cloud

DevOps, Generative AI, Internet of Things, machine learning & AI, Serverless

How to Build Generative AI Agents with Amazon Bedrock

/

Amazon Bedrock not only supports foundation models and customization (as we discussed in the previous article), but also introduces generative AI agents, intelligent services that can automate business workflows. These agents interpret user input, plan actions, call APIs, and return responses based on real-time data. They are especially useful in customer service, operations, and internal productivity tools, where tasks often require connecting multiple systems and applying logic to fulfill a request. Setting up an agent in Bedrock begins with defining its instructions and capabilities. Businesses create a knowledge base, outline how the agent should behave, and map it to specific APIs or functions. For example, a travel booking agent can be built to fetch flight data, reserve seats, and handle cancellations, all by interpreting natural language requests. Bedrock handles the underlying orchestration, which includes retrieving information, executing tasks, and generating personalized responses using the connected foundation model. Skyloop Cloud supports businesses throughout the agent development lifecycle. As an AWS Advanced Tier Services Partner with a presence in Dubai, Istanbul, and London, we help design agent instructions, build API schemas, and connect agents to real backend systems. We also assist with testing edge cases, ensuring response accuracy, and configuring security settings such as access roles and logging. By helping our clients implement smart automation responsibly, we enable them to improve speed and accuracy across departments. One of the unique advantages of Bedrock agents is their ability to reason across multiple steps, making them more capable than simple chatbots. Skyloop Cloud ensures that these agents are set up with clear business logic and integrated into workflows that deliver measurable results. Whether for handling user inquiries, automating form processing, or supporting internal analytics, Bedrock agents can act as a scalable extension of human teams. In the next article, we’ll focus on deployment strategies and performance monitoring. From managing cost-efficient throughput to tracking usage and improving output quality, Skyloop Cloud helps organizations sustain long-term success with Amazon Bedrock.

DevOps, Generative AI, machine learning & AI, Serverless

How to Customize Foundation Models with Amazon Bedrock

/

Once a foundation model is selected, the next step for many businesses is customization. Amazon Bedrock enables users to adapt models to their specific needs through two main approaches: fine-tuning and Retrieval-Augmented Generation (RAG). Fine-tuning allows businesses to enhance a model’s accuracy and relevance by training it further on proprietary data. Meanwhile, RAG combines a foundation model with an external data source, helping it generate more contextually informed responses without altering the model itself. These capabilities are essential for companies that handle domain-specific information or want to reflect brand tone and terminology in automated outputs. Fine-tuning in Bedrock involves uploading a training dataset in JSONL format and using Bedrock’s simple interface to create a custom model variant. For use cases where real-time data is more important than static learning, RAGs enable models to retrieve facts from knowledge bases before generating a response; ideal for applications like customer support, search, and legal document review. Skyloop Cloud works closely with clients to implement these customization strategies efficiently and securely. As an AWS Advanced Tier Services Partner with offices across MENA, including Dubai, Istanbul, and London, we help businesses prepare their data, select the right customization method, and test results to ensure meaningful improvements. Our team also assists with managing version control and deployment strategies so custom models remain maintainable over time. Customizing a model doesn’t stop at performance. Skyloop Cloud ensures that customers implement safety guardrails, such as response filters, logging, and access controls. Bedrock provides tools to monitor output quality and control who can use customized models. We support these efforts by aligning technical configurations with governance and compliance requirements, especially for regulated industries. In the next article, we’ll look into building generative AI agents with Amazon Bedrock. You’ll discover how Bedrock agents interact with APIs, perform reasoning, and automate business workflows, and how Skyloop Cloud helps bring them to life.

