Getty Images

AWS AI for analytics partner tool simplifies model building

AWS' new AIDA program helps close the tech skill gap, the tech giant said. Enterprises can use languages like SQL to embed ML software tools using Amazon SageMaker Autopilot.

AWS is out with a new tool aimed at helping enterprises use AI and machine learning to gain better insights from data in concert with interfaces and integrations from AWS vendor partners.

The tech giant at its AWS re:Invent conference on Nov. 29 introduced AI for Data Analytics Partner Solutions (AIDA). The new offering enables business experts with varying levels of data science knowledge and experience to build ML models for business applications.

The partners

The partners are independent software vendors and system integrators with databases, analytics and business intelligence platforms and data warehouses that employ specialized intelligence to focus on business processes across multiple vertical industries, said Tracy Woo, an analyst at Forrester.

Partners include Amplitude, Anaplan, Causality Link, Domo, Exasol, Interworks, Pegasystems, Provectus, Qlik, Snowflake, Tableau, Tibco and Workato.

"[AIDA is] lowering the barrier to entry for business users or non-AI trained experts to use more commonplace languages like SQL to embed AI software solutions into the AWS environment using Amazon SageMaker Autopilot," Woo said.

The Autopilot tool is the automated machine learning component of AWS' Sagemaker suite of AI tools.

Challenges for business experts

According to AWS, enterprises often face challenges when trying to build ML-driven analytics applications for business users.

This task requires deep experience building and maintaining specific machine learning models, as well as collaboration between data scientists with Python knowledge, business experts with Excel and SQL knowledge, and those with workflow and data visualization tools knowledge.

Because of these requirements, predictive analytics projects often are limited in scale or hard to implement, especially for organizations without enough resources and talent.

Screenshot of AI for data analytics from AWS announcement page.
AIDA helps businesses easily embed AI software tools using AWS SageMaker.

The new AWS offering aims to bridge the tech skill gap that most enterprises are facing, especially with AI and cloud technology, Woo said.

As cloud and AI are reaching mainstream adoption scale, many organizations are finding it difficult to compete or hire the right tech talent. Since the AIDA system uses GUI-based or more common coding language control, enterprises can be more competitive and add insight to their business processes, while not necessarily having to rely on skilled AI or data analytics experts to do so.

[AIDA is] lowering the barrier to entry for business users or non-AI trained experts to use more commonplace languages like SQL to embed AI software solutions into the AWS environment using Amazon SageMaker Autopilot.
Tracy WooAnalyst, Forrester

Some missing parts

However, enterprises will still confront some missing parts and challenges with the AWS AI system.

The biggest omission is that the offering only uses SageMaker Autopilot, and does not include SageMaker Debugger and SageMaker Studio for model development, for example, Woo said.

"To distinguish itself as a leader in this market, [AWS] should further integrate these services into a unified offering for more seamless end-to-end support for the enterprise workflow," she continued.

Also, AIDA is not multi-cloud or hybrid cloud-based, Woo said.

Most organizations maintain some form of hybrid cloud or multi-cloud. So, while the offering is helpful for AWS customers, "many rely on multiple environments and will need this same sort of connectors for their other applications that are not in AWS," Woo said.

Also, compared to Google's AI platform, AIDA still lacks in providing support to the full AI lifecycle, she continued. She said it also lacks support for the open source community, an area Google dominates with its open sourcing of the TensorFlow machine learning framework.

Next Steps

An AWS re:Invent recap: Key services to watch

Dig Deeper on AI technologies

Business Analytics
Data Management