreover, it has a real-world example of DW modernization. Finally, we will check the alternative solution on Azure using Snowflake and Matillion ETL.
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
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.
Robust MLOps with Open-Source: ModelDB, Docker, Jenkins, and PrometheusManasi Vartak
These are slides from Manasi Vartak's Strata Talk in March 2020 on Robust MLOps with Open-Source.
* Introduction to talk
* What is MLOps?
* Building an MLOps Pipeline
* Real-world Simulations
* Let’s fix the pipeline
* Wrap-up
Introdution to Dataops and AIOps (or MLOps)Adrien Blind
This presentation introduces the audience to the DataOps and AIOps practices. It deals with organizational & tech aspects, and provide hints to start you data journey.
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
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
This presentation will cover Cloud history and Microsoft Azure Data Analytics capabilities. Moreover, it has a real-world example of DW modernization. Finally, we will check the alternative solution on Azure using Snowflake and Matillion ETL.
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
Putting the Ops in DataOps: Orchestrate the Flow of Data Across Data PipelinesDATAVERSITY
With the aid of any number of data management and processing tools, data flows through multiple on-prem and cloud storage locations before it’s delivered to business users. As a result, IT teams — including IT Ops, DataOps, and DevOps — are often overwhelmed by the complexity of creating a reliable data pipeline that includes the automation and observability they require.
The answer to this widespread problem is a centralized data pipeline orchestration solution.
Join Stonebranch’s Scott Davis, Global Vice President and Ravi Murugesan, Sr. Solution Engineer to learn how DataOps teams orchestrate their end-to-end data pipelines with a platform approach to managing automation.
Key Learnings:
- Discover how to orchestrate data pipelines across a hybrid IT environment (on-prem and cloud)
- Find out how DataOps teams are empowered with event-based triggers for real-time data flow
- See examples of reports, dashboards, and proactive alerts designed to help you reliably keep data flowing through your business — with the observability you require
- Discover how to replace clunky legacy approaches to streaming data in a multi-cloud environment
- See what’s possible with the Stonebranch Universal Automation Center (UAC)
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
AWS delivers an integrated suite of services that provide everything needed to quickly and easily build and manage a data lake for analytics. AWS-powered data lakes can handle the scale, agility, and flexibility required to combine different types of data and analytics approaches to gain deeper insights, in ways that traditional data silos and data warehouses cannot. In this session, we will show you how you can quickly build a data lake on AWS that ingests, catalogs and processes incoming data and makes it ready for analysis. Using a live demo, we demonstrate the capabilities of AWS provided analytical services such as AWS Glue, Amazon Athena and Amazon EMR and how to build a Data Lake on AWS step-by-step.
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.
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
Vertex AI - Unified ML Platform for the entire AI workflow on Google CloudMárton Kodok
Vertex AI is a managed ML platform for practitioners to accelerate experiments and deploy AI models.
Enhanced developer experience
- Build with the groundbreaking ML tools that power Google
- Approachable from the non-ML developer perspective (AutoML, managed models, training)
- Ease the life of a data scientist/ML (has feature store, managed datasets, endpoints, notebooks)
- Infrastructure management overhead have been almost completely eliminated
- Unified UI for the entire ML workflow
- End-to-end integration for data and AI with build pipelines that outperform and solve complex ML tasks
- Explainable AI and TensorBoard to visualize and track ML experiments
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
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/
The data architecture of solutions is frequently not given the attention it deserves or needs. Frequently, too little attention is paid to designing and specifying the data architecture within individual solutions and their constituent components. This is due to the behaviours of both solution architects ad data architects.
Solution architecture tends to concern itself with functional, technology and software components of the solution
Data architecture tends not to get involved with the data aspects of technology solutions, leaving a data architecture gap. Combined with the gap where data architecture tends not to get involved with the data aspects of technology solutions, there is also frequently a solution architecture data gap. Solution architecture also frequently omits the detail of data aspects of solutions leading to a solution data architecture gap. These gaps result in a data blind spot for the organisation.
