PoC support service for AI implementation using Amazon SageMaker

While the use of AI is attracting attention in many companies, high barriers to actual implementation still exist. In particular, companies must design and build their own configurations that link multiple services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon DataZone, and Amazon Bedrock, which poses technical hurdles and a heavy initial deployment burden. The technical hurdles and the large initial implementation burden have become bottlenecks. In addition, many companies are still hesitating in the planning stage, wondering where to start and which use cases will be effective, and many are stalling before introducing AI.

Under these circumstances, companies should address the following issues as the first step in AI implementation. To build an AI environment that integrates various AWS services in a short period of time in a safe and secure manner. Establish use cases that match the company's business and data, and verify the feasibility and effectiveness of the use cases. Establish an all-in-one verification process including environment construction, model creation, and evaluation. Establish a system to collaborate with specialized partners so that verification can be conducted even without sufficient in-house AI human resources.

GEIL's “AI implementation PoC support service using Amazon SageMaker” provides a quick PoC environment for next-generation integrated analysis and AI infrastructure linked with Glue, Redshift, Athena, Bedrock, DataZone, etc., by using Amazon SageMaker. We can quickly build and provide an environment for the next generation of integrated analytics and AI infrastructure. This integrated platform enables one-stop implementation from data preparation to modeling and AI validation. In addition, we can also propose scaling strategies and improvement measures for production use after PoC implementation, including the development of a plan for effective PoC implementation by identifying current business issues and setting evaluation indicators.

<Concrete steps Hearing and establishment of evaluation indices 　Analyze business issues and the current environment, and establish PoC target use cases and evaluation indices. PoC environment construction 　Build a verification environment integrating various AWS services (Glue, S3, Redshift, Athena, Bedrock, etc.) using SageMaker. 3. Model development and AI verification 　Conduct model design and evaluation using business data, and visualize verification results 4. Results review and proposal 　Analyze PoC results and propose configuration improvement and scaling strategies for production implementation

<Example of deliverables PoC plan PoC result report System configuration diagram Proposal for production environment construction

<Reasons for choosing J.L. Consulting More than 30 years of experience in supporting data utilization and analysis 　Extensive support experience and industry understanding in the BI/DWH/AI field Expert team of more than 100 AWS certified professionals 　Support for all aspects of cloud infrastructure, AI development, and data integration Full support from PoC to production deployment 　Continuous support for improvement and expansion after PoC Deep knowledge of each service that can be integrated with SageMaker 　Optimal integration design using Glue, Redshift, Bedrock, DataZone, etc. 　 <Target Users Companies that are in the planning stage of AI implementation and wish to start with technical validation Companies that have already built or are in the process of building a data infrastructure on AWS and want to accelerate the use of AI. Organizations that lack the knowledge and resources necessary for PoC and are seeking external professional support. Planning and DX departments that want to trial use cases of generative AI and business automation together with business departments.