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ADM201 - Leadership session: Digital marketing and ad technology Remember when having scaled infrastructure was a huge differentiator in marketing and ad tech? Not anymore. In this session, meant for executive leaders, you get to see the AWS vision for how companies can stand out in the crowded, massively scaled advertising and marketing ecosystem. Industry leaders will share stories about how they used AWS to enable breakthroughs in customer data collection, identity resolution, audience targeting, media buying, personalization and measurement. Then we turn to the future, and share our playbook for artificial intelligence and real-time solutions to transform big data into marketing outcomes. Session
AIM301-R - [REPEAT] Creating high-quality training datasets with data labeling Amazon SageMaker Ground Truth makes it easy to quickly label high-quality, accurate training datasets. In this workshop, we set up labeling jobs for text and images to help you understand how to make the most of Amazon SageMaker Ground Truth. You learn how to explore and prepare the dataset and label it with object bounding boxes. Then, we use Amazon SageMaker to train a Single Shot MultiBox Detector (SSD) object-detection model based on the labeled dataset, use hyperparameter optimization to find the best model for deployment, and deploy the model to an endpoint for use in an application. Workshop
AIM301-R1 - [REPEAT 1] Creating high-quality training datasets with data labeling Amazon SageMaker Ground Truth makes it easy to quickly label high-quality, accurate training datasets. In this workshop, we set up labeling jobs for text and images to help you understand how to make the most of Amazon SageMaker Ground Truth. You learn how to explore and prepare the dataset and label it with object bounding boxes. Then, we use Amazon SageMaker to train a Single Shot MultiBox Detector (SSD) object-detection model based on the labeled dataset, use hyperparameter optimization to find the best model for deployment, and deploy the model to an endpoint for use in an application. Workshop
AIM302-R - [REPEAT] Create a Q&A bot with Amazon Lex and Amazon Alexa A recent poll showed that 44 percent of customers would rather talk to a chatbot than to a human for customer support. In this workshop, we show you how to deploy a question-and-answer bot using two open-source projects: QnABot and Lex-Web-UI. You get started quickly using Amazon Lex, Amazon Alexa, and Amazon Elasticsearch Service (Amazon ES) to provide a conversational chatbot interface. You enhance this solution using AWS Lambda and integrate it with Amazon Connect. Workshop
AIM302-R1 - [REPEAT 1] Create a Q&A bot with Amazon Lex and Amazon Alexa A recent poll showed that 44 percent of customers would rather talk to a chatbot than to a human for customer support. In this workshop, we show you how to deploy a question-and-answer bot using two open-source projects: QnABot and Lex-Web-UI. You get started quickly using Amazon Lex, Amazon Alexa, and Amazon Elasticsearch Service (Amazon ES) to provide a conversational chatbot interface. You enhance this solution using AWS Lambda and integrate it with Amazon Connect. Workshop
AIM303-R - [REPEAT] Stop guessing: Use AI to understand customer conversations You don't need to be a data scientist to build an AI application. In this workshop, we show you how to use AWS AI services to build a serverless application that can help you understand your customers. Analyze call-center recordings with the help of automatic speech recognition (ASR), translation, and natural language processing (NLP). Get hands-on by producing your own call recordings using Amazon Connect. In the last step, you set up a processing pipeline to automate transcription and NLP analysis, and run analytics and visualizations on the results. Workshop
AIM303-R1 - [REPEAT 1] Stop guessing: Use AI to understand customer conversations You don't need to be a data scientist to build an AI application. In this workshop, we show you how to use AWS AI services to build a serverless application that can help you understand your customers. Analyze call-center recordings with the help of automatic speech recognition (ASR), translation, and natural language processing (NLP). Get hands-on by producing your own call recordings using Amazon Connect. In the last step, you set up a processing pipeline to automate transcription and NLP analysis, and run analytics and visualizations on the results. Workshop
AIM304-R - [REPEAT] Build a content-recommendation engine with Amazon Personalize Machine learning is being used increasingly to improve customer engagement by powering personalized product and content recommendations. Amazon Personalize lets you easily build sophisticated personalization capabilities into your applications, using machine learning technology perfected from years of use on Amazon.com. In this workshop, you build your own recommendation engine by providing training data, building a model based on the algorithm of your choice, testing the model by deploying your Amazon Personalize campaign, and integrating it into your own application. Workshop
