AI-Enabled Tools for Faster Software Development

Software Development

For entrepreneurs and business organizations of all shapes and sizes, software development is paramount, particularly mobile app development and enterprise software.

In our extremely digital world today, if you want your business to succeed, then you should also get into online marketing. Software apps significantly contribute to routine activities across companies.

Although software is compelling, the development is a tough endeavor. The process is complex, which requires the definition of a product, ideation, coding, quality assessment, and strategic designing. Moreover, if any step in the process goes wrong, the whole process should be done all over again.

The process of development is furthermore driven by the evolving market trends, thus there’s no room for distraction. Fortunately, there are technologies that help streamline custom software development. One of the emerging technologies is AI or Artificial Intelligence, with its systems and tools that enable organizations to perform tasks accurately and efficiently.

Faster Development with AI-Enabled Tools

1. IBM Watson

Among the best AI-driven development tools for business organizations that demand better and faster results. The tool comes pre-trained and pre-integrates on a flexible information architecture that’s optimized to accelerate AI production and deployment.

It helps organizations automate processes, make predictions that are more accurate, interact with users, and augment expertise.

It has the developer tools, which make integrating language, conversation, and search into apps easy. It moreover provides access to clients to detailed developer resources for faster documentation, enriched interactions, accelerated R&D, and anticipating the market trends, and risk mitigation, and so on.

2. H20 Platform

A machine learning, open-source, distributed platform with linear scalability. It supports the most widely used machine learning and statistical algorithms, which include generalized linear models, gradient boosted machines, deep learning, and many more. Created with mobile apps, it helps languages, such as Java, Python, and R.

The use instances are customer intelligence, data analysis, and risk analysis. H20 works on the current big data infrastructure, on existing Hadoop or bare metal, Kubernetes, or Spark clusters. It could directly ingest from Spark, S3, HDFS, Azure Data Lake, or any other source of data towards its in-memory distributed key-value store.

3. ML Kit by Google

The market’s most common software development tool today. It brings the machine learning expertise of Google to mobile developers in an easy-to-use and powerful package. It can be used on Android and iOS for an engaging and more personalized experience.

The processing of the tool occurs on-device, which makes it fast with real-time use scenarios such as camera-input processing. Also, it works offline and could be used to process text and images that should remain on the device.

Google’s ML Kit is a combination of ML learning models that are best-in-class with advanced processing pipelines, which offer APIs that are easy to use to allow powerful use cases in the applications. The Image Analysis and Video APIs enable barcode scanning, image labeling, as well as facial detection.

4. TensorFlow ML Platform

An end-to-end open-source machine learning platform. It has a flexible and extensive ecosystem of libraries, tools, and community resources that allows researchers to push the state-of-the-art in Machine Learning and developers to build and deploy ML-powered apps fast.

With the tool, organizations could train and deploy cloud models, on-premise, on a browser, or on-device effortlessly. It creates and trains machine learning models with high-level, easy, intuitive API, like Keras, making it easy to debug and immediate iteration model.

5. Mxnet

A deep-learning, open-source platform that’s perfect for flexible research prototyping and production. It establishes scalable performance and distributed training for research and production optimization via parameter server and hooved support. Its use scenario includes NLP, computer vision, and time series, among others because of its thriving ecosystem.

6. Infosys NIA

The next-generation AI platform for building, with a combination of Infosys Mana, AI platform, Robotic Process Automation, and AssistEdge. The capabilities of the platform include socialization of deep analytics, organizational knowledge, automated incident root cause analysis, service automation, and others.

It handles break-through business issues like forecasting what products should be built, revenue forecasting, understanding the customer behavior, deep understanding of the content of legal documents and contracts, and understanding fraud and compliance.

7. Dialog Flow

It uses the infrastructure of Google and incorporates its ML capabilities. Because it runs on the Google Cloud Platform, thus you should be able to scale your API application in a seamless manner. It enables you to create a text-based and voice conversational interface for your application.

Your app could run on mobile and web, and you could also connect your users or customers on Amazon’s Alexa, Google Assistant, Facebook Messenger, and so on. The key capabilities that it offers include NLP, ML, and speech.

8. BigML

The tool is highly-focused on machine learning, offering powerful ML capabilities. Furthermore, it provides strong machine learning algorithms for both supervised and unsupervised learning. Instant access could be implemented to its ML platform with the use of the REST API, and you could do it both on the cloud and on-premises.

BigML offers exportable and interpretable ML models, which is a major advantage. The platform is repeatable and programmable. You could use popular programming languages, such as Node.js, Python, Java, Ruby, Swift, and so on for app coding.

Moreover, it helps automate predictive modeling tasks with a flexible deployment since you could deploy an AI app on-premises and in the cloud. It also has smart infrastructure solutions, which help in app scaling.

Conclusion

With the all changing needs of people and businesses, the emergence of new technologies, the ultimate question is whether AI could help subvert the human need for the development of technology.

There’s no doubt how big the growth of AutoML solutions is, which aim at automating the machine learning model training process, thus, lessening the workload of data scientists.

Artificial Intelligence, in general, seems to have a considerable impact on the designing and building of software. A software development company is required to adopt the potential AI benefits that prove to be a game-changer in the process of development.

AI tools, such as those mentioned above, support human developers by taking over the routine tasks at each step of the software development lifecycle.