Data architecture tends to concern itself with post-individual solutions. Data architecture needs to shift left into the domain of solutions and their data and more actively engage with the data dimensions of individual solutions. Data architecture can provide the lead in sealing these data gaps through a shift-left of its scope and activities as well providing standards and common data tooling for solution data architecture
The objective of data design for solutions is the same as that for overall solution design:
• To capture sufficient information to enable the solution design to be implemented
• To unambiguously define the data requirements of the solution and to confirm and agree those requirements with the target solution consumers
• To ensure that the implemented solution meets the requirements of the solution consumers and that no deviations have taken place during the solution implementation journey
Solution data architecture avoids problems with solution operation and use:
• Poor and inconsistent data quality
• Poor performance, throughput, response times and scalability
• Poorly designed data structures can lead to long data update times leading to long response times, affecting solution usability, loss of productivity and transaction abandonment
• Poor reporting and analysis
• Poor data integration
• Poor solution serviceability and maintainability
• Manual workarounds for data integration, data extract for reporting and analysis
Data-design-related solution problems frequently become evident and manifest themselves only after the solution goes live. The benefits of solution data architecture are not always evident initially.
Strategic Approach To Data Migration Project PlanSlideTeam
Presenting this set of slides with name Strategic Approach To Data Migration Project Plan. This is a six stage process. The stages in this process are Plan, Develop, Validate, Migrate Stage, Test. This is a completely editable PowerPoint presentation and is available for immediate download. Download now and impress your audience. https://bit.ly/3CTswep
Platform Strategy to Deliver Digital Experiences on AzureWSO2
This slide deck introduces Choreo, a cloud native internal developer platform by Microsoft independent software vendor (ISV) Partner, WSO2. It enables your developers to create, deploy, and run new digital components like APIs, microservices, and integrations in serverless mode on any Kubernetes cluster with built-in DevSecOps.
Recording: https://wso2.com/choreo/resources/webinar/platform-strategy-to-deliver-digital-experiences-on-azure/
At wetter.com we build analytical B2B data products and heavily use Spark and AWS technologies for data processing and analytics. I explain why we moved from AWS EMR to Databricks and Delta and share our experiences from different angles like architecture, application logic and user experience. We will look how security, cluster configuration, resource consumption and workflow changed by using Databricks clusters as well as how using Delta tables simplified our application logic and data operations.
AWS Control Tower is a new AWS service for cloud administrators to set up and govern their secure, compliant, multi-account environments on AWS.
In this session, University of York will discuss their implementation of AWS Landing Zone. We’ll also explain how AWS Control Tower automates AWS Landing Zone creation with best-practice blueprints.
Learn how Azure DevOps has empowered Horizons LIMS to streamline their collaboration and CI / CD process to accelerate their enterprise digital transformation. You will also hear about the latest Azure DevOps features and how to integrate DevOps with GetHub, Jenkins, and leverage transformation workloads like Kubernetes and Microsoft Common Data Service to deliver products and services faster.
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.
Putting the Ops in DataOps: Orchestrate the Flow of Data Across Data PipelinesDATAVERSITY
With the aid of any number of data management and processing tools, data flows through multiple on-prem and cloud storage locations before it’s delivered to business users. As a result, IT teams — including IT Ops, DataOps, and DevOps — are often overwhelmed by the complexity of creating a reliable data pipeline that includes the automation and observability they require.
The answer to this widespread problem is a centralized data pipeline orchestration solution.
Join Stonebranch’s Scott Davis, Global Vice President and Ravi Murugesan, Sr. Solution Engineer to learn how DataOps teams orchestrate their end-to-end data pipelines with a platform approach to managing automation.
Key Learnings:
- Discover how to orchestrate data pipelines across a hybrid IT environment (on-prem and cloud)
- Find out how DataOps teams are empowered with event-based triggers for real-time data flow
- See examples of reports, dashboards, and proactive alerts designed to help you reliably keep data flowing through your business — with the observability you require
- Discover how to replace clunky legacy approaches to streaming data in a multi-cloud environment
- See what’s possible with the Stonebranch Universal Automation Center (UAC)
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
AWS delivers an integrated suite of services that provide everything needed to quickly and easily build and manage a data lake for analytics. AWS-powered data lakes can handle the scale, agility, and flexibility required to combine different types of data and analytics approaches to gain deeper insights, in ways that traditional data silos and data warehouses cannot. In this session, we will show you how you can quickly build a data lake on AWS that ingests, catalogs and processes incoming data and makes it ready for analysis. Using a live demo, we demonstrate the capabilities of AWS provided analytical services such as AWS Glue, Amazon Athena and Amazon EMR and how to build a Data Lake on AWS step-by-step.
