AI Frameworks For Software Development

Here are the AWS services you need for AI/ML This simplified guide to help you understand how each AWS tool fits into the AI/ML lifecycle: 1. 🔸Data Collection & Storage Store raw or processed data using services like S3, RDS, Redshift, Glue, and real-time streaming with Kinesis. 2. 🔸Data Preparation Use Glue DataBrew and Data Wrangler to clean, transform, and shape datasets for training without heavy coding. 3. 🔸Model Building Use Studio, Notebooks, and Deep Learning AMIs to build and experiment with ML models efficiently and securely. 4. 🔸Model Training Train models at scale with SageMaker Training Jobs and track progress using SageMaker Experiments. 5. 🔸Model Evaluation & Optimization Debug and monitor model performance with SageMaker Debugger and tune hyperparameters using Automatic Model Tuning. 6. 🔸Model Deployment & Inference Deploy models at scale using Hosting Services, Batch Transform, or Multi-Model Endpoints for various use cases. 7. 🔸ML Ops & Pipelines Orchestrate your ML workflows using Pipelines, Step Functions, and EventBridge for smooth automation and monitoring. 8. 🔸AI Services (Pre-trained & Serverless) Tap into powerful AI APIs like Rekognition, Comprehend, Polly, and Translate without needing to train models yourself. 9. 🔸Security & Governance Protect and monitor your AI workloads using IAM, CloudTrail, Macie, and SageMaker Model Monitor. 10. 🔸Edge AI & Specialized Hardware Deploy ML models to edge devices using Inferentia, Trainium, and SageMaker Edge for real-time, low-latency inference. AWS offers a complete stack: collect, prepare, build, train, deploy, monitor, and scale, all in one place. Which services do you leverage? #genai #artificialintelligence

Have you observed lately that many agentic AI applications fail because they rely directly on raw LLM calls without a gateway to handle context routing, model orchestration, caching, rate-limiting, and fallback strategies? You must need an LLM gateway or a layer of such kind that acts as a middleware layer that sits between your application and multiple LLM providers. Hence, an LLM gateway is essential for building scalable, safe, and cost-effective agentic AI applications in the enterprise. An LLM gateway essentially functions as a central control panel to orchestrate workloads across models, agents, and MCP servers (the emerging protocol connecting AI agents to external services). Core functions and concepts of an LLM gateway include: ➤ Unified Entry Point: It provides a single, consistent interface (API) for applications to interact with multiple foundational model providers. ➤ Abstraction Layer: It hides the complexity and provider-specific quirks of working directly with individual LLM APIs. This means developers can use the same code structure regardless of which model they call. ➤ Traffic Controller: It intelligently routes requests to the most suitable LLM based on specific criteria like performance, cost, or policy. ➤ Orchestration Platform: It improves the deployment and management of LLMs in production environments by handling security, authentication, and model updates from a single platform. LLM gateways are becoming essential, particularly for enterprises building production-ready and scalable agentic AI applications, because they address multidimensional challenges related to vendor lock-in, complexity, costs, security, and reliability. Know more about LLM gateways through below resources: https://lnkd.in/gimgJ4hD https://lnkd.in/gawvkzGw https://lnkd.in/g-377ESP

If you’re an AI engineer, product builder, or researcher- understanding how to specialize LLMs for domain-specific tasks is no longer optional. As foundation models grow more capable, the real differentiator will be: how well can you tailor them to your domain, use case, or user? Here’s a comprehensive breakdown of the 3-tiered landscape of Domain Specialization of LLMs. 1️⃣ External Augmentation (Black Box) No changes to the model weights, just enhancing what the model sees or does. → Domain Knowledge Augmentation Explicit: Feeding domain-rich documents (e.g. PDFs, policies, manuals) through RAG pipelines. Implicit: Allowing the LLM to infer domain norms from previous corpora without direct supervision. → Domain Tool Augmentation LLMs call tools: Use function calling or MCP to let LLMs fetch real-time domain data (e.g. stock prices, medical info). LLMs embodied in tools: Think of copilots embedded within design, coding, or analytics tools. Here, LLMs become a domain-native interface. 2️⃣ Prompt Crafting (Grey Box) We don’t change the model, but we engineer how we interact with it. → Discrete Prompting Zero-shot: The model generates without seeing examples. Few-shot: Handpicked examples are given inline. → Continuous Prompting Task-dependent: Prompts optimized per task (e.g. summarization vs. classification). Instance-dependent: Prompts tuned per input using techniques like Prefix-tuning or in-context gradient descent. 3️⃣ Model Fine-tuning (White Box) This is where the real domain injection happens, modifying weights. → Adapter-based Fine-tuning Neutral Adapters: Plug-in layers trained separately to inject new knowledge. Low-Rank Adapters (LoRA): Efficient parameter updates with minimal compute cost. Integrated Frameworks: Architectures that support multiple adapters across tasks and domains. → Task-oriented Fine-tuning Instruction-based: Datasets like FLAN or Self-Instruct used to tune the model for task following. Partial Knowledge Update: Selective weight updates focused on new domain knowledge without catastrophic forgetting. My two cents as someone building AI tools and advising enterprises: 🫰 Choosing the right specialization method isn’t just about performance, it’s about control, cost, and context. 🫰 If you’re in high-risk or regulated industries, white-box fine-tuning gives you interpretability and auditability. 🫰 If you’re shipping fast or dealing with changing data, black-box RAG and tool-augmentation might be more agile. 🫰 And if you’re stuck in between? Prompt engineering can give you 80% of the result with 20% of the effort. Save this for later if you’re designing domain-aware AI systems. Follow me (Aishwarya Srinivasan) for more AI insights!

