At nClouds, we help businesses of all sizes and industries leverage the latest technologies to drive innovation and optimize growth. One of the most exciting developments in recent years is the increased awareness, accessibility, and adoption of generative AI, which is transforming the way companies approach content creation, customer engagement, and more.
Why Use Generative AI with AWS?
- Automate and optimize cloud-based operations to enhance performance and improve both employee and user experiences
- Use natural language prompts to troubleshoot and perform operational tasks associated with platforms like Kubernetes, freeing up your team to focus on higher-level tasks
- Access the most cost-effective cloud-based infrastructure options for generative AI, helping you save money while still achieving high performance
Original Content on Demand
- Explore creative frontiers by generating new and engaging content and ideas for websites, social media, newsletters, white papers, and more
- Analyze user behavior, preferences, and contextual data to tailor service offerings and provide personalized experiences while meeting specific business needs
- Apply industry-leading generative AI models across all lines of business, including marketing, customer service, engineering, finance, and sales
Secure Foundational Models
- Access the AWS repository of foundational models and generative AI-powered applications so you can choose and scale the right infrastructure for your needs
- Analyze real-time data to detect anomalies and security incidents that could negatively affect reliability, ensuring efficient and effective resource provisioning
- Rest easy knowing everything is being run in a company-specific virtual private cloud, so your proprietary data is always encrypted and never shared thanks to the enterprise-level security AWS users have come to expect
Common AWS Generative AI Services
Because generative AI is a constantly evolving field, AWS offers a range of solutions that can be customized to meet the needs of startups and enterprises alike. These are just a few of the most highly adopted AWS services designed to help developers of any skill level at organizations of all sizes harness the power of generative AI to drive productivity and transform their offerings.
- Provides real-time suggestions trained on billions of lines of publicly available code that is customized for developers and understands multiple programming languages
- Checks code suggestions for open-source training data and flags with the repository URL
- Integrates with Amazon Elastic Compute Cloud (Amazon EC2), AWS Lambda, and Amazon Simple Storage Service (Amazon S3)
- Powers data-driven insights using interactive dashboards, paginated reports, embedded analytics, and natural language queries with generative business intelligence (BI)
- Takes traditional business intelligence tasks and transforms them into intuitive natural language experiences using Amazon Bedrock and machine learning (ML) expertise
- Integrates with Amazon Redshift, Amazon Relational Database Service (Amazon RDS), and Amazon Athena
- Provides fully customizable solutions using more than 150 popular open-source models
- Can be used to extract, process, and analyze documents, detect suspicious transactions, improve customer retention by predicting churn, and deliver personalized experiences
- Integrates with a variety of other services using its own set of low-code ML capabilities, including Amazon SageMaker Data Wrangler, SageMaker Autopilot, and SageMaker JumpStart
Four Ways to Incorporate Generative AI
According to a recent survey conducted by the IBM Institute for Business Value, more than six in 10 executives are planning to pilot or operate generative AI in some way by 2024. Because generative AI has the potential to revolutionize multiple industries, it’s important to understand its four most common applications in order to leverage it for a competitive edge.
1. Case-By-Case Use
- Third-party software that provides limited customization or control
- Generally includes a subscription-based price structure
- Will not require a technical team in most cases
- Best use cases: code and content drafting
2. Seamless Integration
- Direct integration of third-party software with your existing infrastructure through APIs.
- Varying costs based on size and scope of implementation.
- May require a technical team
- Best use cases: data processing, analysis, and extraction
3. Data-Enriched Integration
- Direct integration of third-party software with access to internal or proprietary data
- Varying costs based on size and scope of implementation
- Will require a technical team in most cases
- Best use cases: customer service, marketing, reporting, and support.
4. Data-Trained Integration
- Direct integration of third-party software trained using internal or proprietary data
- Requires a substantial amount of reliable, authenticated training data in order to be accurate and effective
- Will require a technical team
- Best use cases: specialized skills, industries, and domains.
nClouds can help!
At nClouds, we understand the importance of identifying real opportunities for your business and balancing the rewards of generative AI implementation against the associated risks.
Our advisory team can work with you to discuss a framework for identifying the right generative AI use cases, examine the possibilities for utilizing and harnessing generative AI, and help identify and balance risks associated with its introduction.
We can also ensure that the foundation of data, which is key to AI success, is ready. By partnering with nClouds, you can be confident in creating business value by applying generative AI in a responsible and trusted manner while mitigating the potential risks.
Whether you’re just getting started or want to scale your existing generative AI infrastructure, nClouds can help you choose the right use case for your needs and outline best practices backed by the most cost-effective and secure technology available.