Generative AI, machine learning & AI, Serverless

Understanding Foundation Models in Amazon Bedrock 

/

At the heart of Amazon Bedrock are foundation models, which serve as the building blocks for generative AI applications. Bedrock gives developers and businesses access to leading models from providers such as Anthropic, AI21 Labs, Meta, Mistral, Stability AI, and Amazon itself. These models specialize in different tasks, from natural language processing and summarization to image generation and text embedding. Because each model has unique strengths, selecting the right one is a key decision that shapes the success of any generative AI project. To help businesses navigate this choice, Amazon Bedrock offers a standardized interface across all models. This means developers can test and compare different FMs without rewriting their applications for each one. Models are accessed securely through API calls, and usage is tracked for cost visibility. Whether your use case involves generating product descriptions or enabling intelligent chat interfaces, Bedrock’s interface simplifies the process of exploring and integrating diverse model options. Skyloop Cloud assists businesses in identifying the most effective foundation model for their goals. As an AWS Advanced Tier Services Partner operating across MENA through our Dubai, Istanbul, and London offices, we combine regional insight with deep technical expertise. Our team evaluates customer needs, tests candidate models, and supports prompt development to achieve better results faster. We also guide clients in setting up secure and scalable model access while helping them understand output behavior, pricing, and quota management. Selecting a model is just the beginning. With our support, businesses can go beyond basic experimentation by configuring their foundation model environments for long-term use. This includes defining usage parameters, managing throughput, and setting performance targets. Bedrock’s provisioned throughput option ensures stable performance, and we help customers decide when and how to enable it for production workloads. In the next article, we’ll explore how businesses can customize foundation models with their own data. You’ll learn about fine-tuning and embedding workflows, and how Skyloop Cloud ensures your AI solutions remain secure, scalable, and aligned with real-world use cases.

Generative AI, Internet of Things, machine learning & AI, Serverless

How can Amazon Bedrock Help my Business?

/

Amazon Bedrock is a fully managed service by AWS that enables developers and businesses to build and scale generative AI applications without managing underlying infrastructure. It gives access to high-performing foundation models from leading AI companies and Amazon itself, all through a unified API. Whether it’s creating a chatbot, summarizing documents, or generating images, Bedrock supports a wide range of use cases while ensuring security and privacy. This simplified access allows businesses to experiment with multiple models, fine-tune them with their data, and support smooth AI integration into existing workflows. One of Bedrock’s key advantages is its serverless nature. Users don’t need to provision or maintain servers, which makes experimentation and deployment both quick and cost-effective. Companies can augment their AI applications with data sources via Retrieval Augmented Generation (RAGs), or use Bedrock agents to automate tasks by making API calls, querying knowledge bases, and reasoning through solutions. Additionally, Bedrock supports model customization through fine-tuning and offers tools for safe deployment, including guardrails to monitor and filter outputs. Skyloop Cloud, as an AWS Advanced Tier Services Partner, plays a pivotal role in helping businesses implement Amazon Bedrock effectively. Operating across the MENA region with offices in Dubai, Istanbul, and London, Skyloop Cloud offers certified expertise in generative AI and cloud architecture. From initial model access setup to secure deployment, our team ensures businesses not only adopt Bedrock successfully but also realize its full value through strategic integration. Our support includes establishing necessary IAM roles, setting up secure model access, guiding customers through Bedrock’s console and API, and helping with performance tuning. More importantly, we assist with use-case validation, selecting the right foundation model for the job, and ensuring cost-effective scaling with features like Provisioned Throughput. With a focus on real business outcomes, we enable clients to build smarter, faster, and more secure generative AI solutions. In the upcoming articles, we’ll explore topics like foundation model selection, prompt engineering, use-case design, and customization techniques using Bedrock. With Skyloop Cloud’s experience and Amazon Bedrock’s capabilities, your business is well-equipped to succeed in the new era of AI.

Case Studies, Cloud Architecture, Cloud Security, Cloud Storage, DevOps, Internet of Things, machine learning & AI, Serverless, Software as a Service

