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Top 25 AI Tools for Software Development in 2025

Marktechpost

It suggests code snippets and even completes entire functions based on natural language prompts. TabNine TabNine is an AI-powered code auto-completion tool developed by Codota, designed to enhance coding efficiency across a variety of Integrated Development Environments (IDEs).

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Unleashing real-time insights: Monitoring SAP BTP cloud-native applications with IBM Instana

IBM Journey to AI blog

This solution extends observability to a wide range of roles, including DevOps, SRE, platform engineering, ITOps and development. You can find a complete list of supported technologies for IBM Instana on this page. Auto-discovery and dependency mapping : Automatically discovers and maps services and their interdependencies.

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Modernizing child support enforcement with IBM and AWS

IBM Journey to AI blog

With its proven tools and processes, AIMM meets clients where they are in the legacy modernization journey, analyzing (auto-scan) legacy code, extracting business rules, converting it to modern language, deploying it to any cloud, and managing technology for transformational business outcomes. city agency serving 19M citizens.

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Application modernization overview

IBM Journey to AI blog

Application modernization is the process of updating legacy applications leveraging modern technologies, enhancing performance and making it adaptable to evolving business speeds by infusing cloud native principles like DevOps, Infrastructure-as-code (IAC) and so on. Ease of integration of APIs with channel front-end layers.

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Enabling generative AI self-service using Amazon Lex, Amazon Bedrock, and ServiceNow

AWS Machine Learning Blog

Application Auto Scaling is enabled on AWS Lambda to automatically scale Lambda according to user interactions. Prerequisites The following prerequisites need to be completed before building the solution. Integration of Lambda with Application Auto Scaling is beyond the scope of this post.

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Deploy Amazon SageMaker pipelines using AWS Controllers for Kubernetes

AWS Machine Learning Blog

DevOps engineers often use Kubernetes to manage and scale ML applications, but before an ML model is available, it must be trained and evaluated and, if the quality of the obtained model is satisfactory, uploaded to a model registry. SageMaker simplifies the process of managing dependencies, container images, auto scaling, and monitoring.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning Blog

Data science and DevOps teams may face challenges managing these isolated tool stacks and systems. AWS also helps data science and DevOps teams to collaborate and streamlines the overall model lifecycle process. The suite of services can be used to support the complete model lifecycle including monitoring and retraining ML models.