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.
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
Vertex AI - Unified ML Platform for the entire AI workflow on Google CloudMárton Kodok
Vertex AI is a managed ML platform for practitioners to accelerate experiments and deploy AI models.
Enhanced developer experience
- Build with the groundbreaking ML tools that power Google
- Approachable from the non-ML developer perspective (AutoML, managed models, training)
- Ease the life of a data scientist/ML (has feature store, managed datasets, endpoints, notebooks)
- Infrastructure management overhead have been almost completely eliminated
- Unified UI for the entire ML workflow
- End-to-end integration for data and AI with build pipelines that outperform and solve complex ML tasks
- Explainable AI and TensorBoard to visualize and track ML experiments
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
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/
The data architecture of solutions is frequently not given the attention it deserves or needs. Frequently, too little attention is paid to designing and specifying the data architecture within individual solutions and their constituent components. This is due to the behaviours of both solution architects ad data architects.
Solution architecture tends to concern itself with functional, technology and software components of the solution
Data architecture tends not to get involved with the data aspects of technology solutions, leaving a data architecture gap. Combined with the gap where data architecture tends not to get involved with the data aspects of technology solutions, there is also frequently a solution architecture data gap. Solution architecture also frequently omits the detail of data aspects of solutions leading to a solution data architecture gap. These gaps result in a data blind spot for the organisation.
Data architecture tends to concern itself with post-individual solutions. Data architecture needs to shift left into the domain of solutions and their data and more actively engage with the data dimensions of individual solutions. Data architecture can provide the lead in sealing these data gaps through a shift-left of its scope and activities as well providing standards and common data tooling for solution data architecture
The objective of data design for solutions is the same as that for overall solution design:
• To capture sufficient information to enable the solution design to be implemented
• To unambiguously define the data requirements of the solution and to confirm and agree those requirements with the target solution consumers
• To ensure that the implemented solution meets the requirements of the solution consumers and that no deviations have taken place during the solution implementation journey
Solution data architecture avoids problems with solution operation and use:
• Poor and inconsistent data quality
• Poor performance, throughput, response times and scalability
• Poorly designed data structures can lead to long data update times leading to long response times, affecting solution usability, loss of productivity and transaction abandonment
• Poor reporting and analysis
• Poor data integration
• Poor solution serviceability and maintainability
• Manual workarounds for data integration, data extract for reporting and analysis
Data-design-related solution problems frequently become evident and manifest themselves only after the solution goes live. The benefits of solution data architecture are not always evident initially.
Strategic Approach To Data Migration Project PlanSlideTeam
Presenting this set of slides with name Strategic Approach To Data Migration Project Plan. This is a six stage process. The stages in this process are Plan, Develop, Validate, Migrate Stage, Test. This is a completely editable PowerPoint presentation and is available for immediate download. Download now and impress your audience. https://bit.ly/3CTswep
Platform Strategy to Deliver Digital Experiences on AzureWSO2
This slide deck introduces Choreo, a cloud native internal developer platform by Microsoft independent software vendor (ISV) Partner, WSO2. It enables your developers to create, deploy, and run new digital components like APIs, microservices, and integrations in serverless mode on any Kubernetes cluster with built-in DevSecOps.
Recording: https://wso2.com/choreo/resources/webinar/platform-strategy-to-deliver-digital-experiences-on-azure/
At wetter.com we build analytical B2B data products and heavily use Spark and AWS technologies for data processing and analytics. I explain why we moved from AWS EMR to Databricks and Delta and share our experiences from different angles like architecture, application logic and user experience. We will look how security, cluster configuration, resource consumption and workflow changed by using Databricks clusters as well as how using Delta tables simplified our application logic and data operations.
