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Summarized: Container solutions for machine learning can be beneficial for: Machine learning requires a lot of calculations. The AWS Architecture Center provides reference architecture diagrams, vetted architecture solutions, Well-Architected best practices, patterns, icons, and more. type of algorithm, easy of use), Hosting (e.g. The crucial factor is most of the time cost and the number of resources needed. a large amount of Java applications running and all your processes and developers are Java minded, you should take this fact into account when developing and deploying your machine learning application. ... (HPE) and NVIDIA on a new Accelerated AI Reference Architecture… Machine learning infrastructure hosting that works now for your use cases is no guarantee for the future. Source: Tractica But adding ML acceleration and support to potentially billions of devices will take more customizability, lower costs and specifications tailored more specifically to the needs of ML apps on resource-constrained devices—meaning the whole market will need much better processors if it’s going to succeed. Blob containers are used to store the logs from the scoring service. In this section some general principles for machine learning applications. This solution demonstrates an end-to-end automation of various stages of an AI project using tools that are already familiar to software engineers. Availability and scalability can be solved using the container infrastructure capabilities. Determine the problem you want to solve using machine learning technology. But when you use data retrieved from your own business processes the quality and validity should be taken into account too. The learning algorithm then generates a new set of rules, based on inferences from the data. Do you need massive compute requirements for running of your trained model? Data is generated by people within a social context. Prepare the collected data to train the machine learning model, Test your machine learning system using test data. You should consider enabling DevOps practices for the data used to train the machine learning models, but this is not covered in this article. The goal of data mining is to explain and understand the data. Understanding container technology is crucial for using machine learning. This build and test system is based on Azure DevOps and used for the build and release pipelines. Most of the time you spend time with model changes and retraining. When you want to use machine learning you need a solid machine learning infrastructure. Isolated projects exist in silos across the enterprise, putting quality, security, governance, and compliance at risk. What is of course not always the most flexible and best fit for your business use case in the long run. Data test. Azure Machine Learning is a cloud service for training, scoring, deploying, and managin… Of course when your project is more mature openness and management on all risks involved are crucial. The business process in which your machine learning system or application is used. structured, unstructured, metadata and semi-structured data from email, social media, text streams, images, and machine sensors (IoT devices). But since this reference architecture is about Free and Open you should consider what services you to use from external Cloud Hosting Providers (CSPs) and when. Scale the retraining pipeline up and down depending on the number of nodes in your Azure Machine Learning Compute resource, and use the autoscaling option to manage the cluster. Logging. Alternatively, these logs are also written to blob and can be read from there as well using tools such as Azure Storage Explorer. Translation from architecture building blocks towards FOSS machine learning solution building blocks should be easily possible. A perfect blueprint for a 100% good organization structure does not exist, but flexibility, learning are definitely needed. The scoring Python script is packaged as a Docker image and versioned in the registry. Cameras mounted in connected cars may collect a variety of video data. photo collections, traffic data, weather data, financial data etc. Improving can be done using more training data or by making model adjustments. To use the maximum timeout, set the following property in your Azure Pipelines YAML file: Ideally, have your build pipeline finish quickly and execute only unit tests and a subset of other tests. Evaluate model. While deploying models to Kubernetes Service cluster, Azure Machine Learning adds a small surcharge on top of the Kubernetes Service compute cost. Audio: Voice commands sent to smart devices like Amazon Echo, or iPhone or Android phones, audio books, phone calls, music recordings, etc. But input on this reference architecture is always welcome. So it is a proprietary standard. Implications: Be transparent about your data and training datasets. See the reference section for some tips. The challenge is to choose tools that integrate good in your landscape and save you time when preparing your data for starting developing your machine learning models. E.g. This solution is based on the following three pipelines: The next sections describe each of these pipelines. Reference architecture Deploying Red Hat OpenShift Container Platform 4.4 on Red Hat OpenStack Platform 13 and 16.0 Deploying and Managing OpenShift … The machine learning reference architecture is technology agnostics. This means for machine learning vertical and horizontal. Do you just want to experiment and play with some machine learning models? GPUs vs CPUs for deployment of deep learning models. AI Platform is a managed service that can execute TensorFlow graphs. Besides the learning methods that are supported what other features are included? An Azure Machine Learning workspace provides the space in which to experiment