AIM304-R1 - [REPEAT 1] Build a content-recommendation engine with Amazon Personalize Machine learning is being used increasingly to improve customer engagement by powering personalized product and content recommendations. Amazon Personalize lets you easily build sophisticated personalization capabilities into your applications, using machine learning technology perfected from years of use on Amazon.com. In this workshop, you build your own recommendation engine by providing training data, building a model based on the algorithm of your choice, testing the model by deploying your Amazon Personalize campaign, and integrating it into your own application. Workshop
AIM305-R - [REPEAT] Automate content moderation and compliance with AI Brand safety is a major concern as advertising becomes more automated, issues with ad adjacency arise, contracts with brands or celebrities run out, and user-generated content is more prevalent. In this workshop, you learn how to use Amazon Rekognition, Amazon Textract, and Amazon Comprehend to detect inappropriate content for moderation, or improper use of content like logos or celebrity faces for compliance. You leave with a scalable architecture that will save days of manual review in media moderation and compliance workflows. Workshop
AIM305-R1 - [REPEAT 1] Automate content moderation and compliance with AI Brand safety is a major concern as advertising becomes more automated, issues with ad adjacency arise, contracts with brands or celebrities run out, and user-generated content is more prevalent. In this workshop, you learn how to use Amazon Rekognition, Amazon Textract, and Amazon Comprehend to detect inappropriate content for moderation, or improper use of content like logos or celebrity faces for compliance. You leave with a scalable architecture that will save days of manual review in media moderation and compliance workflows. Workshop
AIM306 - Deploying machine learning models in production Amazon SageMaker is a modular service that makes it easy to build, train, and deploy machine learning models. In this session, we dive deep into how to deploy machine learning models in the cloud and on edge devices. Amazon SageMaker lets you deploy your trained model in production with a single click so that you can start generating predictions. We also explain how to reduce inference costs by up to 75 percent using Amazon Elastic Inference, and how to train models once and run them anywhere using Amazon SageMaker Neo. Come away understanding all of your options for model deployment. Session
AIM307 - Amazon SageMaker deep dive: A modular solution for machine learning Amazon SageMaker is a fully managed service that offers developers and data scientists the flexibility to build, train, and deploy machine learning models through modular capabilities. In this session, we dive deep into the technical details of each module so you understand how to label and prepare your data, choose an algorithm, train and optimize the model, and make predictions. We also discuss practical deployments of Amazon SageMaker through real-world customer examples. Session
AIM308 - Build accurate training datasets with Amazon SageMaker Ground Truth Successful machine learning models are built on high-quality training datasets. Typically, the task of labeling is distributed across a large number of humans, adding significant overhead and cost. In this session, learn how Amazon SageMaker Ground Truth reduces cost and complexity using a machine learning technique called active learning to label datasets. Active learning reduces the time and manual effort required to do data labeling, by continuously training machine learning algorithms based on labels from humans. Session
AIM401-R - [REPEAT] Deep learning with TensorFlow The TensorFlow deep-learning framework has broad support in areas such as computer vision, natural language understanding, and speech translation. You can get started with a fully managed TensorFlow experience using Amazon SageMaker, a service designed to build, train, and deploy machine learning models at scale. In this workshop, we build, train, and deploy a computer-vision model using key TensorFlow features, including distributed training with Horovod, training on pipe mode datasets, and monitoring with TensorBoard. Workshop
AIM401-R1 - [REPEAT 1] Deep learning with TensorFlow The TensorFlow deep-learning framework has broad support in areas such as computer vision, natural language understanding, and speech translation. You can get started with a fully managed TensorFlow experience using Amazon SageMaker, a service designed to build, train, and deploy machine learning models at scale. In this workshop, we build, train, and deploy a computer-vision model using key TensorFlow features, including distributed training with Horovod, training on pipe mode datasets, and monitoring with TensorBoard. Workshop
AIM402-R - [REPEAT] Deep learning with PyTorch PyTorch is a deep-learning framework that is becoming popular, especially for rapid prototyping of new models. You can get started easily with PyTorch using Amazon SageMaker, a fully managed service, to build, train, and deploy machine learning models at scale. In this workshop, we build a natural-language-processing model to analyze text. Workshop