AWS Control Tower is a new AWS service for cloud administrators to set up and govern their secure, compliant, multi-account environments on AWS.
In this session, University of York will discuss their implementation of AWS Landing Zone. We’ll also explain how AWS Control Tower automates AWS Landing Zone creation with best-practice blueprints.
Learn how Azure DevOps has empowered Horizons LIMS to streamline their collaboration and CI / CD process to accelerate their enterprise digital transformation. You will also hear about the latest Azure DevOps features and how to integrate DevOps with GetHub, Jenkins, and leverage transformation workloads like Kubernetes and Microsoft Common Data Service to deliver products and services faster.
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.
Microsoft recently released Azure DevOps, a set of services that help developers and IT ship software faster, and with higher quality. These services cover planning, source code, builds, deployments, and artifacts.
One of the great things about Azure DevOps is that it works great for any app and on any platform regardless of frameworks.
In this session, I will give you a quick overview of what Azure DevOps is and how you can quickly get started and incorporate it into your continuous integration and deployment processes.
Todo o Azure DevOps no terminal
Em plataformas como GNU Linux é muito comum estar no terminal durante o desenvolvimento de software. E usar o Azure DevOps no termnal é completamente possível, vamos criar repositórios, pipelines e até mesmo verificar o trabalho que precisa ser feito, tudo no Bash.
Os slides foram usados na introdução da palestra. Todo
Accelerate Spring Apps to Cloud at Scale—Discussion with Azure Spring Cloud C...VMware Tanzu
SpringOne 2020
Adib Saikali: Principal Platfrom Architect, VMware;
Armando Guzman: Principal Software Engineer, Raley's Family of Stores;
Peter Verstraete: Java Software Crafter, Liantis;
Asir Selvasingh: Principal PM Architect, Java on Azure, Microsoft;
Jonathan Jones: Technical Lead for Group Finance IT, Swiss Re
Learn how enterprise leaders are using Azure Spring Cloud to transform their IT operations and deliver value. This moderated panel discussion will feature customers sharing real-world stories about:
• Running Spring apps in the cloud at enterprise scale
• Embracing hybrid as the new normal
• Transforming their technology stacks
• Implementing zero-trust security and network requirements
• Empowering their developers to rapidly dev and deploy
• Delivering value faster to their end customers
Azure Devops provides a set of cloud DevOps services that allow enterprises to deliver business outcomes, from an idea to production-level code. Azure Devops works for any language, any cloud, and any platform.
DevOps brings together people, processes and technology, automating software delivery to provide continuous value to your users. With Azure DevOps solutions, deliver software faster and more reliably—no matter how big your IT department or what tools you are using
Software release cycles are now measured in days instead of months. Cutting edge companies are continuously delivering high-quality software at a fast pace. In this session, we will cover how you can begin your DevOps journey by sharing best practices and tools used by the engineering teams at Amazon. We will showcase how you can accelerate developer productivity by implementing continuous Integration and delivery workflows. We will also cover an introduction to AWS CodeStar, AWS CodeCommit, AWS CodeBuild, AWS CodePipeline, AWS CodeDeploy, AWS Cloud9, and AWS X-Ray the services inspired by Amazon's internal developer tools and DevOps practice.
Level: 200
Speaker: Nick Brandaleone - Solutions Architect, AWS
DevOps brings together people, processes and technology, automating software delivery to provide continuous value to your users. With Azure DevOps solutions, deliver software faster and more reliably—no matter how big your IT department or what tools you are using
Continues Integration and Continuous Delivery with Azure DevOps - Deploy Anyt...Janusz Nowak
Continues Integration and Continuous Delivery with Azure DevOps - Deploy Anything to Anywhere with Azure DevOps
Janusz Nowak
@jnowwwak
https://www.linkedin.com/in/janono
https://github.com/janusznowak
https://blog.janono.pl
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"Big Data is not the new oil." - Jer Thorp, the co-founder of the Office For Creative Research, a multi-disciplinary research group exploring new modes of engagement with data.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
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Multiply with different modes (map)
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2. Comparing various launch configs for CUDA based vector multiply.
