AWS ReInvents cloud service offerings with natural language querying and GenAI

Generative AI is going to pretty much change applications inside companies and every application consumers interact with.

– Adam Selipsky, CEO, AWS

The bread and butter of any software engineer is writing code, which forms the bedrock of applications used across enterprises. The number of applications used in companies is humongous, and each application runs on millions of lines of code; for example, standard SAP has 238 million lines of code. Typically, writing code to build models requires in-depth knowledge of languages and the manual input of numerous lines of code. Troubleshooting requires analyzing thousands of lines of code when there is an error.

The advent of generative artificial intelligence (GenAI) promises to automate the grunt work involved in writing and analyzing code by providing inputs in the form of natural language. Using natural language as an input to auto-generate code will save software engineers time and help them focus their attention on more creative tasks. The resultant productivity boost means engineers will need to spend less time troubleshooting and can spend more time creating new products. It will also make tools easier for those not conversant with coding knowledge.

At ReInvent 2023, Amazon Web Services announced numerous initiatives involving the integration of natural language querying in its solutions.

AWS launches GenAI-powered natural language query generation

Whenever an application or website goes down, an alert is triggered in a monitoring tool, bringing the issue to the attention of the monitoring team. The DevOps engineer then looks at the issue, errors, and issue-related patterns and looks for a solution. Finding a solution requires the DevOps engineer to have a deep understanding of coding, use intuition gained from other issues they have experience with, and analyze tons of data as quickly as possible. In short, it requires skilled personnel working in a high-pressure environment with limited time.

The advent of GenAI could upend troubleshooting. GenAI-based tools can change and ease the process by enabling the engineer to provide queries in simple English, avoiding the need for knowing prompts for numerous codes and languages, analyzing the log data, and identifying patterns that pinpoint the core issue causing the error. The engineer could then swiftly use the input to remedy the problem quickly. Using GenAI in troubleshooting will free up time that can be used in more creative pursuits.

Right on cue, Amazon CloudWatch announced (in preview) the launch of natural language query generation powered by generative AI for logs insights and metrics insights. The feature enables users to generate queries using inputs in English, accelerating insights from data without needing extensive knowledge of the query language.

The solution has three main capabilities:

• It generates queries from natural language questions.
• It provides line-by-line query explanations to help the user learn the query syntax.
• It helps refine existing queries using natural-language instructions for guided query iteration.

AWS introduces the ability to use natural-language instructions in Amazon SageMaker Canvas

SageMaker Canvas is a no-code interface that creates machine-learning models with a drag-and-drop visual interface. It provides access to ready-to-use models and the option to build custom ML models.

AWS has introduced the use of natural language instructions to explore and transform data sets, which will help users get actionable insights quicker, as the most time-consuming aspect is typically building the machine learning model. Building charts, selecting the right data sets, applying filters, and visualizing data can now be done through natural language without writing a single line of code.

GenAI will help write queries in Amazon Redshift Query Editor

Amazon Redshift is a fast, fully managed cloud data warehousing solution from Amazon Web Services. It can handle large volumes of data at speed and large-scale data migrations. The tool can be used for business intelligence, log analysis, real-time analytics, combining multiple data sources, and more.

AWS announced the launch of Amazon Q generative SQL in Amazon Redshift Query Editor, its web-based SQL editor. Users need to describe the information they wish to extract from data in natural language, and the editor comes up with suggested SQL queries.

The Bottom Line: A productivity boost and the democratization of tools are on the horizon.

Since the advent of GenAI, enterprises have identified key areas where implementations will unleash a productivity boost. Using natural language in querying and code generation promises to be one of the areas ripe for implementation. It will save software engineers time that they can spend on product innovation instead of troubleshooting. Natural language querying makes tools more user-friendly for non-technical users. With the use of GenAI and natural language querying, the focus will be on the business impact of the data analysis rather than the process of analyzing data, which will democratize the use of platforms and make them easy to use for a significant number of users.