AI Development Challenges
These days artificial intelligence (AI) integration into solutions is one of the routine challenges of a Software Developer. In the early days of AI understanding algorithms and coding an entire machine learning framework was mission critical and usually within a small timeframe. Failure to produce an artificial intelligence was always an option, with many Developers not understanding how to create a coherent statistical analysis neural network that was optimised for its specific usage, not understanding how to iteratively assess a node effectively or the number of layers. Challenges of using poorly selected algorithms due to ambiguity, stopping machine learning processes too fast and producing error outside the model tolerance, error of design through poor user research and the list goes on.
These days Software Developers in a relatively short space of time have extensive domain knowledge freely available to them. Templated models created in controlled environments that can be disseminated to a whole team of Developers within the space of a few minutes. Need to create something complex and resource intensive, this can be uploaded to a cloud server and the machine learning (ML) for your project created extensively faster than on site.
The staple of online AI applications these days are modelled on the Software as a Service (SaaS) leveraging cloud real-time AI while the end user only needs a light-weight application that can be run on practically any device practically anywhere. For offline edge devices such as ‘assisted driving’ cars and manufacturing real-time inline quality inspection devices where connectivity to a server may be intermittent or cause too much lag AI ML is conducted first and then the AI placed into the product solution. This means the neural network weighting values are iteratively optimised based upon a dataset without further modification when shipped.
What is an AI Software Development Kit (SDK) Toolchain?
Deep learning SDKs are development kits that contain tools, coded functions, that can be called by the Software Developer to produce solutions quickly. Many AI solutions whether online or offline are created in standardised environments such as Docker. TensorFlow utilises it to its advantage as it allows team sharing during the development phase while also allowing a form of standardisation for deployment on hardware, while not the only way to work it can be very quick to integrate AI. What does this mean, well unlike virtual machines (VM) that identify and allocate resource based on a particular hardware build, these environment frameworks allow for unification at the operating system up while the hardware and system firmware can be very different.
So, what does this environment standardisation allow for deployment. Well, this is where AI software SDK toolchains become effective. The user trained neural network is high level. To get the hardware to interact with it a compiler is used between the hardware and the software and works as a profiler and emulator through various sublayers.
To optimise the solution a dedicated AI processor is typically housed on a PCIe card allowing it to be used in many diverse solutions with sensor input being taken by a regular CPU and pushed to the PCIe card for analysis and then fed back accordingly.
Another interesting feature of this type of AI SDK Toolchain is that by replacing the neural network standardised environment you instantly reused or recycled the device to perform a different operation. One day it could be used as a retail security system, identifying people and objects in store through a camera system mainboard, then quickly changed to conduct thermal sensing of customers during future pandemics as necessary requires.
An Exciting Future
What if you fail to sell an original product containing the hardware, well as it is mounted using PCIe it can be remounted in another product and given another neural network. This also allows for interesting business models to become achievable through deep learning SDKs. Perhaps in future more AI based devices will not be sold but provided as part of a service contract similar to a photocopier. The modular nature and dexterity of the Toolchain allows for newer products to user older hardware. When a photocopier is renewed after the end of a service contract it is usually through a restyled exterior and only key wearable parts. The possibilities due to the modular nature of both the AI SDK Toolchain and modular hardware and dedicated AI processor options a very exciting and sustainable place for users and a much more efficient working environment for Software Developers.