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Sum with different modes (reduce)
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Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
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Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
[AI] ML Operationalization with Microsoft Azure
1. ML Operationalization with Microsoft Azure
Kyle Akepanidtaworn (Krid/Kyle)
Cloud Solution Architect (Advanced Analytics & AI)
Global Digital Transformation Partnerships Team
Email | Linkedin | Quora | Medium | GitHub
2. Learning Objectives
In this workshop, you will learn:
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. You will also learn how to monitor the
model's performance after it is deployed so your startup can be proactive with performance issues.
The target audience for the workshop includes:
• Data Scientists
• App Developers
• AI Engineers
• DevOps Engineers
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
5. {2.Introduction ofMLOps}
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6. Machine Learning Across Microsoft
Microsoft 365
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
7. Machine Learning at Scale
Monthly active Office
365 users, leveraging
Office AI capabilities
180
Million
Questions being
asked on Cortana
18
Billion
Number of signals
analyzed on behalf of
users to identify patterns
of emerging threats daily
6.5
Trillion
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
8. But ML is hard!
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
9. Building
a model
Data ingestion Data analysis
Data
transformation
Data validation Data splitting
Trainer
Model
validation
Training
at scale
LoggingRoll-out Serving Monitoring
Machine Learning Process
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
10. E2E ML lifecycle
Train Model Validate Model Deploy ModelPackage Model Monitor Model
Retrain Model
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
11. MLOps = ML + DEV + OPS
Experiment
Data Acquisition
Business Understanding
Initial Modeling
Develop Operate
Continuous Delivery
Data Feedback Loop
System + Model Monitoring
ML
Modeling + Testing
Continuous Integration
Continuous Deployment
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
12. App developer
using Azure DevOps
Build appCollaborate Test app Release app Monitor app
Model reproducibility Model retrainingModel deploymentModel validation
Data scientist using
Azure Machine Learning
13. Code, dataset, and
environment versioning
Model reproducibility Model retrainingModel deploymentModel validation
Build appCollaborate Test app Release app Monitor app
App developer
using Azure DevOps
Data scientist using
Azure Machine Learning
14. Model reproducibility Model retrainingModel deploymentModel validation
Automated ML
ML Pipelines
Hyperparameter tuning
Train model
Build appCollaborate Test app Release app Monitor app
App developer
using Azure DevOps
Data scientist using
Azure Machine Learning
15. Model validation
& certification
Model reproducibility Model retrainingModel deploymentModel validation
Train model Validate
model
Build appCollaborate Test app Release app Monitor app
App developer
using Azure DevOps
Data scientist using
Azure Machine Learning
16. Model packaging
Simple deployment
Model reproducibility Model retrainingModel deploymentModel validation
Train model Validate
model
Deploy
model
Build appCollaborate Test app Release app Monitor app
App developer
using Azure DevOps
Data scientist using
Azure Machine Learning
17. Model
management
& monitoring
Model performance
analysis
Model reproducibility Model retrainingModel deploymentModel validation
Train model Validate
model
Deploy
model
Monitor
model
Retrain model
Build appCollaborate Test app Release app Monitor app
App developer
using Azure DevOps
Data scientist using
Azure Machine Learning
18. Train model Validate
model
Deploy
model
Monitor
model
Retrain model
Model reproducibility Model retrainingModel deploymentModel validation
Build appCollaborate Test app Release app Monitor app
Azure DevOps integration
App developer
using Azure DevOps
Data scientist using
Azure Machine Learning
19. MLOps Benefits
1. Reproducibility + Auditability
• Code drives generation and
deployments
• Pipelines are reproducible and verifiable
• All artifacts can be tagged and audited
2. Validation
• SWE best practices for quality control
• Offline comparisons of model quality
• Minimize bias and enable explainability
3. Automation + Observability
• Controlled rollout capabilities.
• Live comparison of predicted vs.
expected performance.
• Results fed back to watch for drift and
improve model.
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
20. Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
21. Azure MLOps
Asset management & orchestration
services to help manage the ML lifecycle.