AIM402-R1 - [REPEAT 1] Deep learning with PyTorch PyTorch is a deep-learning framework that is becoming popular, especially for rapid prototyping of new models. You can get started easily with PyTorch using Amazon SageMaker, a fully managed service, to build, train, and deploy machine learning models at scale. In this workshop, we build a natural-language-processing model to analyze text. Workshop
AIM403-R - [REPEAT] Deep learning with Apache MXNet In this workshop, learn how to get started with the Apache MXNet deep learning framework using Amazon SageMaker, a fully managed service, to build, train, and deploy machine learning models at scale. Learn how to build a computer-vision model using MXNet to extract insights from an image dataset. Once the model is built, learn how to quickly train it to get the best possible results and then easily deploy it to production using Amazon SageMaker. Workshop
AIM403-R1 - [REPEAT 1] Deep learning with Apache MXNet In this workshop, learn how to get started with the Apache MXNet deep learning framework using Amazon SageMaker, a fully managed service, to build, train, and deploy machine learning models at scale. Learn how to build a computer-vision model using MXNet to extract insights from an image dataset. Once the model is built, learn how to quickly train it to get the best possible results and then easily deploy it to production using Amazon SageMaker. Workshop
AIM404-R - [REPEAT] Reinforcement learning with Amazon SageMaker Reinforcement learning (RL) is used to build sophisticated models without the need for pre-labeled training data. In this workshop, you learn how to use the built-in, fully managed RL algorithms that are part of Amazon SageMaker RL. Amazon SageMaker supports RL in multiple frameworks, including TensorFlow and MXNet, as well as in custom-developed frameworks designed from the ground up for reinforcement learning, such as Intel Coach and Ray RLlib. Also, use Amazon SageMaker RL to train in using virtual 3D environments that are built in Amazon Sumerian and AWS RoboMaker. Workshop
AIM404-R1 - [REPEAT 1] Reinforcement learning with Amazon SageMaker Reinforcement learning (RL) is used to build sophisticated models without the need for pre-labeled training data. In this workshop, you learn how to use the built-in, fully managed RL algorithms that are part of Amazon SageMaker RL. Amazon SageMaker supports RL in multiple frameworks, including TensorFlow and MXNet, as well as in custom-developed frameworks designed from the ground up for reinforcement learning, such as Intel Coach and Ray RLlib. Also, use Amazon SageMaker RL to train in using virtual 3D environments that are built in Amazon Sumerian and AWS RoboMaker. Workshop
AIM405-R - [REPEAT] Start using computer vision with AWS DeepLens If you're new to deep learning, this workshop is for you. Learn how to build and deploy computer-vision models using the AWS DeepLens deep-learning-enabled video camera. Also learn how to build a machine learning application and a model from scratch using Amazon SageMaker. Finally, learn to extend that model to Amazon SageMaker to build an end-to-end AI application. Workshop
AIM405-R1 - [REPEAT 1] Start using computer vision with AWS DeepLens If you're new to deep learning, this workshop is for you. Learn how to build and deploy computer-vision models using the AWS DeepLens deep-learning-enabled video camera. Also learn how to build a machine learning application and a model from scratch using Amazon SageMaker. Finally, learn to extend that model to Amazon SageMaker to build an end-to-end AI application. Workshop
API201 - Accelerating app migration using Amazon MQ A managed message broker like Amazon MQ is essential to connect applications with messaging. In this workshop, learn how to set up an Amazon MQ broker and use the supporting protocols. We dive deep into the security and monitoring features of Amazon MQ and show you how Amazon MQ works across multiple Availability Zones to provide high availability to your systems. You'll leave with a deeper understanding of how to use Amazon MQ to migrate your enterprise applications to the cloud. Workshop Otavio Ferreira Assaji Aluwahre
API301-R - [REPEAT] Securing data in serverless applications and services In this chalk talk, we walk you through the process of designing a serverless application that secures customer data sent to the cloud. The design uses features recently introduced by Amazon Simple Notification Service (Amazon SNS) and Amazon Simple Queue Service (Amazon SQS), including AWS Key Management Service (AWS KMS) keys for encrypting messages at rest and Amazon Virtual Private Cloud (Amazon VPC) endpoints powered by AWS PrivateLink for sending messages without traversing the public internet. These techniques are security best practices for systems that deal with private data, such as e-commerce orders, candidate resumes, or employee information. Chalk Talk Otavio Ferreira
