Deploying CV Applications with Apture

From Cloud to Edge deployment, Apture provides everything

Get the best deployment solutions with Apture

Apture allows deploying the use cases as per the flexibility of a business based on their requirement and capabilities. For executing model inferences on the Apture platform, the platform manager will need to connect an inference device to the platform’s interface in order to run the models. These inference devices can be the cloud, on premise servers or certain edge devices. Apture supports deployment with all these three options. 

Why do we need to deploy use cases?

ML models when built require deploying them so that they can be used by the users. Hence, it needs to be put in some place where it can be easily accessed by the people who need it.  

Deployment usually requires trained ML models and building an API or a UI for the users to interact with these models. A business has to set up a deployment strategy that works the best for them.  

Apture takes care of all of these. 

What does it mean by deploying on cloud or on edge?

Cloud deployment means that the models will run on a remote server of our or the user’s choosing. It is the default method of how most of the SAAS applications run. Based on the service provider and the number of computing resources being used, the cost will vary for cloud deployment. 

With edge deployment, the models are deployed very close to where the business operates. Usually, edge AI solutions are deployed right on the business premises or very close to where the data is captured. The reliability and the latency of the response from such devices is much higher. 

Apture's Cloud

Apture’s cloud deployment helps setting up your system quickly with minimum hassle. The databases and the models can be stored and run-on multiple cloud platforms ranging from GCP, AWS, Azure, IBM Cloud, etc., within our own cloud instances or from one of the business’s choosing. 

Deploy From Anywhere

Apture's Cloud deployment allows deploying computer vision solutions from anywhere to a secure cloud service, our default ones or a one of your choosing.

Mitigate Risks Faster

Apture's Cloud deployment helps with faster issue identification and hence a shorter response time to action allowing quick risk mitigation.

Reduced Expenses

Due to cloud usage, the upfront cost for the hardware requirement and the reduced cost due to quick risk management helps reduce costs even further.

Data Protection

With Cloud, the data remains within a business's control and ownership allowing better usage of the data.

Get Started with our best CV solution offerings today

Apture's On Edge Deployment

With Edge AI deployment, it is possible to deploy AI models at the edge of the process i.e., just where the data gets collected. There are a number of edge devices such as OAKD cameras or other AI cameras available currently that can run ML processes such as inference on themselves. Such edge cameras can be connected to the Apture platform seamlessly and can work with cloud as well as on-premise servers for processing, reporting, or analysis purposes. 

Improved Safety & Reliance

With a total control of your data and processing equipment, Apture allows better control and an improved safety for your entire system.

Data Security & Privacy

Keeping the data within the premises helps with achieving better security of the data and privacy of the of the subjects in the data.

More Hardware Flexibility & Seamless Integration

Upgrading or downgrading the hardware becomes easier as per the business need and a better integration with other systems can be achieved using Apture.

On Site Deployment for faster inference

With on premise availability of the processing machines, the latency for inference is reduced helping businesses achieve quicker inference and better reliability from their AI solutions.

Get Started with our best CV solution offerings today

On Premise Deployment

For on premise deployment using Apture, normal computing devices or specialized ML hardware can be installed for businesses to help deploy their computer vision use cases locally, with improved speed and reliability. With on premise deployment, data security and the privacy of the subjects within the scene are also better handled as the data never leaves the premises. But, with on premise there can be certain scalability issues with storage or the processing that the user must keep in mind. 

What will work the best for you ?

On Premise or On Cloud