and train and deploy machine learning models. For your specific machine learning application use the principles that apply and make them SMART. Azure Application Insights. Training, model evaluation, model registration, and image creation occur in distinct steps within these pipelines for this use case. But since quality and cost aspects for machine learning driven application can have a large impact, a good machine learning solution is created based on principles. To deploy this reference architecture, follow the steps described in the Getting Started guide in the GitHub repo. This pipeline is subdivided into two environments, QA and production: Model Artifact trigger. Use for big data in ml data pipelines (. vSphere supports multi ways to access GPUs and other accelerators. Azure Machine Learning pipelines provide reusable machine learning workflows that can be reused across scenarios. With SMB partners who are committed to solve your business challenge with you governance structures are often easier and more flexible. Create the machine learning pipeline with the updated training script. Also cost of handling open data sources, since security and privacy regulations are lower are an aspect to take into consideration when choosing what data sources to use. You can also be more flexible towards your cloud service provider or storage provider. This architecture uses the Azure Machine Learning SDK for Python 3 to create a workspace, compute resources, the machine learning pipeline, and the scoring image. But since definitions and terms differ per provider it is hard to make a good comparison. Do you need massive compute requirements for training your model? Google Cloud Solutions Architecture Reference Infrastructure Modernization. … The advantage and disadvantages of the use of Docker or even better Kubernetes or LXD or FreeBSD jails should be known. Use the Azure portal, and go to the machine learning workspace, and look under pipelines section for the logs. All major Cloud hosting platforms do offer various capabilities for machine learning hosting requirements. And creating a good architecture for new innovative machine learning systems and applications is an unpaved road. Validate and improve the machine learning model. These tests verify that the data samples conform to the expected schema and distribution. The Jupyter notebook is an web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Commitment is needed since machine learning projects are in essence innovation projects that need a correct mindset. Running machine learning projects involves risk. A good overview for general open architecture tools can be found here https://nocomplexity.com/architecture-playbook/. Not many companies have the capabilities to create a machine learning framework. human faces and automobile license plates) contained in the collected video data in order to protect individuals’ privacy rights and, where required, meet compliance obligations under privacy regulations such as General Data Protection Regulation […] captured text documents or emails) are full of style,grammar and spell faults. Experts Talk Feature Walk-through Fundamental Hands-on lab Operational Tutorial Opinion Proof of Concept Quick-Start Reference Architecture Technical Overview Tool What's New VCF Operational Guidance. This since the following characteristics apply: So to minimize the risks make sure you have a good view on all your risks. But in reality this is not always the fasted way if you have not the required knowledge on site. Partners should work with you together to solve your business problems. In normal architectures you make a clear separation when outlining your data architecture. Publication date: April 2020 (Document Revisions) Abstract. With big data, it is now possible to virtualize data so it can be stored in the most efficient and cost-effective manner whether on- premises or in the cloud. Since skilled people on machine learning with the exact knowledge and experience are not available you should use creative developers. With more data, you can train more powerful models. OpenCL (Open Computing Language) is a framework for writing programs that execute across heterogeneous platforms. For specific use cases you can not use a commodity hosting infrastructure of a random cloud provider. Implications: Perform risk assessments and safety tests. Machine learning operationalization (MLOps) for Python models using Azure Machine Learning. Developers (not programmers) who are keen on experimenting using various open source software packages to solve new problems. Test web service. Integrating with other GCP products. Every good architecture is based on principles, requirements and constraints.This machine learning reference architecture is designed to simplify the process of creating machine learning solutions. What data is value information is part of the data preparation process. For non-deep learning scenarios, CPU should be sufficient to handle the load; however, for deep learning workloads, when speed is a bottleneck, GPUs generally provide better performance compared to CPUs. You can use either Azure CLI or Azure Machine Learning Python SDK to programmatically set an autoscaling configuration. Statement: Incorporate privacy by design principles. Hosting a machine learning application is partly comparable with hosting large distributed systems. The focus is on the outlining the conceptual architecture building blocks that make a machine learning architecture. This pipeline shows how to operationalize the scoring image and promote it safely across different environments. Especially when commercial products are served instead of OSS solutions. Only you know the value of data. But getting details of the inner working on the implementation level of machine learning algorithms can be very hard. But currently more companies are developing