API301-R1 - [REPEAT 1] Securing data in serverless applications and services In this chalk talk, we walk you through the process of designing a serverless application that secures customer data sent to the cloud. The design uses features recently introduced by Amazon Simple Notification Service (Amazon SNS) and Amazon Simple Queue Service (Amazon SQS), including AWS Key Management Service (AWS KMS) keys for encrypting messages at rest and Amazon Virtual Private Cloud (Amazon VPC) endpoints powered by AWS PrivateLink for sending messages without traversing the public internet. These techniques are security best practices for systems that deal with private data, such as e-commerce orders, candidate resumes, or employee information. Chalk Talk Otavio Ferreira
API301-R2 - [REPEAT 2] Securing data in serverless applications and services In this chalk talk, we walk you through the process of designing a serverless application that secures customer data sent to the cloud. The design uses features recently introduced by Amazon Simple Notification Service (Amazon SNS) and Amazon Simple Queue Service (Amazon SQS), including AWS Key Management Service (AWS KMS) keys for encrypting messages at rest and Amazon Virtual Private Cloud (Amazon VPC) endpoints powered by AWS PrivateLink for sending messages without traversing the public internet. These techniques are security best practices for systems that deal with private data, such as e-commerce orders, candidate resumes, or employee information. Chalk Talk Assaji Aluwahre
API304 - Scalable serverless architectures using event-driven design Serverless architectures free you to focus on solving business problems without the burden of managing infrastructure on AWS. However, building serverless applications requires a change in architectural thinking. In this session, learn how to use powerful event-driven design patterns to architect highly scalable solutions that are enterprise-grade, robust, and cost-effective. We showcase how to use AWS Lambda combined with messaging services such as Amazon Simple Queue Service (Amazon SQS) and Amazon Simple Notification Service (Amazon SNS), to improve time to market while delivering great-quality service. Session Luay Kawasme Fernando Dingler Assaji Aluwahre
API305-R - [REPEAT] Building serverless machine-learning workflows Modern machine-learning workflows leverage AWS services such as Amazon Transcribe and Amazon Comprehend to extract, validate, mutate, and enrich your data. Some drive transactional systems that use ML to generate metadata, others derive insights by visualizing customer-interaction sentiment. All share a common challenge: orchestrating a combination of sequential and parallel steps fulfilled by independent microservices. Join us as we examine how workflows can be used to manage that orchestration in a way that's scalable, reliable, and easy to maintain and run. We contrast two approaches for creating such workflows: a traditional monolithic approach and a serverless approach utilizing AWS Step Functions. Chalk Talk Luay Kawasme
API305-R1 - [REPEAT 1] Building serverless machine-learning workflows Modern machine-learning workflows leverage AWS services such as Amazon Transcribe and Amazon Comprehend to extract, validate, mutate, and enrich your data. Some drive transactional systems that use ML to generate metadata, others derive insights by visualizing customer-interaction sentiment. All share a common challenge: orchestrating a combination of sequential and parallel steps fulfilled by independent microservices. Join us as we examine how workflows can be used to manage that orchestration in a way that's scalable, reliable, and easy to maintain and run. We contrast two approaches for creating such workflows: a traditional monolithic approach and a serverless approach utilizing AWS Step Functions. Chalk Talk
API306-R - [Repeat] Building event-driven architectures Many customers choose to build event-driven application architectures, in which subscriber or target services automatically perform in response to events triggered by publisher or source services. This pattern can help development teams operate more independently so they can release new features faster and make their applications more scalable. In this session, we cover the basics of event-driven design, using examples involving Amazon Simple Notification Service (Amazon SNS), Amazon Simple Queue Service (Amazon SQS), AWS Lambda, Amazon EventBridge, and more. You also learn how to choose the right AWS service for the job, and how to optimize for cost and performance. Workshop
API306-R1 - [Repeat 1] Building event-driven architectures Many customers choose to build event-driven application architectures, in which subscriber or target services automatically perform in response to events triggered by publisher or source services. This pattern can help development teams operate more independently so they can release new features faster and make their applications more scalable. In this session, we cover the basics of event-driven design, using examples involving Amazon Simple Notification Service (Amazon SNS), Amazon Simple Queue Service (Amazon SQS), AWS Lambda, Amazon EventBridge, and more. You also learn how to choose the right AWS service for the job, and how to optimize for cost and performance. Workshop
ARC201 - Using containers and serverless to deploy microservices Microservices are a great way to segment your application into well-defined, self-contained units of functionality. Come join us in this chalk talk as we discuss two common architectures for deploying microservices: containers and serverless. Chalk Talk
