Let’s dive into the world of serverless and give you real world examples of how to get started. We will focus on Azure Functions in Java and discuss how to provision, deploy and test them in a productive environment. In my demos we will see the ease of local development leveraging from the great integration in Visual Studio Code. Finally, let’s ship our samples and scale them in Azure. If you are tired of server maintenance and want to achieve more with your java functions , don’t miss this session.
Let’s dive into the world of serverless and give you real world examples of how to get started. We will focus on Azure Functions in Java and discuss how to provision, deploy and test them in a productive environment. In my demos we will see the ease of local development leveraging from the great integration in Visual Studio Code. Finally, let’s ship our samples and scale them in Azure. If you are tired of server maintenance and want to achieve more with your java functions , don’t miss this session.
Prometheus is a popular open source metric monitoring solution and Azure Monitor provides a seamless onboarding experience to collect Prometheus metrics. Learn how to configure scraping of Prometheus metrics with Azure Monitor for containers running in AKS cluster.
CREATING REAL TIME DASHBOARD WITH BLAZOR, AZURE FUNCTION COSMOS DB AN AZURE S...CodeOps Technologies LLP
In this talk people will get to know how we can use change feed feature of Cosmos DB and use azure functions and signal or service to develop a real time dashboard system
Must Know Azure Kubernetes Best Practices And Features For Better Resiliency ...CodeOps Technologies LLP
Running day-1 Ops on your Kubernetes is somewhat easy, but it is quite daunting to manage day two challenges. Learn about AKS best practices for your cloud-native applications so that you can avoid blow up your workloads.
Service Fabric is the foundational technology introduced by Microsoft Azure to empower the large-scale Azure service. In this session, you’ll get an overview of containers like Docker after an overview of Service Fabric, explain the difference between it and Kubernetes as a new way To Orchestrate Microservices. You’ll learn how to develop a Microservices application and how to deploy those services to Service Fabric clusters and the new serverless Service Fabric Mesh service. We’ll dive into the platform and programming model advantages including stateful services and actors for low-latency data processing and more. You will learn: Overview of containers Overview of Service Fabric Difference between Kubernetes and Service Fabric Setup Environment to start developing an application using Microservices with Service Fabric.
Shared as part of Cloud Community Days on 17th June 2020 - ccdays.konfhub.com
Shift Remote AI: Build and deploy PyTorch Models with Azure Machine Learning ...Shift Conference
Take a deep look into Azure Machine Learning, a cloud service that helps you build, train, deploy, and manage models. Walk through the data science process and then have some fun creating a ML recognition model based on the Simpsons cartoon with PyTorch. You'll leave this session with a better grasp of the technological components of Azure Machine Learning services.
The presentation covers overview of Azure App Service and Azure Web Apps. The presentation also covers the different features of Azure Web Apps - like Kudu, Continuous Deployment, Application Insights, Deployment Slots, Auto-Scaling and so on including demos. It will be useful for anyone looking forward to learn about Azure Web Apps or anyone preparing for Azure Certifications (70-532/533).
Applying DevOps to Databricks can be a daunting task. In this talk this will be broken down into bite size chunks. Common DevOps subject areas will be covered, including CI/CD (Continuous Integration/Continuous Deployment), IAC (Infrastructure as Code) and Build Agents.
We will explore how to apply DevOps to Databricks (in Azure), primarily using Azure DevOps tooling. As a lot of Spark/Databricks users are Python users, will will focus on the Databricks Rest API (using Python) to perform our tasks.
When it comes to microservice architecture, sometimes all you wanted is to perform cross cutting concerns ( logging, authentication , caching, CORS, Routing, load balancing , exception handling , tracing, resiliency etc..) and also there might be a scenario where you wanted to perform certain manipulations on your request payload before hitting into your actual handler. And this should not be a repetitive code in each of the services , so all you might need is a single place to orchestrate all these concerns and that is where Middleware comes into the picture. In the demo I will be covering how to orchestrate these cross cutting concerns by using Azure functions as a Serverless model.
