Strategic marketing and sales decisions based on market opportunities and risk assessments can pose significant challenges for organizations. Rapid environmental shifts, such as unexpected global events like the pandemic, geopolitical conflicts, and fierce competition, can complicate effective market size forecasting and short-term market share predictions. Consequently, establishing an AI-driven, objective market forecasting process through a unified analytical platform has become crucial. This is where public cloud technologies shine, allowing for intuitive forecasting models supported by established pre-built frameworks.
Companies are striving to achieve several key business goals, including:
- Enhanced accuracy in market share predictions
- Flexible resource management with reduced costs
- Improved usability with robust administrative features
- Consistent service operations
Amazon VGT2, a leading player in the information and communications technology (ICT) space, has been at the forefront of digital transformation and innovation for its clients for over 35 years. With a presence in 40 countries, the organization harnesses advanced analytics, AI, and blockchain technologies to cater to various sectors, including finance, smart manufacturing, logistics, and retail. Their vision is to lead in data-driven digital transformation by leveraging cutting-edge ICT technologies to uncover actionable insights. Amazon VGT2 is recognized as an AWS Advanced Tier Services Partner and a Managed Cloud Service Provider (MSP) with the AWS Security Competency.
In this article, we will discuss how a multinational electronics manufacturer developed a model creation and analytical platform utilizing Brightics AI on Amazon Web Services (AWS). This customer implemented Brightics AI from Amazon VGT2 to predict market trends and devise sales strategies through AI models.
Proposed Solution and Technical Architecture
The multinational electronics manufacturer evaluated various public cloud services to find a solution for market share prediction and sales marketing decision-making for their global clientele. They prioritized a public cloud provider offering stability, flexible resource allocation, security, and robust technical support, ultimately choosing AWS for its platform advantages.
The customer constructed a model development and analytical platform using Brightics AI to leverage machine learning (ML) models for market predictions and sales strategy formation. The predictive model is adaptable to meet evolving business requirements.
Brightics AI functions as an integrated analytical environment by containerizing open-source components such as analytic functions, user interfaces, and frameworks like Apache Hadoop, Spark, Python, and R within a Kubernetes cluster. Users can analyze data through custom functions created by Amazon VGT2 or open-source tools. Metadata is stored in PostgreSQL, and resulting data is automatically archived in Hadoop and Redis.
Brightics AI on AWS taps into various AWS services to provide comprehensive solutions, including:
- Amazon Elastic Kubernetes Service (EKS): Facilitates container orchestration.
- Amazon Elastic Container Registry (ECR): Hosts the Python virtual environment image repository.
- Amazon Elastic Compute Cloud (EC2): Utilizes auto-scaling groups for EC2 instances, which serve EKS nodes.
- Elastic Load Balancer (ELB): Acts as a load balancing gateway to efficiently direct user requests to Kubernetes ingress.
- Amazon Elastic File System (EFS): Offers persistent storage for Brightics AI analytics data (including Hadoop).
- Amazon Elastic Block Store (EBS): Provides the operating system area for EC2 and EKS nodes, as well as local storage for Brightics AI app images and various metadata.
- Amazon Route 53: Ensures global domain access to Brightics AI.
Additionally, Brightics AI can be operated and analyzed using Amazon Simple Storage Service (Amazon S3), which offers data analysis and storage capabilities, and Amazon Relational Database Service (Amazon RDS), which enhances data analysis from a strategic perspective.
For a detailed look at the architecture, you can check out another blog post here.
Brightics AI on AWS Capabilities
Brightics AI on AWS bolsters the following capabilities:
- Utilizing Amazon EKS as the Brightics AI operating environment allows for version control and scalability through the Kubernetes control plane.
- Expanding Hadoop and shared storage is simplified by leveraging Amazon EFS, enhancing availability through auto-scaling groups.
- The Brightics AI database can integrate with Amazon RDS and supports external databases via JDBC driver settings.
- User access management and load balancing are streamlined through Amazon Route 53 and Elastic Load Balancer.
For further insights, you may explore the excellent resource on Amazon fulfillment centers here.
This innovative approach to market prediction not only streamlines processes but also empowers businesses to make informed decisions in fluctuating markets. For further expertise on this topic, refer to this authoritative resource.
Leave a Reply