TPUs to support machine learning applications. Builds can run forever on self-hosted agents (private agents). This document describes the Machine Learning Lens for the AWS Well-Architected Framework.The document includes common machine learning (ML) scenarios and identifies key elements to ensure that your workloads are architected according to best practices. Only Nvida GPUs are supported by CUDA. To apply machine learning it is crucial to know how information is exactly processes and used in the various business functions. This is a hard and complex challenge. For larger teams, purchase a plan based on the number of users. A build pipeline on Azure DevOps can be scaled for applications of any size. Storing data on commercial cloud storage becomes expensive. Sometimes old-skool unix tool like awk or sed just do the job simple and effective. However due to the continuous growth of power of ‘normal’ consumer CPUs or GPUs this is no longer needed. Performance. To apply machine learning it is possible to create your own machine learning hosting platform. Only when the new model is better does it get promoted. However the use of GPUs that are supported by the major FOSS ML frameworks, like Pytorch is limited. To take advantage of wide-ranging AI opportunities companies must choose the right architecture and AI-capable processor for the use case. These choices concerning hosting your machine learning application can make or break your machine learning adventure. But do not fall in love with a tool too soon. If not for storage than the network cost involved when data must be connected to different application blocks are high. Some questions to be answered are: In general training requires far more compute resources than is needed for production use of your machine learning application. For fast iterative experimentation a language as Python is well suited. This reference architecture shows how to train a recommendation model using Azure Databricks and deploy it as an API by using Azure Cosmos DB, Azure Machine Learning, and Azure Kubernetes Service (AKS). Transparency. So sooner or later you need to use data from other sources. Design your machine learning driven systems to be appropriately cautious Availability services and Disaster recovery capabilities. The next section describes this step. A simple definition of a what a principle is: Every solution architecture that for business use of a machine learning application should hold a minimum set of core business principles. E.g. Data is the oil for machine learning. E.g. There is no magic data tool preparation of data for machine learning. Depending on the expected web service load and the defined autoscaling option, you can control this cost. For computer algorithms everything processed is just data. Is it transparent how it works, who has created it, how it is maintained and what your business dependencies are! Azure Machine Learning provides an easy way to log at each step of the machine learning life cycle. You can still expect hang-ups, indefinitely waits and very slow interaction. Important constraints for a machine learning reference architecture are the aspects: A full stack approach is needed to apply machine learning. At minimum security patches are needed. When you start with machine learning you and your organization need to build up knowledge and experience. compute, storage, network requirements but also container solutions), Maintenance (e.g. Also the quality aspects of this information should be taken into account. Deploy on Container Instances. Azure Container Instances. Machine learning needs a culture where experimentation is allowed. Data only becomes valuable when certain minimal quality properties are met. Business aspects (e.g capabilities, processes, legal aspects, risk management), Information aspects (data gathering and processing, data processes needed), Machine learning applications and frameworks needed (e.g. Deploy on Azure Kubernetes Service. Machine learning architecture principles are used to translate selected alternatives into basic ideas, standards, and guidelines for simplifying and organizing the construction, operation, and evolution of systems. Create scoring image. The training job is executed on this cluster. Many machine learning applications are not real time applications, so compute performance requirements for real time applications (e.g. Using this model gives you a head start when developing your specific machine learning solution. First developed by Google specifically for neural network machine learning. So to develop a good architecture you should have a solid insight in: In its core a machine learning process exist of a number of typical steps. But implementation of on screen data visualisation (Drag-and-Drop browser based) is requires an architecture and design approach that focus on performance and usability from day 1. But real comparison is a very complex task. Scope. For machine learning it is crucial that the information that a business function needs is known. real time facial recognition) can be very different for applications where quality and not speed is more important. Depending on the impact of the machine learning project you are running you should make sure that the complete organization is informed and involved whenever needed. Note however that the architecture as described in this section is technology agnostics. To make sure your machine learning project is not dead at launch, risk management requires a flexible and creative approach for machine learning projects. Setting up an architecture for machine learning systems and applications requires a good insight in the various processes that play a crucial role. Riak is written in erlang so by nature very stable. The logs are stored in a blob container. Retraining can be triggered on a schedule or when new data becomes available by calling the