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To solve these challenges, a ML lifecycle tool is required that can automatically track and log these dependencies during the training phase as configuration as code and later bundle them along with the trained model in a ready-to-deploy artifact. I would recommend you rely on a tool or platform that can instantly translate code from one language to another or allow your data science team to deploy models behind an API so they can be integrated anywhere. Neural nets are often very deep, which means that training and using them for inference takes up a lot of compute power.
Usually, we want our algorithms to run fast, for a lot of users and that can be an obstacle. However, they are scarce and expensive, which easily adds another layer of complexity to the task of scaling ML. Another interesting challenge of model deployment is the lack of portability. I noticed that it is often a problem with legacy analytics systems. Lacking the capability to easily migrate a software component to another host environment and run it there, organizations can become locked into a particular platform.
This can create barriers for data scientists when creating models and deploying them. Scalability is a real issue for many AI projects.
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Indeed, you need to make sure that your models will be able to scale and meet increases in performance and application demand in production. At the beginning of a project, we usually rely on relatively static data on a manageable scale. As the model moves forward to production, it is typically exposed to larger volumes of data and data transport modes. Your team will need several tools to both monitor and solve for the performance and scalability challenges that will show up over time. I believe that issues of scalability can be solved by adopting a consistent, microservices-based approach to production analytics.
Teams should be able to quickly migrate models from batch to on-demand to streaming via simple configuration changes. Similarly, teams should have options to scale compute and memory footprints to support more complex workloads. For all these reasons, only a few data science projects end up actually making it into production systems. We always spend a lot of time trying to make our model ready.
Robustifying a model consists of taking a prototype and preparing it so that it can actually serve the number of users in question, which often requires considerable amounts of work. In many cases, the entire model needs to be re-coded in a language suitable for the architecture in place. That point alone is very often a source of massive and painful work, which results in many months' worth of delays in deployment.
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During my projects, I also noticed the following issues:. As you may already know, models evolve continuously due to data changes, new methods, etc. As a consequence, every time such a change happens, we must re-validate the model performance. These validations steps introduce several challenges:. Apart from the validation of models in offline tests, assessing the performance of models in production is very important.
Usually, we plan this in the deployment strategy and monitoring sections. There are always new discoveries just on the horizon and it is these technologies that keep combat marching forward into the future. Through its online programs, Norwich delivers relevant and applicable curricula that allow its students to make a positive impact on their places of work and their communities.
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Contact Us Request Info Apply. Student Login Course Catalog Search form. Moving Forward It did not take long until Maxim sold the gun he had created and watched as it quickly catapulted to even larger stardom on the battlefield. Changing Battle Tactics with the Introduction of the Machine Gun The earliest, most primitive renditions of the machine gun worked entirely from a hand crank but by the ending of World War I in , the machine gun was entirely automatic and was capable of producing an output of up to rounds per minute. Machine Guns: The Future and Beyond As we have seen over the course of this article, warfare is always evolving.
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