Artificial intelligence

Top 5 AI tools for Software development.

Top 5 AI tools

These days, AI is one of the most promising technologies out there. The software industry’s mindset has changed dramatically because of AI. Whether you’re an application development company or a business owner who’s using a software solution, you must have thought of using AI to make your product better. 

AI can help your business strategize better and do things faster. It enables you to focus more on core competencies instead of repetitive tasks. AI can even redefine the human experience. There are many ways companies are using AI in their day to day work, from auto filling fields in an online form and runbook automation to engaging with users. 

There are certain benefits of using AI tools for your software development. such as:

  • Programming Assistant: Programming Assistants provide timely support and suggestions when coding. These also help in understanding documentation. Some AI services automatically generate relevant code by creating pre-defines modules so that software engineers can focus more on the complexities of the tool.
  • Bug Fixing: This is one of the most important uses of AI in software development. AI tools auto-correct your code without requiring human intervention. The AI-based tools can also test the code once it’s done.
  • Delivery Estimation and Strategy: AI tools create precise planning for the project based on details and manpower dedicated to it. This helps schedule a near-perfect delivery date estimation. AI algorithms also help to identify features and functionality for a project based on similar projects done by the company in the past.   

Here are the top 5 AI tools that are being used in the market today. 

1) Amazon Machine Learning Studio:  Through Amazon Web Service (AWS), Amazon provides a host of AI functionalities. They have pre-trained services for different machine learning and AI functions. For example:

  • Amazon Sagemaker, a service that allows developers to build, train, and deploy Machine learning models. 
  • Amazon Comprehend, a service you can use for natural language processing (NLP). 
  • Amazon Codeguru, a service that performs automated code reviews. It also provides recommendations for improving application performance.
  • Amazon Personalize is a service used for making recommendation engines. It helps in content creation and targeted marketing campaigns.  
  • Amazon Forecast is used for business decision forecasting. 

2) Infosys Nia: Infosys Nia does AI deployments to generate business outcomes. It is mainly used for enterprises. It gathers data and feeds into the legacy system. It has different platforms such as Data collection and manipulation platform, knowledge platform and RPA platform. It’s components are:

  • Nia Alops: It Identifies irregularities and provides predictive and preventive management through Big data and AI models. It also improves efficiency, Meantime to Repair (MTR)  future proofs IT Operations. 
  • Nia Document AI: It uses information from an organization’s documents and creates dashboards from actionable data. It can also do intent classification and sentiment analysis. 
  • EDgeverve Business applications: These are AI applications that are for specific business challenges. 

3) IBM Watson: IBM Watson is a suite of enterprise-ready AI services and tools. With an extensible model, it helps developers deploy their solutions through the IBM Watson Studio. It also retains models and generates APIs. It provides support to SUSE Linux Enterprise Server 11 through Apache Hadoop base. It has different deployment options such as

  • Watson Machine learning server: This is a single node server deployment. It has benefits such as Simple installation and no CPU limitations. 
  • Watson Machine learning local: This is a local machine deployment instance. It can be used to build an analytical model and neural networks. It can train complex models on the experiment builder. 

4) Google Cloud ML Engine: This is a machine learning tool by Google that provides quick, easy, and cost-effective deployment. This engine uses Google’s open-source platform Kubeflow to build portable ML pipelines without changing code.  For preparing data, you can use Bigquery. You can use Jupyter notebook with a Deep learning VM image for building models. Training and prediction services are configured into the platform too. Console or Kubeflow pipelines manage end-to-end workflow. It features:

  • NLP through its RESTful APIs. 
  • Speech to text capabilities. It uses the Neural model to change 120 languages from speech to text. It also has text to speech capabilities, and it can create mp3 files out of text files.
  • Images as data. Vision capabilities through RPC and REST API’s help derive insights from images. It can also detect faces and handwritten texts.

5) H2O AI: This is an open-source, distributed, ML platform that allows you to build and train models. H2O is a fast and scalable platform. Written in Java, this platform provides REST API along with ML capabilities. Half of the Fortune 500 companies use this, and they have a 330% growth in the last two years. This has both Supervised and unsupervised algorithms. It can integrate with Hadoop and with distributions Cloudera CDH, and IBM Open Platform. It can also integrate with Conda, an open-source, cross-platform environment management system. It features:

  • AutoML functionality included. It is readily scalable. It has an In-memory, distributed structure. 


So these are the top 5 AI tools available on the market that you can use for your software development. Software development companies use these tools to solve real-life business problems and make their tech stack better. 

AI tools, in no way, provide a completely autonomous solution. They can only help data scientists and programmers in their efforts to tackle business needs. AI is used, ultimately, for making people’s lives easier. It depends on how you leverage these tools to support your business requirements that will define their use. 

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