ARC202-R - [REPEAT] Architecting for the cloud Bring your ideas, war stories, and "aha moments" to this interactive session with an AWS solutions architect, where we discuss cloud architecture best practices. We highlight specific discoveries and insights from AWS customers, as the cloud has redefined how they think about scalability, designing for failure, constrained thinking, elasticity, parallel processing, loose coupling, and more. Come with your own story or an interest in learning how AWS forever changed the way your colleagues think about the IT world. Chalk Talk
ARC202-R1 - [REPEAT 1] Architecting for the cloud Bring your ideas, war stories, and "aha moments" to this interactive session with an AWS solutions architect, where we discuss cloud architecture best practices. We highlight specific discoveries and insights from AWS customers, as the cloud has redefined how they think about scalability, designing for failure, constrained thinking, elasticity, parallel processing, loose coupling, and more. Come with your own story or an interest in learning how AWS forever changed the way your colleagues think about the IT world. Chalk Talk
ARC202-R2 - [REPEAT 2] Architecting for the cloud Bring your ideas, war stories, and "aha moments" to this interactive session with an AWS solutions architect, where we discuss cloud architecture best practices. We highlight specific discoveries and insights from AWS customers, as the cloud has redefined how they think about scalability, designing for failure, constrained thinking, elasticity, parallel processing, loose coupling, and more. Come with your own story or an interest in learning how AWS forever changed the way your colleagues think about the IT world. Chalk Talk
ARC203 - Trends in digital transformation As industries digitally transform their existing business models to fend off competitors or disrupt new markets, they find their IT to be a limiting factor. In this session, we cover the trends of disruptions and opportunities of digital transformation, and the evolution of IT monoliths to microservices and now cloud-native services. We also explore dependency management, or “lock-in,” through a “choosing, using, and losing” mental model. Finally, we discuss chaos architecture as an evolving method for exposing weaknesses before they become real problems. Session
ARC301-R - [REPEAT] Architecture patterns: Serverless stream processing at scale Streaming application architectures are commonly used to solve real-time analytics requirements. Serverless architectures are a great fit for stream-processing applications because they enable you to lower operational costs and pay per execution, and they can seamlessly scale as your stream data rates vary. In this workshop, learn how to create serverless stream-processing architectures that can seamlessly scale as your needs grow. Get hands-on experience using Amazon Kinesis Data Streams, Amazon Kinesis Data Firehose, Amazon Kinesis Data Analytics, and AWS Lambda to create real-time analytics pipelines. Learn how to ingest streaming data at scale and process it to generate real-time insights. Builders Session
ARC301-R1 - [REPEAT 1] Architecture patterns: Serverless stream processing at scale Streaming application architectures are commonly used to solve real-time analytics requirements. Serverless architectures are a great fit for stream-processing applications because they enable you to lower operational costs and pay per execution, and they can seamlessly scale as your stream data rates vary. In this workshop, learn how to create serverless stream-processing architectures that can seamlessly scale as your needs grow. Get hands-on experience using Amazon Kinesis Data Streams, Amazon Kinesis Data Firehose, Amazon Kinesis Data Analytics, and AWS Lambda to create real-time analytics pipelines. Learn how to ingest streaming data at scale and process it to generate real-time insights. Builders Session
ARC302-R - [REPEAT] AI/ML-powered microservices Companies across industries want to use machine learning models in real time. To do this, customers need to use machine learning models in conjunction with their microservices architectures where high throughput and low latency are paramount. In this session, we cover how Amazon SageMaker enables low-latency real-time inferencing and review considerations needed to achieve satisfactory performance. We discuss how Amazon SageMaker enables large-scale batch inferencing. We then detail how to make these batch inferences available in low-latency environments. We conclude with practical tips and tricks and provide demos that show these techniques in action. Chalk Talk
ARC302-R1 - [REPEAT 1] AI/ML-powered microservices Companies across industries want to use machine learning models in real time. To do this, customers need to use machine learning models in conjunction with their microservices architectures where high throughput and low latency are paramount. In this session, we cover how Amazon SageMaker enables low-latency real-time inferencing and review considerations needed to achieve satisfactory performance. We discuss how Amazon SageMaker enables large-scale batch inferencing. We then detail how to make these batch inferences available in low-latency environments. We conclude with practical tips and tricks and provide demos that show these techniques in action. Chalk Talk