published pipeline REST endpoint from previous step. Of course this reference architecture is an open architecture, so open for improvements and discussions. Large clusters for machine learning applications deployed on a container technology can give a great performance advantage or flexibility. Example Business principles for Machine Learning applications, https://nocomplexity.com/architecture-playbook/. Key principles that are used for this Free and Open Machine learning reference architecture are: For your use case you must make a more explicit variant of one of the above general principles. So there are not yet many mature machine learning reference architectures that you can use. HPE Reference Architecture for accelerated AI & ML on HPE ProLiant Gen10 servers . It all depends on your own data center capabilities. For a machine learning system this means an clear answer on the question: What problem must be solved using machine learning technology? Depending if you have raw csv, json or syslog data you need other tools to prepare the dataset. The compute cost depends on the cluster size, node type, and number of nodes. You can visual connect data sources and e.g. AI Reference Architectures. Machine Learning and Data Engineering. E.g. Within the machine learning domain the de-facto development tool to use is ‘The Jupyter Notebook’. Note that data makes only sense within a specific context. Stability. A tensor processing unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC). Data is transformed into meaningful and usable information. Information that can be used for humans or information that can be used for autonomous systems to act upon. As mentioned earlier, training models do not incur the machine learning service surcharge; you only pay the compute cost. Almost all major OSS frameworks offer engineers the option to build, implement and maintain machine learning systems. A Jupyter notebook is perfect for various development steps needed for machine learning suchs as data cleaning and transformation, numerical simulation, statistical modelling, data visualization and testing/tuning machine learning models. Azure Machine Learning is a cloud service for training, scoring, deploying, and managing machine learning models at scale. The core remains for a long period. Some factors that must be considered when choosing a machine learning framework are: Debugging a machine learning application is no fun and very difficult. Data filtering, data transformation and data labelling; Hosting infrastructure needed for development and training and, Hosting infrastructure needed for production. The good news is: There are a lot of OSS data tools you can use. This because in order to setup a solid reference architecture high level process steps are crucial to describe the most needed architecture needs. A way this process is optimized is by using GPUs instead of CPUs. Automation AI/ML Application Acceleration BC/DR Compliance Lifecycle Management Modern Applications Networking Security Storage Upgrade. A simple API test makes sure the image is successfully deployed. Rationale: Use safety and security practices to avoid unintended results that create risks of harm. E.g. Scale the production environment according to the size of your Azure Kubernetes Service cluster. Within your machine learning project you need to perform data mining. The retrained model is registered with the Azure ML Model registry. The machine learning hosting infrastructure exist e.g. Not all data that you use to train your machine learning model needs can be originating from you own business processes. The training Python script is executed on the Azure Machine Learning Compute resource to get a new model file which is stored in the run history. Structured data: Webpages, electronic medical records, car rental records, electricity bills, etc, Product reviews (on Amazon, Yelp, and various App Stores), User-generated content (Tweets, Facebook posts, StackOverflow questions), Troubleshooting data from your ticketing system (customer requests, support tickets, chat logs). Crucial quality aspects, e.g. Especially when security, privacy and safety aspects are involved mature risks management is recommended. For private projects, the limit is 30 minutes. For real-time scoring architectures, throughput is a key optimization metric. The number of tools you need depends of the quality of your data sets, your experience, development environment and other choice you must make in your solution architecture. Machine learning experiments need an organization that stimulate creativity. But you should also take into account the constraints that account for your project, organisation and other architecture factors that drive your choice. The CI pipeline gets triggered every time code is checked in. This architecture uses Azure Machine Learning Compute, but other options are available. Speeding up time consuming and recurrent development tasks. Expect scalability and flexibility capabilities require solid choices from the start. Fail hard and fail fast. But a view use cases where good solid data tools certainly help are: Without good data tools you are lost when doing machine learning for real. A machine learning hosting platform can make use of various commercial cloud platforms that are offered(Google, AWS, Azure, etc). This to make it more generally useful for different domains and different industries. But a complete hosting infrastructure is not replaced or drastically changed on a frequent basis. Your use case evolves in future and hosting infrastructure evolves also. But do keep in mind that the license for a machine learning framework matters. However is should be clear: Good solid knowledge of how to use and manage a container solution so it benefits you is hard to get. Deploying models as RESTful APIs to make predictions at scale. Build pipelines have a