Azure Days 2019: Infrastructure as Code auf Azure (Jonas Wanninger & Daniel H...Trivadis
Heutzutage schreibt man nicht nur Applikationen mit Code. Dank der Cloud wird die Konfiguration von Infrastruktur wie virtuellen Maschinen oder Netzwerken in Code definiert und automatisiert ausgeliefert. Man spricht von Infrastructure as Code, kurz: IAC. Für Infrastructure as Code auf Azure gibt es viele tools wie Ansible, Puppet, Chef, etc. Zwei Lösungen stechen durch Ihren unterschiedlichen Ansatz heraus - Die Azure Resource Manager Templates (ARM) als Microsoft-native Lösung, immer auf dem neusten Stand, aber an Azure gebunden. Auf der anderen Seite Terraform von HashiCorp mit einer deskriptiven Sprache als Grundlage, dafür weniger Features im Security-Bereich. Für einen Grosskunden haben wir die beiden Technologien verglichen. Die Resultate zeigen wir in dieser Session mit Livedemos auf.
It is difficult to deploy interloop Kubernetes development in current state. Know these open-source projects that can save us from the burden of various tools and help in deploying microservices on Kubernetes cluster without saving secrets in a file.
Virtual Global Azure 2020 - Azure MonitorPedro Sousa
This presentation was given at Global Azure 2020 Lisbon, about Azure Monitor.
This session focused on:
- steps of the Monitoring Lifecycle;
- Conceptual Architecture of Azure Monitoring;
- Data Collection & Onboarding;
- Metrics & Logs;
- Demos.
Recordings for the event sessions will be available soon.
Building Deploying and Managing Microservices-based Applications with Azure P...CodeOps Technologies LLP
This presentation covers:
* Setup AKS cluster on Azure
* Deploy a sample microservice-based highly available and scalable app to the cluster
* Set up Azure pipeline for CI and CD
* Automate deployment of the application on Git commit to AKS cluster
Presented as part of Cloud Community Days - 19 June - ccdays.konfhub.com
Modern Cloud Fundamentals: Misconceptions and Industry TrendsChristopher Bennage
A discussion of misconceptions, problems, and industry trends that hinder adoption of cloud technology; with an emphasis on scenarios that appear to work but fail at critical moments.
Be sure to read the notes!
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.
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
Prometheus is a popular open source metric monitoring solution and Azure Monitor provides a seamless onboarding experience to collect Prometheus metrics. Learn how to configure scraping of Prometheus metrics with Azure Monitor for containers running in AKS cluster.
CREATING REAL TIME DASHBOARD WITH BLAZOR, AZURE FUNCTION COSMOS DB AN AZURE S...CodeOps Technologies LLP
In this talk people will get to know how we can use change feed feature of Cosmos DB and use azure functions and signal or service to develop a real time dashboard system
Must Know Azure Kubernetes Best Practices And Features For Better Resiliency ...CodeOps Technologies LLP
Running day-1 Ops on your Kubernetes is somewhat easy, but it is quite daunting to manage day two challenges. Learn about AKS best practices for your cloud-native applications so that you can avoid blow up your workloads.
Service Fabric is the foundational technology introduced by Microsoft Azure to empower the large-scale Azure service. In this session, you’ll get an overview of containers like Docker after an overview of Service Fabric, explain the difference between it and Kubernetes as a new way To Orchestrate Microservices. You’ll learn how to develop a Microservices application and how to deploy those services to Service Fabric clusters and the new serverless Service Fabric Mesh service. We’ll dive into the platform and programming model advantages including stateful services and actors for low-latency data processing and more. You will learn: Overview of containers Overview of Service Fabric Difference between Kubernetes and Service Fabric Setup Environment to start developing an application using Microservices with Service Fabric.
Shared as part of Cloud Community Days on 17th June 2020 - ccdays.konfhub.com
Shift Remote AI: Build and deploy PyTorch Models with Azure Machine Learning ...Shift Conference
Take a deep look into Azure Machine Learning, a cloud service that helps you build, train, deploy, and manage models. Walk through the data science process and then have some fun creating a ML recognition model based on the Simpsons cartoon with PyTorch. You'll leave this session with a better grasp of the technological components of Azure Machine Learning services.
The presentation covers overview of Azure App Service and Azure Web Apps. The presentation also covers the different features of Azure Web Apps - like Kudu, Continuous Deployment, Application Insights, Deployment Slots, Auto-Scaling and so on including demos. It will be useful for anyone looking forward to learn about Azure Web Apps or anyone preparing for Azure Certifications (70-532/533).