ARC303-R - [REPEAT] Failing successfully: The AWS approach to resilient design AWS global infrastructure provides the tools customers need to design resilient and reliable services. In this session, we explore how to get the most out of these tools. We discuss achieving continued stability and availability in the face of impaired dependencies. We also cover AWS tools and best practices you can use to design applications and services that avoid overload. Chalk Talk
ARC303-R1 - [REPEAT 1] Failing successfully: The AWS approach to resilient design AWS global infrastructure provides the tools customers need to design resilient and reliable services. In this session, we explore how to get the most out of these tools. We discuss achieving continued stability and availability in the face of impaired dependencies. We also cover AWS tools and best practices you can use to design applications and services that avoid overload. Chalk Talk
ARC303-R2 - [REPEAT 2] Failing successfully: The AWS approach to resilient design AWS global infrastructure provides the tools customers need to design resilient and reliable services. In this session, we explore how to get the most out of these tools. We discuss achieving continued stability and availability in the face of impaired dependencies. We also cover AWS tools and best practices you can use to design applications and services that avoid overload. Chalk Talk
ARC304-R - [REPEAT] From one to many: Diving deeper into evolving VPC design Most organizations run their workloads inside Amazon VPC. This software-defined network structure provides the security boundaries that organizations and their customers require. For most organizations, the natural evolution involves migrating from a single VPC to multiple VPCs in either the same AWS Region or across many regions. The question of how to enforce security policies while simplifying the flow of traffic between multiple VPCs, data centers, and remote offices while adhering to AWS best practices becomes an intricate one to answer. In this chalk talk, we provide solutions to these scenarios and more. Chalk Talk
ARC304-R1 - [REPEAT 1] From one to many: Diving deeper into evolving VPC design Most organizations run their workloads inside Amazon VPC. This software-defined network structure provides the security boundaries that organizations and their customers require. For most organizations, the natural evolution involves migrating from a single VPC to multiple VPCs in either the same AWS Region or across many regions. The question of how to enforce security policies while simplifying the flow of traffic between multiple VPCs, data centers, and remote offices while adhering to AWS best practices becomes an intricate one to answer. In this chalk talk, we provide solutions to these scenarios and more. Chalk Talk
ARC304-R2 - [REPEAT 2] From one to many: Diving deeper into evolving VPC design Most organizations run their workloads inside Amazon VPC. This software-defined network structure provides the security boundaries that organizations and their customers require. For most organizations, the natural evolution involves migrating from a single VPC to multiple VPCs in either the same AWS Region or across many regions. The question of how to enforce security policies while simplifying the flow of traffic between multiple VPCs, data centers, and remote offices while adhering to AWS best practices becomes an intricate one to answer. In this chalk talk, we provide solutions to these scenarios and more. Chalk Talk
ARC305-R - [REPEAT] Migrating single-tenant applications to multi-tenant SaaS The appeal of SaaS has many ISVs interested in the value of delivering their solutions in a SaaS model. Moving a single-tenant application to a multi-tenant environment can be daunting. In this session, we look at many obstacles that ISVs face as they consider the move to a SaaS model. We explore a range of patterns from lift-and-shift to an incremental cutover to multi-tenant-aware microservices, data, and infrastructure. We highlight the challenges and technical considerations, including onboarding, identity, billing, metering, and analytics, that shape your solution and allow you to better align your transformed solution with SaaS best practices. Chalk Talk
ARC305-R1 - [REPEAT 1] Migrating single-tenant applications to multi-tenant SaaS The appeal of SaaS has many ISVs interested in the value of delivering their solutions in a SaaS model. Moving a single-tenant application to a multi-tenant environment can be daunting. In this session, we look at many obstacles that ISVs face as they consider the move to a SaaS model. We explore a range of patterns from lift-and-shift to an incremental cutover to multi-tenant-aware microservices, data, and infrastructure. We highlight the challenges and technical considerations, including onboarding, identity, billing, metering, analytics, that shape your solution and allow you to better align your transformed solution with SaaS best practices. Chalk Talk
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