There are several digital innovations to transform artificial intelligence (AI) deployment in the software development lifecycle. With the recent growth of AI, the way software engineers are writing code is changing.
The evolution of AI is facilitating massive data processing and allowing the computer to perform actions previously reserved for human minds.
AI is optimizing the software development lifecycle, revolutionizing the way programmers solve deterministic problems and apply logic.
As a software developer, you can utilize tools to streamline your AI production. Read on to discover several digital innovations to transform AI deployment in your development lifecycle.
1) Open-Source Machine Learning Frameworks
Open-source machine learning (ML) frameworks are another innovative digital solution altering AI deployment in your development pipeline.
Usually, these platforms support machine learning and easy computation deployment from desktops to server clusters to mobile devices. Open-source ML frameworks are ideal if you are looking for an AI platform that can lift heavy workloads and facilitate projects from scratch.
With this digital solution, you can power applications requiring numerous AI capabilities. For example, you can train your own image recognition system, music manipulation, or language processing model. Open-source machine learning frameworks are transforming AI deployment with their workload scalability.
2) Bot Services
One digital innovation that is transforming AI deployment in your development lifecycle is bot services. Bot services provide resources to build, manage, test, and deploy sophisticated bots in one single location.
Popular digital services like chatbots are improving digital marketing campaigns and strategies for modern organizations. Through modular SDK framework, you can create bots that use speech, understand natural language, and handle user questions.
Typically, bot service platforms provide command-line tools to help you manage and test bot assets. Often, they can configure LUIS apps, build a QnA knowledge base, and build models to dispatch components. Bot services are significantly optimized AI deployment in the software development lifecycle.
3) Artifactory Docker Registries
This containerization technology allows you to manage development, vulnerability analyses, artifact flow control, and distribution throughout the software development process.
As a universal repository manager, artifactory supports all major package formats with exhaustive metadata for any development ecosystem.
Additionally, Docker repositories also optimize automation scripts with REST API and CLI to facilitate a more efficient delivery. In this way, artifactory Docker registries are transforming AI deployment in the software development lifecycle.
4) Python Libraries
Moreover, Python libraries are another power digital innovation to transform your AI deployment. As mentioned earlier, Python is one of the best IoT programming languages for developers.
Typically, Python libraries allow you to define, optimize, and evaluate mathematical expressions with multi-dimensional arrays. With a Python library, it is possible to achieve competitive speeds that rival C-implementations for problems involving large amounts of data.
In addition, Python libraries are capable of taking your structures and transforming them into efficient code that can integrate with NumPy, C++, and native libraries.
Optimal for GPUs, most Python libraries provide symbolic differentiation and expansive code-testing capacities. Python libraries are can be an influential tool to transform your AI deployment within your development pipeline.
5) Cloud-Hosted Vision API
Cloud-hosted vision API is also a game-changer for AI deployment in your software development pipeline. When properly integrated, development teams can leverage APIs as a part of a digital strategy.
Vision API offers powerful pre-trained machine learning models through REST and RPC APIs. This technological tool allows you to assign labels to images and quickly classify them into millions of pre-defined categories.
In addition, using vision API, you can develop applications to detect objects, faces, and read printed and hand-written text. Furthermore, you can build valuable metadata into your image catalogue.
Vision API also supports fast, highly accurate deployment image classification models. Cloud-hosted vision API is an essential innovative tool to optimize your AI deployment in your software development.
There are several digital innovations to transform AI deployment in your development lifecycle. For example, bot services offer command-line tools to help you manage and test assets.
Open-source machine learning frameworks can facilitate large scalable workloads. Moreover, artifactory container registries can optimize AI deployment with REST API and CLI automation technology.
Cloud-hosted vision API can help you build valuable metadata into your image catalogue. Furthermore, Python libraries can help you optimize and evaluate mathematical expressions with multi-dimensional arrays.
Consider the above points to learn about digital innovations to transform AI deployment in the software development lifecycle.