Systems | Development | Analytics | API | Testing

AI

What is AI Analytics?

Imagine your software transforming from merely a tool into a strategic partner that can automatically alert your users to trends, provide explanations of data with a click, and help you ask the right questions of your data-sets - in addition to delivering data-led insights. This is the power of AI analytics solutions for independent software vendors (ISV). Today's users demand more than just functionality. They crave intelligent software that analyzes data, surfaces insights, and empowers them to act.

Top 12 Benefits of Using AI in Oracle Fusion Financials Module Testing

Artificial intelligence (AI) is rapidly transforming the business world, and Application testing is no exception. AI-powered testing tools can help businesses to automate tasks, improve accuracy, and reduce costs. One of the most important benefits of using AI for Application testing is that it can help automate repetitive tasks. This can free up testers to focus on more complex and strategic tasks. For example, AI can automate creating test cases, executing test cases, and analyzing test results.

AI: Friend or Foe of a Tester? | Rahul Verma | #softwaretesting #automationtesting #shorts

Dive into a candid discussion about the intersection of AI and job security in the testing industry. Rahul Verma fearlessly addresses the hard truths surrounding job loss and the role of AI, debunking common misconceptions and shedding light on the real challenges ahead. 🔍 Key Takeaways: Prepare yourself for the realities of AI in testing and equip yourself with the knowledge needed to thrive in an ever-changing industry. Tune in to this eye-opening session and take the first step towards securing your future in testing. #JobSecurity #TechIndustry #TheTestTribe #TechTalks.

6 Ways Marketers Are Using Generative AI: Is It Really Saving Time?

AI was the hot topic of 2023 and will continue to reign in 2024: ChatGPT first launched at the end of 2022 and became a massive hit in just a few months. Google released Bard shortly after, and then, new AI tools just kept popping up, prompting marketers to learn how to leverage them to become more efficient and productive.

LLMOps vs. MLOps: Understanding the Differences

Data engineers, data scientists and other data professional leaders have been racing to implement gen AI into their engineering efforts. But a successful deployment of LLMs has to go beyond prototyping, which is where LLMOps comes into play. LLMOps is MLOps for LLMs. It’s about ensuring rapid, streamlined, automated and ethical deployment of LLMs to production. This blog post delves into the concepts of LLMOps and MLOps, explaining how and when to use each one.

Top 3 Data + AI Predictions for Manufacturing in 2024

Investment in AI for manufacturing is expected to grow by 57% by 2026. That’s hardly surprising — with AI’s ability to augment worker productivity, improve efficiency and drive innovation, its potential in manufacturing is vast. AI’s predictive capabilities can help manufacturing leaders anticipate market trends and make data-driven decisions, creating financial opportunities for suppliers as well as customers.

Optimizing Quality Assurance: Harnessing the Power of AI for Efficient and Effective Software Testing

In the present digital period, Artificial Intelligence (AI) is impacting the future of various aspects of Quality Assurance (QA). This evolution has resulted in strategies for ensuring quality is effectively integrated into development processes.