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
22. Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
23. Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
24. {3.Hands-on Workshop}
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26. {4.Before Hands-on Lab}
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
27. Critical Requirements
1. Azure subscription. You will need a valid and active Azure account to complete the quickstarts. If you do
not have one, you can sign up for a free trial.
• The Microsoft Azure subscription must be pay-as-you-go or MSDN.
• Trial subscriptions will not work. You will run into issues with Azure resource quota limits.
• Subscriptions with access limited to a single resource group will not work. You will need the ability to deploy
multiple resource groups.
2. Azure DevOps subscription. You will need a valid and active Azure DevOps account to complete the
quickstarts. If you do not have one, you can sign up for a free account. (Please provision in “East US” to
leverage GPU.)
3. Azure Notebooks. You will need an Azure Notebooks project to import the quickstart notebooks into. See
instructions below on how to prepare your Azure Notebooks environment. (Please provision in “East
US” to leverage GPU.)
4. Azure Machine Learning service workspace. (Please provision in “East US” to leverage GPU.)
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
28. Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
29. Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
31. Azure DevOps
Deliver value to your users faster
using proven agile tools to plan,
track, and discuss work across
your teams.
Build, test, and deploy with CI/CD that
works with any language, platform,
and cloud. Connect to GitHub or any
other Git provider and deploy
continuously.
Get unlimited, cloud-hosted
private Git repos and collaborate
to build better code with pull
requests and advanced file
management.
Test and ship with confidence
using manual and exploratory
testing tools.
Create, host, and share packages with
your team, and add artifacts to your
CI/CD pipelines with a single click.
Azure Boards Azure ReposAzure Pipelines
Azure Test Plans Azure Artifacts
https://azure.com/devops
➔
32. Cloud-hosted pipelines for Linux, Windows and
macOS, with unlimited minutes for open source
Azure Pipelines
Any language, any platform, any cloud
Build, test, and deploy Node.js, Python, Java, PHP, Ruby,
C/C++, .NET, Android, and iOS apps. Run in parallel on
Linux, macOS, and Windows. Deploy to Azure, AWS,
GCP or on-premises
Extensible
Explore and implement a wide range of community-
built build, test, and deployment tasks, along with
hundreds of extensions from Slack to SonarCloud.
Support for YAML, reporting and more
Best-in-class for open source
Ensure fast continuous integration/continuous delivery
(CI/CD) pipelines for every open source project. Get
unlimited build minutes for all open source projects with
up to 10 free parallel jobs across Linux, macOS and
Windows
https://azure.com/pipelines➔
Containers and Kubernetes
Easily build and push images to container registries like
Docker Hub and Azure Container Registry. Deploy
containers to individual hosts or Kubernetes.
33. Track work with Kanban boards, backlogs, team
dashboards, and custom reporting
Azure Boards
https://azure.com/devops➔
Connected from idea to release
Track all your ideas at every development stage and
keep your team aligned with all code changes linked
directly to work items.
Scrum ready
Use built-in scrum boards and planning tools to help
your teams run sprints, stand-ups, and planning
meetings.
Project insights
Gain new insights into the health and status of your
project with powerful analytics tools and dashboard
widgets.
34. Unlimited private Git repo hosting and support for
TFVC that scales from a hobby project to the
world’s largest Git repositories
Azure Repos
https://azure.com/devops➔
Works with your Git client
Securely connect with and push code into your Git
repos from any IDE, editor, or Git client.
Web hooks and API integration
Add validations and extensions from the marketplace
or build your own using web hooks and REST APIs.
Semantic code search
Quickly find what you’re looking for with code-aware
search that understands classes and variables.
35. Get end-to-end traceability. Run tests and log
defects from your browser. Track and assess quality
throughout your testing lifecycle.
Azure Test Plans
Capture rich data
Capture rich scenario data as you execute tests to
make discovered defects actionable. Explore user
stories without test cases or test steps. You can
create test cases directly from your exploratory test
sessions.
Test across web and desktop
Test your application where it lives. Complete
scripted tests across desktop or web scenarios. Test
on-premises application from the cloud and vice-
versa.