Applying DevOps to Databricks can be a daunting task. In this talk this will be broken down into bite size chunks. Common DevOps subject areas will be covered, including CI/CD (Continuous Integration/Continuous Deployment), IAC (Infrastructure as Code) and Build Agents.
We will explore how to apply DevOps to Databricks (in Azure), primarily using Azure DevOps tooling. As a lot of Spark/Databricks users are Python users, will will focus on the Databricks Rest API (using Python) to perform our tasks.
When it comes to microservice architecture, sometimes all you wanted is to perform cross cutting concerns ( logging, authentication , caching, CORS, Routing, load balancing , exception handling , tracing, resiliency etc..) and also there might be a scenario where you wanted to perform certain manipulations on your request payload before hitting into your actual handler. And this should not be a repetitive code in each of the services , so all you might need is a single place to orchestrate all these concerns and that is where Middleware comes into the picture. In the demo I will be covering how to orchestrate these cross cutting concerns by using Azure functions as a Serverless model.
Azure Days 2019: Infrastructure as Code auf Azure (Jonas Wanninger & Daniel H...Trivadis
Heutzutage schreibt man nicht nur Applikationen mit Code. Dank der Cloud wird die Konfiguration von Infrastruktur wie virtuellen Maschinen oder Netzwerken in Code definiert und automatisiert ausgeliefert. Man spricht von Infrastructure as Code, kurz: IAC. Für Infrastructure as Code auf Azure gibt es viele tools wie Ansible, Puppet, Chef, etc. Zwei Lösungen stechen durch Ihren unterschiedlichen Ansatz heraus - Die Azure Resource Manager Templates (ARM) als Microsoft-native Lösung, immer auf dem neusten Stand, aber an Azure gebunden. Auf der anderen Seite Terraform von HashiCorp mit einer deskriptiven Sprache als Grundlage, dafür weniger Features im Security-Bereich. Für einen Grosskunden haben wir die beiden Technologien verglichen. Die Resultate zeigen wir in dieser Session mit Livedemos auf.
It is difficult to deploy interloop Kubernetes development in current state. Know these open-source projects that can save us from the burden of various tools and help in deploying microservices on Kubernetes cluster without saving secrets in a file.
Virtual Global Azure 2020 - Azure MonitorPedro Sousa
This presentation was given at Global Azure 2020 Lisbon, about Azure Monitor.
This session focused on:
- steps of the Monitoring Lifecycle;
- Conceptual Architecture of Azure Monitoring;
- Data Collection & Onboarding;
- Metrics & Logs;
- Demos.
Recordings for the event sessions will be available soon.
Building Deploying and Managing Microservices-based Applications with Azure P...CodeOps Technologies LLP
This presentation covers:
* Setup AKS cluster on Azure
* Deploy a sample microservice-based highly available and scalable app to the cluster
* Set up Azure pipeline for CI and CD
* Automate deployment of the application on Git commit to AKS cluster
Presented as part of Cloud Community Days - 19 June - ccdays.konfhub.com
Modern Cloud Fundamentals: Misconceptions and Industry TrendsChristopher Bennage
A discussion of misconceptions, problems, and industry trends that hinder adoption of cloud technology; with an emphasis on scenarios that appear to work but fail at critical moments.
Be sure to read the notes!
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.
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
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.
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...Databricks
Getting machine learning models to production is notoriously difficult: it involves multiple teams (data scientists, data and machine learning engineers, operations, …), who often does not speak to each other very well; the model can be trained in one environment but then productionalized in completely different environment; it is not just about the code, but also about the data (features) and the model itself… At DataSentics, as a machine learning and cloud engineering studio, we see this struggle firsthand – on our internal projects and client’s projects as well.
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.
Deploying ML models in production, with or without CI/CD, is significantly more complicated than deploying traditional applications. That is mainly because ML models do not just consist of the code used for their training, but they also depend on the data they are trained on and on the supporting code. Monitoring ML models also adds additional complexity beyond what is usually done for traditional applications. This talk will cover these problems and best practices for solving them, with special focus on how it's done on the Databricks platform.
