Deployed ML models are being used to solve business problems already. There are several risks associated with machine learning ML model deployment , however, this makes it important to follow the best practices to deploy machine learning models. The best practices for a successful model deployment are discussed in this blog post.
Some best practices that should be adopted for successful model deployment are discussed below. It is important to use large datasets for the deployment of ML models.
Also, the data has to be available in real-time so it has to get fast and accurate predictions. For example; before the machine learning model is deployed, there needs to be a streaming source of the data. These data can be stored in data warehouses and databases. A data lake environment with easy and efficient access to multiple data sources also needs to be set up.
This data needs to be fed to the machine learning models speedily. When there is a well-structured pipeline, it ensures that the machine learning model continuously receives data after it has been deployed. It is important to choose the right tools for deployment. It is much recommended to use AutoML tools to build the deployment pipeline.
It will also help to boost the efficiency of the ML models. AutoML helps to automatically perform feature engineering, data preprocessing, thereby reducing the risk of human error. It also helps to choose the right ML algorithms. AutoML tools should also integrate with the deployment pipeline. Choosing the right AutoML tools also depends on technical requirements like the machine learning ML model deployment requirements, the type of data, the platform features, etc.
It is recommended to have a robust approach to ML model deployment. It can be robust by integrating the model both as an API endpoint and a graphical user interface. And building a smooth ML pipeline architecture so that all the teams can work seamlessly. A complete environment and large datasets also need to be made available. The right ML frameworks need to be chosen for the monitoring, reporting, and logging of results.
This will make testing and deployment seamless. The ML deployment pipeline should be tested in real-time and closely monitored, test results can then be sent back to the data scientist to retrain the model if necessary. For example, the data scientist can add more features to the dataset to improve the machine learning model.
Data quality and model performance also need to be closely monitored. The successful deployment of machine learning models also depends on good communication between all the teams involved. The Data science teams need to work together with machine learning teams in order to deploy the models. The machine learning engineers also need to have full control of the systems.
Transparent communication is highly recommended during machine learning deployments. To perform a successful model deployment it is important to choose an architecture that best suits your machine learning system.
There are two most commonly used architectures for machine learning ml model deployments, both have their pros and cons. The pre-computed model prediction architecture is one of the simplest and earliest serving machine learning models. It uses an indirect method for serving models. They strongly feels that post production support is not meant for the project team. In reality there is usually more knowledge transfer required than actually done during handshake period.
Hence there tends to be an overlap which is sometimes referred to as a warranty or post-implementation operational support period. The objective of the Post-Implementation Phase is to maintain and enhance the system to meet the ongoing needs of the user community. The Post-Implementation Support Phase begins once the system is in operation, the warranty period has expired, and the production review is complete. Post implementation activity may be the regular warranty support.
This includes providing the support necessary to sustain, modify, and improve the operational software of a deployed system to meet user requirements. Post Implementation is the final stage in an application development project. A process document describing the post-implementation process guides the activities performed in the post-implementation phase, which generally consists of the warranty period as per the contract signed by the client.
It also includes helpdesk support, fixing the bugs, and planning for release of the reworked application and all other activities pertaining to the overall support of the system in action. Most well-staffed projects do not have dedicated project resources but are made up of a combination of project team, outsourced consultants and assigned resources from operations.
The main activities in the implementation stage are planning and defining the process for rollout, to deploy the new application, train users on the new system after the rollout has been implemented, and communicate the details of deployment to relevant people.
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The user can view a list of IP addresses and restoring percentages. These features provide an overview that makes remote management much easier.
The software also has dissimilar hardware support. That means that you can deploy OSs to the target computers with different types of hardware. Ivanti is an automated OS imaging platform with zero-touch imaging and migration capabilities.
The software is easy to configure with a range of wizards to help set up OSs and drivers. To provision a new service, all you need to do is click on the type of service you want to deploy. There are around different commands you can use to manage installations. Packages can also be monitored throughout deployment through the Software Package Lifecycle dashboard , which shows the status of packages. If there is a problem during deployment, Ivanti supports remote administration so the user can control devices and systems remotely.
Ivanti is ideal for geographically dispersed teams who want a simple OS deployment platform to work from. To view the pricing information for Ivanti you will have to request a custom quote form the company. You can download the free trial version. It also automates driver deployment and picks the best driver for a given platform, speeding up the wider deployment process. Automatic driver capturing and indexing allows you to work with new hardware almost immediately without installing any add-ons.
The user interface makes it easy to keep on top of deployment progress through the dashboard. The dashboard displays real-time metrics and reporting features that enable you to manage deployments.
The software is also available for your phone or tablet so that you can remotely manage your network no matter where you are located.
However, if you need something different then you can request a custom quote from the company directly. There is also a day free trial. Paragon Deployment Manager is an OS deployment platform that can install systems on bare-metal and virtual writable devices.
Paragon Deployment Manager is easy to install and use with a Wix-Based installer t hat takes you through the process of configuring your administration environment. When it comes to deploying software, you can deploy to dissimilar hardware and groups of devices.
There is also the ability to schedule future deployments. To maximize efficiency you can also back up changes to master images. To view the price of the Paragon Deployment Manager you will have to contact the company directly. You can contact Paragon by filling out the form here.
DeployStudio is a freeware image deployment platform for Mac OS. Users can deploy to Mac computers and monitor deployments in real-time. Deploy Studio can be used through a hard drive and remotely, making it suitable for networks of all shapes and sizes. Workflows drive the bulk of the user experience.
Workflow tasks can be created for setting a hostname , creating a new user, creating additional network locations , and inputting a site license number. There is also the ability to complete more general administrative tasks such as running scripts , partitioning disks , installing packages , copying files , and restarting systems after completing a workflow.
These features give the user complete control over OS deployments without the need for any technical knowledge. You can download the program for free.
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