$297.00You will learn how to use the latest tools and techniques to capture, process, and interpret visual data. The course will also cover the principles of good
Computer Vision Software Development works by using machine learning algorithms to process and analyze visual data. This can include tasks such as image recognition, object detection, and scene understanding.
Best Practices in Computer Vision Models Deployment
Test-driven machine learning development
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Computer Vision Model Lifecycle
Deploying a computer vision model or rather any machine learning model is a challenge in itself, considering the fact that only a few of the developed models go to continuous production environments.
The CV model lifecycle starts from collecting quality data to preparing the data, training and evaluating the model, deploying the model, and monitori.
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Cv Deployment Through API (Rest/Rpc) Endpoints
REST stands for “representational state transfer” (by Roy Fielding).
In a nutshell, REST is about a client-server relationship, where data is made available/transferred through simple formats, such as JSON/XML.
The “RPC” part stands for “remote procedure call,” and it’s often similar to calling a function in JavaScript, Python, etc.
Please read thi.
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Cv Model Deployment Maturity Levels
The success of an ML deployment mostly lies in the level of automation.
So that means the lesser the manual intervention required to maintain the model in production, the more mature it has become.
The levels of maturity can be attributed to current deployment practices shown below.
You can read the detailed article on the Google Cloud documentatio.
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Cv Model Deployment Modes, Platforms & UI
In this section let’s dive into different ways you can deploy and serve Computer Vision models.
The key elements that need to be considered here are:.
1) Deployment modes (with REST/RPC endpoints, on the edge, hybrid).
2) How they are served to the end-user.
3) Ease of access to hardware and scalability of the deployment platform
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Cv Model Deployment UI
After discussing the API endpoints, the time is ripe for us to take the discussion to the popular UI options available for CV deployments.
Let’s explore some of the popular & convenient UI options available.
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Cv Model Serving Platforms
All of the things discussed above, be it ML lifecycle operations or a UI, require some sort of platform to operate.
The choice between these platforms is often primarily based on: For example, the users often want AI to be invisible while they use the solution.
That means often users don’t want to get tangled in the intricacies of AI-based decision.
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Edge Deployment of Cv Models
Before diving into the concept of on edge deployment, it’s important to understand what edge computingis.
Simply put, edge computing is a process where computing happens at the same location or close to where the data originates.
Nowadays businesses are flooded with data that they really don’t know what to do with.
The traditional way of analysis w.
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How difficult is it to deploy a computer vision model?
Deploying a computer vision model or rather any machine learning model is a challenge in itself, considering the fact that only a few of the developed models go to continuous production environments.
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Other Tools That May Be Useful When Deploying Cv Models
neptune.ai
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What is a computer vision model development?
A computer vision model development could develop additional tool overheads like specialized annotation tools compared to NLP or classic Machine learning model developments.
It’s important to note that close to 90% of ML models never make it to production; the same goes for Computer vision models.
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What is computer vision & why is it important?
Computer vision is the subset within AI that processes visual data.
Advances in AI — specifically deep learning and neural network innovations — have made it possible for computer vision to become as good at recognizing objects and patterns as the human eye.
The human visual cognitive system is complex.
