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Data Accessibility: A Hurdle Before SAP's AI Integration

Unlocking the power of AI within SAP for your team requires overcoming a significant hurdle: data accessibility. SAP data’s complexity, spread across various modules, creates silos of information that your team might struggle to understand and utilize effectively. Inaccessible or misaligned SAP data will hinder your AI system’s ability to learn and deliver valuable results specific to your organization.

Data Prep for AI: Get Your Oracle House in Order

Despite the transformative potential of AI, a large number of finance teams are hesitating, waiting for this emerging technology to mature before investing. According to a recent Gartner report, a staggering 61% of finance organizations haven’t yet adopted AI. Finance has always been considered risk averse, so it is perhaps unsurprising to see that AI adoption in finance significantly lags other departments.

Impact of AI on #SoftwareTesting: Are Testers Ready? | #QonfX 2024

Join industry experts Rahul Verma, Navin Nair, Nagabhushan Ramappa, and our amazing host Balaji Ponnada in an insightful panel discussion on "AI's Impact on Testing, Tester Roles, and Tester Readiness." In this session, the panelists discuss how artificial intelligence (AI) has revolutionized software testing, sharing the complexities and opportunities of AI-driven testing environments. Through real-world examples and interactive discussion, they explore the changing role of testers in the AI era and provide valuable insights into the future of software testing.

Introducing Choreo Copilot

We're excited to introduce Choreo Copilot (preview), which allows you to interact with Choreo. You can pose questions in natural language and Copilot will provide answers. Choreo Copilot enables you to grasp Choreo concepts, teaches you how to perform tasks in Choreo, and provides guidance when you encounter obstacles. Copilot is familiar with APIs in Choreo’s internal marketplace. Choreo already features an AI capability that enables API testing through natural language.

A Software Engineer's Tips and Tricks #3: CPU Utilization Is Not Always What It Seems

Hey there! We're back for our third edition of Tips and Tricks. As we said in our first posts on Drizzle ORM and Template Databases in PostgreSQL, our new Tips and Tricks mini blog series is going to share some helpful insights and cool tech that we've stumbled upon while working on technical stuff. Today's topic is short and sweet. It'll be on CPU utilization and what that metric indicates. If you enjoy it and want to learn more, I encourage you to check out the "further reading" links.

Artificial Intelligence vs. Intelligent Automation: What's the Difference?

AI injects “intelligence” into automation, enabling systems to execute tasks, comprehend complex data, make informed decisions, and learn from outcomes. Unlike technologies such as robotic process automation (RPA), which follow predetermined rules, AI leverages data to evaluate situations and determine the best course of action. Now that we've explored how AI augments traditional automation tools, let's delve deeper into the realm of intelligent automation.

Get Your AI to Production Faster: Accelerators For ML Projects

One of the worst-kept secrets among data scientists and AI engineers is that no one starts a new project from scratch. In the age of information there are thousands of examples available when starting a new project. As a result, data scientists will often begin a project by developing an understanding of the data and the problem space and will then go out and find an example that is closest to what they are trying to accomplish.