MLops on Vertex AI Presentation (AI/ML).pptxKnoldus Inc.
During this session, our focus will be on Google's Vertex AI suite, a comprehensive tool designed to facilitate MLOps within our machine learning workflow. Exploring its capabilities, we aim to understand how Vertex AI enhances the efficiency and management of our machine-learning operations.
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI.
This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
This presentation is the fourth of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
A survey on Machine Learning In Production (July 2018)Arnab Biswas
What does Machine Learning In Production mean? What are the challenges? How organizations like Uber, Amazon, Google have built their Machine Learning Pipeline? A survey of the Machine Learning In Production Landscape as of July 2018
ML.NET Model Lifecycle with Azure DevOps - Devops heroes 2019 Marco Zamana
Are you a developer? Are you a Architect? You must have the focus about on the application lifecycle! Building, maintaining, and continuously updating the end-user business application. No news here, but if we add also ML, for example a ML.NET model what we must to do? We need a update and extend a new type of lifecycle: Machine Learning Model Lifecycle.
Digital 1nn0vation saturday pn 2019 - ML.NETMarco Zamana
Alla scoperta del framework ML.NET e dei suoi concetti importanti. Scopriamo la sua malleabilità e Il come è integrabile all'interno di una applicazione .NET già esistente.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
Strategies for Successful Data Migration Tools.pptxvarshanayak241
Data migration is a complex but essential task for organizations aiming to modernize their IT infrastructure and leverage new technologies. By understanding common challenges and implementing these strategies, businesses can achieve a successful migration with minimal disruption. Data Migration Tool like Ask On Data play a pivotal role in this journey, offering features that streamline the process, ensure data integrity, and maintain security. With the right approach and tools, organizations can turn the challenge of data migration into an opportunity for growth and innovation.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
Multiple Your Crypto Portfolio with the Innovative Features of Advanced Crypt...Hivelance Technology
Cryptocurrency trading bots are computer programs designed to automate buying, selling, and managing cryptocurrency transactions. These bots utilize advanced algorithms and machine learning techniques to analyze market data, identify trading opportunities, and execute trades on behalf of their users. By automating the decision-making process, crypto trading bots can react to market changes faster than human traders
Hivelance, a leading provider of cryptocurrency trading bot development services, stands out as the premier choice for crypto traders and developers. Hivelance boasts a team of seasoned cryptocurrency experts and software engineers who deeply understand the crypto market and the latest trends in automated trading, Hivelance leverages the latest technologies and tools in the industry, including advanced AI and machine learning algorithms, to create highly efficient and adaptable crypto trading bots
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
Designing for Privacy in Amazon Web ServicesKrzysztofKkol1
Data privacy is one of the most critical issues that businesses face. This presentation shares insights on the principles and best practices for ensuring the resilience and security of your workload.
Drawing on a real-life project from the HR industry, the various challenges will be demonstrated: data protection, self-healing, business continuity, security, and transparency of data processing. This systematized approach allowed to create a secure AWS cloud infrastructure that not only met strict compliance rules but also exceeded the client's expectations.
5. #azuresatpn
DevOps Workflow with ML Model lifecycle
• Application LifeCycle evolve in a extended Machine Learning Model
Lifecycle.
• ML model generation
• Training model
• Testing
• Evaluation
• Automatic Deployment
7. #azuresatpn
Evolve the pipeline
•
We need to add the following steps:
• Build/compile the ML model trainer app (Usually a console app)
• Run the process (console app) to train the ML.NET model and
generate the serialized model (.zip file).
• Run model’s tests (model quality validation)
• Deployment the model file into the actual end-user application code
(project structure)
8. #azuresatpn
Evolve the pipeline
•
And after we can return to our typical tasks:
• Build/compile the end-user app (such as an ASP.NET Core web app or
WebAPI service)
• Run app’s unit tests and integration tests
• Generate and publish the final pipeline artifact in Azure DevOps (or if
using containers, generate a Docker image and publish it into a
Docker Registry)
11. #azuresatpn
Improvements => the direction
1. Versioning datasets
2. Databases as training data
3. DevOps workflow Scenarios
4. ML Model Versioning
5. Integration with Azure ML and MLFlow