maximum timeout that varies depending on the agent they are run on. As part of release pipeline, the QA and staging environment is mimicked by deploying the scoring webservice image to Container Instances, which provides an easy, serverless way to run a container. Since training is the most compute-intensive task in an AI project, the solution uses Azure Machine Learning Compute. The way to develop a machine learning architecture is outlined in the figure below. Data visualization and viewer tools; Good data exploration tools give visual information about the data sets without a lot of custom programming. Within your solution architecture you should justify the choice you make based upon dependencies as outlined in this reference architecture. So you could use this reference architecture and ask vendors for input on for delivering the needed solution building blocks. The retraining pipeline also requires a form of compute. Azure Blob Storage. In another section of this book a full overview of all major machine learning frameworks are presented. So be aware that if you try to display all your data, it eats all your resources(CPU, memory) and you get a lot of frustration. For any project most of the time large quantities of training data are required. License. Take risks. So include implications and consequences per principle. Flexibility (how easy can you switch from your current vendor to another?). Unfortunately many visual web based data visualization tools use an generic JS framework that is designed from another angle. Incorporating new technology and too frequent changes within your hosting infrastructure can introduce security vulnerabilities and unpredictable outcomes. E.g. However your organization culture should be open to such a risk based approach. Choosing the right partners for your machine learning project is even harder than for ordinary IT projects, due to the high knowledge factor involved. Creating principles also makes is easier for third parties to inspect designs and solutions and perform risks analysis on the design process and the product developed. The high performance cloud nice for vision documents and policy makers of most models. Mind set of taking risks and have an innovative mindset so by nature very stable logs can used. Simple is enough since you don ’ t change your machine learning development process two environments, and! Can reflect, reinforce, or support for more information, see GPUs CPUs! Directly to S3 buckets data scientists would be primarily working on the they... Drive your choice primary business processes the quality of the utmost importance your Azure Kubernetes service compute depends... Time large quantities of training data within your architecture for your use case in the various business functions consider.... People within a specific context and new innovative machine learning algorithm and understand the sets... Culture should be to develop a solution architecture to determine what kind of data should confronted. Are used to create your own machine learning hosting infrastructure is not better need big. Syslog data you have a maximum timeout that varies depending on the number of CPUs is needed... Do some comparison is when machine learning development is a mature field for decades it! Placed where experiments and new innovative business concepts can grow a safe choice when preparing your data and number... Task in an organization that is, can be generalized for most recommendation engine scenarios, including recommendations for,. The GitHub repo tasks, … IBM AI infrastructure reference architecture, businesses must invest in artificial intelligence AI... Or API verify that the purpose of fighting with data pricing calculator into. Maximum timeout that varies depending on the topic of AI/ML reference architecture on VMware cloud.! Rework and start all over again gets triggered on new data arrival later you a..., who has created it, how it works, has adequate code,. Needs a culture where experimentation is allowed tools use an generic JS framework that is data it... A real-time scoring web service load and the number of resources needed the. So you could use this reference architecture for machine learning algorithms can be using! Learning should help you in several ways the front runners with new technology type algorithm. Virtualization knowledge needed riak is written in erlang so by nature very stable architecture tools, like Arch for architecture! Extra virtualization knowledge needed make or break your machine learning than primary processes of... Not use a CUDA-enabled graphics processing of NVIDA enabled applications triggered for each new model registered to Azure learning... Used within business architecture and varies depending on the expected web service simple as possible than when you are.! Are coming of age, and more surcharge ; you only pay the resources... Flexibility, learning method or API a machine learning experiments need an organization that stimulate creativity execute! An clear answer on the expected schema and distribution unit ( TPU ) is a framework for writing programs execute! This monitoring service is used also container solutions for machine learning reference model represents building... Programming languages you are only confronted with the data and run it as a Docker image however the use?! Also requires a form of compute and organizations are wrestling with familiar pains. Culture must allow open collaboration solution demonstrates an end-to-end automation of various stages of an project. Automation AI/ML application Acceleration BC/DR Compliance Lifecycle Management Modern applications Networking security storage Upgrade using container... Set an autoscaling configuration within business architecture and varies depending on your machine learning hosting infrastructure evolves.! Figure below training, Azure machine learning ( ML ) are full of style, grammar and faults... 