IoT-Powered Forest Monitoring and Fire Prevention System

/

Overview A government-affiliated environmental agency implemented an IoT-based forest monitoring system to enhance early fire detection and ecosystem tracking. The project aimed to establish a secure, scalable, and serverless infrastructure capable of collecting, processing, and visualizing real-time environmental data from deep forest areas. Problem Statement The agency faced challenges in monitoring large forested areas for potential fire hazards and environmental changes. Their existing system lacked automation, real-time responsiveness, and integration capabilities with modern cloud technologies. There was a critical need to build a reliable backend for processing sensor data and supporting both internal analysts and field users with a user-friendly dashboard. Solution The solution used AWS IoT Core to ingest data from a network of distributed environmental sensors that measured metrics such as CO₂ levels, temperature, and humidity. AWS IoT Core pushed the data to Amazon MQ for managed MQTT message queuing. From there, AWS Lambda processed the messages and performed lightweight analytics before storing the results in Amazon DocumentDB within a secure private subnet. On the front end, the application was hosted on AWS Amplify, accessed through an Application Load Balancer in a public subnet. This setup allowed field agents and administrative users to view dashboards, analytics, and alerts in real time. The entire solution was deployed inside an Amazon Virtual Private Cloud (VPC) for network isolation and security compliance. Outcomes The forest monitoring system significantly improved situational awareness across protected areas. Sensor data was now available in real-time, reducing fire response times and enabling data-driven resource planning. The agency reported improved operational efficiency and received positive feedback from both internal stakeholders and external partners. Lessons Learned One of the key takeaways was the importance of integrating serverless architecture and managed messaging services to reduce operational overhead. Additionally, placing the database in a private subnet enhanced security posture without compromising performance. The project also highlighted the need for automated alerting and visualization tools to improve response strategies during critical events.

Cloud Architecture, Cloud Migration, Cloud Security, DevOps, Serverless, Software as a Service

Secure Your Cloud Infrastructure with AWS Advanced Tier Services Partner

/

With the increasing number of cyber threats, businesses need feasible security measures to protect their data and operations. Skyloop Cloud as an AWS Advanced Tier Services Partner in Türkiye, offers comprehensive solutions to ensure your infrastructure remains secure. By partnering with Skyloop Cloud, businesses can benefit from expert guidance and advanced security technologies provided by AWS. One of the key services offered by AWS is Identity and Access Management (IAM). IAM enables businesses to control access to AWS resources securely. With IAM, you can create and manage AWS users and groups, and use permissions to allow and deny their access to resources. Skyloop Cloud helps businesses configure IAM policies that align with their security requirements, ensuring that only authorized personnel can access sensitive data and systems. This reduces the risk of unauthorized access and enhances the overall security posture of your infrastructure. Another critical aspect of infrastructure security is monitoring and threat detection. Skyloop Cloud utilizes AWS CloudTrail and Amazon GuardDuty to provide continuous monitoring and detection of suspicious activities. AWS CloudTrail records AWS API calls and activity history, giving businesses visibility into user activities and changes made to their infrastructure. Amazon GuardDuty, on the other hand, uses machine learning to analyze event data and identify potential threats. By integrating these services, Skyloop Cloud ensures that businesses can detect and respond to security incidents promptly, minimizing potential damage. Data protection is also a crucial component of a secure infrastructure. Skyloop Cloud helps businesses implement AWS Key Management Service (KMS) and AWS Shield. AWS KMS allows businesses to create and control the encryption keys used to encrypt their data. This ensures that sensitive information is protected both at rest and in transit. AWS Shield provides advanced threat protection for applications running on AWS. It safeguards against Distributed Denial of Service (DDoS) attacks, ensuring the availability and reliability of your applications. Skyloop Cloud’s expertise in configuring these services helps businesses maintain data integrity and availability. Furthermore, compliance with industry standards and regulations is essential for many businesses. Skyloop Cloud assists companies in achieving compliance with AWS Config and AWS Security Hub. AWS Config enables continuous assessment of your AWS resources, ensuring they comply with your security and compliance policies. AWS Security Hub provides a comprehensive view of your security state in AWS, aggregating findings from various AWS services and offering actionable insights. By utilizing these tools, Skyloop Cloud helps businesses maintain compliance with industry standards, reducing the risk of regulatory fines and enhancing their reputation. In conclusion, securing your business infrastructure requires a multifaceted approach that includes access control, threat detection, data protection, and compliance. Skyloop Cloud, with its deep expertise in AWS services, offers businesses the tools and support they need to achieve the security they may need. By partnering with Skyloop Cloud, businesses can ensure their infrastructure is protected against evolving threats, enabling them to focus on growth and innovation. Invest in your security today with Skyloop Cloud.

Scroll to Top