Get end-to-end traceability
Leverage the same test tools across your engineers
and user acceptance testing stakeholders. Pay for the
tools only when you need them.
https://azure.com/devops➔
36. Create and share Maven, npm, and NuGet package
feeds from public and private sources – fully
integrated into CI/CD pipelines
Azure Artifacts
Manage all package types
Get universal artifact management for Maven, npm,
and NuGet.
Add packages to any pipeline
Share packages, and use built-in CI/CD, versioning,
and testing.
Share code efficiently
Easily share code across small teams and large
enterprises.
https://azure.com/devops➔
37. Azure DevOps Services Pricing
Free
Unlimited users and build time
• Azure Pipelines: 10 parallel jobs with
unlimited minutes for CI/CD
• Azure Boards: Work item tracking and
Kanban boards
• Azure Repos: Unlimited public Git repos
Free
Start free with up to 5 users
• Azure Pipelines: Run 1 Microsoft-hosted
job for 1,800 minutes per month and 1
self-hosted job for any amount of time
• Azure Boards: Work item tracking and
Kanban boards
• Azure Repos: Unlimited public Git repos
• Azure Artifacts: package management
• Unlimited stakeholders
Starts at $6
per user, per month for Boards & Repos*
Easy pricing that grows with your team
• Azure Pipelines: Run 1 Microsoft-hosted
job for 1,800 minutes per month and 1
self-hosted job for any amount of time
• Azure Boards: Work item tracking and
Kanban boards
• Azure Repos: Unlimited public Git repos
• Azure Artifacts: package management
• Unlimited stakeholders
• Boards & Repos included for Visual
Studio subscribers
https://azure.com/pricing/details/devops/➔
5 Boards & Repos users and 5 Artifacts users free. Pipelines
with unlimited minutes, Test Plans users and additional
Artifacts users also available. Please see the Azure pricing
calculator for details.
*
38. Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
39. Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
40. {5.Hands-on LabBefore Lunch Break}
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
41. Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
42. Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
43. Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
44. Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
45. Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
46. Registering the Model (via Notebook & Portal)
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
47. Setup New Project in Azure DevOps
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
48. Setup New Project in Azure DevOps
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
49. Import Quickstart code from a GitHub Repo
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
50. Import Quickstart code from a GitHub Repo
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
51. Update the build YAML file
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
52. Update the build YAML file
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
53. Create New Service Connection
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
54. Create New Service Connection
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
55. Create New Service Connection
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
56. Setup and Run the Build Pipeline
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
57. Setup and Run the Build Pipeline
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
58. Setup and Run the Build Pipeline
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
59. Setup and Run the Build Pipeline
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
60. Setup and Run the Build Pipeline
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
61. Setup and Run the Build Pipeline
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
63. Setup and Run the Build Pipeline
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
64. Review Build Outputs
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
65. Setup the Release Pipeline
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
66. Setup the Release Pipeline
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
67. Add the Build Artifact
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
68. Add the Build Artifact
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
69. Add Variables to Deploy & Test stage
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
70. Add Variables to Deploy & Test stage
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
71. Add Variables to Deploy & Test stage
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
72. Setup Agent Pool for Deploy & Test Stage
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
73. Setup Agent Pool for Deploy & Test Stage
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
74. Add Use Python Version Task
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
75. Add Use Python Version Task
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
76. Add Install Requirements Task
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
77. Add Install Requirements Task
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
78. Add Install Requirements Task
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
79. Add Install Requirements Task
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
80. Add Install Requirements Task
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
81. Add Deploy & Test Webservice task
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
82. Add Deploy & Test Webservice task
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
83. Add Deploy & Test Webservice task
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
84. Add Deploy & Test Webservice task
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
85. Define Deployment Trigger
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
86. Enable Continuous Deployment Trigger
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
87. Save the Release Pipeline
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
88. Save the Release Pipeline
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
90. Test, Build, Release Pipelines
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
91. Test, Build, Release Pipelines
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
92. Test, Build, Release Pipelines
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
93. Test, Build, Release Pipelines (~1 Hour+)
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
94. Test, Build, Release Pipelines (~1 Hour+)
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team
95. {8.Q&A}
Copyright (c) Microsoft Corporation. All rights reserved. @Korkrid Akepanidtaworn, GSMO Digital Transformation Partnerships Team