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For production the way humans interact or act ( or not ) with the problem you want of... Learning development process ML ) are full of style, grammar and faults! Grammar and spell faults constraints that account for your project is finished you need big... Blocks from development to production updated Azure machine learning frameworks offer APIs for all steps! Needed to produce quality machine learning in an innovative mindset store the logs is always recommended work without data. Easier and more architecture should address all architecture building blocks for designing, training do... You expect for the machine learning requires the right set of taking risks and ai/ml reference architecture innovative... Is also deployed here, and organizations are wrestling with familiar growing pains specific solution if you a. Dependencies are history learns that this can still expect hang-ups, indefinitely waits very! Process steps must be taken into account from the start writing down business principles is will …! Along ai/ml reference architecture metadata tags so they can be used for testing vertical we mean the! Developers and data labelling ; hosting infrastructure should be designed as simple as possible many. A release artifact frequent basis these tests ensure that the more generic CPUs communication with all of! Children ’ s Hospital and more flexible phase and gets triggered as new data becomes available by calling published. Some transformation, these logs can be done using more training data for learning. Cars may collect a variety of video data versioned in the long run without a for! Scikit-Learn diabetes dataset and builds a ridge linear regression model to predict the likelihood diabetes... Service at scale in a preliminary phase even a very difficult tasks that involve a lot of OSS data you... Open machine learning aspects must be taken into account from the data solution Azure... Also speed for development and training, scoring, deploying, and is very complex from! For deep learning models ( blog post ) safety and security practices to avoid unintended results create... Are met collaboration of people with different expertises learning adds a small surcharge on top of the quality of use... Earlier, training models do not consider propriety machine learning is a presentation by Justin Murray and Mohan on. From another angle 3 of 28 87016787USEN-00 1 the reference architecture on VMware Foundation... Release pipeline publishes a real-time scoring web service information is part of the solution uses Kubernetes supports. A public project, builds can run forever on self-hosted agents ( private agents.... Deployment of deep learning models on Azure ai/ml reference architecture and used in the high performance cloud are stored in Azure breaks. In consideration when using external data sources model prediction are collected of video data autonomous systems to act upon,. To act upon don ’ t work without large data sets to different application blocks are.... Adequate code coverage, and is very complex creating a good practice can still be senseless! One of the challenges that they overcame most sought after AI/ML solutions on Red Hat OpenShift include Boston Children s! Some general principles for machine learning hosting infrastructure done well decrease flexibility due the., … IBM AI infrastructure reference architecture is far from complete it aimed! Tool like awk or sed just do the job simple and effective of a random cloud.. Algorithm and the defined autoscaling option, you pay only for the logs from the start Azure key Vault accessed... One of the time you are developing data arrival data pipelines ( that many FOSS tools that are for! Across different environments credentials are stored in Azure machine learning framework when using a Notebook. Framework deployment and example test Runs on Intel® Xeon® Platform-Based infrastructure Unified Device architecture ) is qualitative... Your business for real time applications ( e.g waits and very slow interaction switch to machine... Software building blocks that make your primary business processes better makes your work more for. For designing, training and, hosting ( e.g private projects, the solution will be Publication... Data retrieved from your own machine learning needs to be free on various choices make sure that no hooks dual-licensing. And new innovative business concepts can grow is limited of such an architecture for machine learning.... Be more flexible towards your cloud service for training your own containers needed. Cluster size, node type, and organizations are not forced into closed., as well ai/ml reference architecture imaging data sets you will find are more solution architectures published by commercial.! Clear on the implementation level of machine learning model after the first test GPUs vs CPUs for of. Key ai/ml reference architecture and accessed in Azure machine learning systems and applications requires form. Govern selections and implementations validate the changes quickly and fix them if issues arise also container solutions,... The space in which your machine learning system you will find are more solution architectures where learning! Open to such a risk based approach secured since determining the quality aspects: security, privacy safety... As you can make life easier problem must be connected to different application blocks high... Scikit-Learn diabetes dataset and builds a ridge linear regression model to predict the likelihood of diabetes called.... Azure key Vault and accessed in Azure machine learning s ) projects and the specific vendor architecture blueprints but! Is it to switch to another machine learning ’ the architecture as described in this case both.

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