An insightful whitepaper from AWS explores the '6 Key Guidelines for Building Secure and Reliable Generative AI Applications on Amazon Web Services (AWS) Bedrock.' 🛡️🤖 Building generative AI applications requires thoughtful planning and careful execution to achieve optimal performance, strong security, and alignment with responsible AI principles. Key takeaways from the whitepaper: 1️⃣ Choose the right model for your specific use case to ensure effectiveness. 2️⃣ Customize models with your data and import your own models for tailored solutions. 3️⃣ Enhance accuracy by grounding foundation models with retrieval systems. 4️⃣ Integrate external systems and data sources to create powerful AI agents. 5️⃣ Ensure responsible AI practices by safeguarding foundation model responses. 6️⃣ Strengthen security and protect privacy in applications powered by foundation models. This whitepaper is a must-read for anyone building the future of AI applications. 💡 Add your thoughts in the comments—how are you incorporating security and reliability into your AI projects? ---------------------- Sarveshwaran Rajagopal #GenerativeAI #AmazonBedrock #AIApplications #ResponsibleAI

AI development comes with real challenges. Here's a practical overview of three ways AWS AI infrastructure solves common problems developers face when scaling AI projects: accelerating innovation, enhancing security, and optimizing performance. Let's break down the key tools for each: 1️⃣ Accelerate Development with Sustainable Capabilities: • Amazon SageMaker: Build, train, and deploy ML models at scale • Amazon EKS: Run distributed training on GPU-powered instances, deploy with Kubeflow • EC2 Instances:   - Trn1: High-performance, cost-effective for deep learning and generative AI training   - Inf1: Optimized for deep learning inference   - P5: Highest performance GPU-based instances for deep learning and HPC   - G5: High-performance for graphics-intensive ML inference • Capacity Blocks: Reserve GPU instances in EC2 UltraClusters for ML workloads • AWS Neuron: Optimize ML on AWS Trainium and AWS Inferentia 2️⃣ Enhance Security: • AWS Nitro System: Hardware-enhanced security and performance • Nitro Enclaves: Create additional isolation for highly sensitive data • KMS: Create, manage, and control cryptographic keys across your applications 3️⃣ Optimize Performance: • Networking:   - Elastic Fabric Adapter: Ultra-fast networking for distributed AI/ML workloads   - Direct Connect: Create private connections with advanced encryption options   - EC2 UltraClusters: Scale to thousands of GPUs or purpose-built ML accelerators • Storage:   - FSx for Lustre: High-throughput, low-latency file storage   - S3: Retrieve any amount of data with industry-leading scalability and performance   - S3 Express One Zone: High-performance storage ideal for ML inference Want to dive deeper into AI infrastructure? Check out 🔗 https://lnkd.in/erKgAv39 You'll find resources to help you choose the right cloud services for your AI/ML projects, plus opportunities to gain hands-on experience with Amazon SageMaker. What AI challenges are you tackling in your projects? Share your experiences in the comments! 📍 save + share! 👩🏻💻 follow me (Brooke Jamieson) for the latest AWS + AI tips 🏷️  Amazon Web Services (AWS), AWS AI, AWS Developers #AI #AWS #Infrastructure #CloudComputing #LIVideo

"Building #AgenticAI with #AmazonBedrock AgentCore and #DataStreaming Using Apache Kafka and Flink" At #AWS Summit New York 2025, Amazon launched Bedrock #AgentCore—a secure, scalable platform to build and operate enterprise-grade Agentic AI systems. But here’s the key insight: LLMs and orchestration tools are only half the story. To truly observe, reason, and act in real time, agents need an event-driven architecture. That’s where #ApacheKafka and #ApacheFlink become essential. Agentic AI is not about synchronous API calls. It’s about autonomous, always-on software that continuously listens to business events and triggers the right action at the right time—across domains like fraud detection, personalization, supply chain, and IT ops. Kafka provides the real-time event backbone. Flink adds continuous intelligence and stateful processing. With support for open protocols like #MCP (Model Context Protocol) and #A2A (Agent-to-Agent), this architecture enables scalable, collaborative agents that can span tools, teams, and clouds. If you’re building #autonomous agents that actually run in production, you can’t afford to ignore the streaming layer. How is your organization preparing its architecture to support long-running, autonomous AI agents at scale? Learn more in my latest blog post: https://lnkd.in/etRJGNsV

This is how Adyen built an LLM-based ticket routing + support agent copilot to increase the speed of their support team. - Adyen used LangChain as the primary framework. The entire setup runs on Kubernetes for flexibility and scalability. - First, the ticket routing system uses an LLM to automatically direct support tickets to the right agents based on content analysis. This improved the accuracy in ticket allocation compared to their human operators. - For the support agent copilot, Adyen built a document management and retrieval system. It uses vector search to retrieves relevant docs from their internal support documentation and suggests answers to support agents, which cuts down the response time significantly. - The architecture is modular so their existing microservices are integrated easily too. Link to article: https://lnkd.in/gqUZZ6nd #AI #RAG #LLMs

Designing #AI applications and integrations requires careful architectural consideration. Similar to building robust and scalable distributed systems, where principles like abstraction and decoupling are important to manage dependencies on external services or microservices, integrating AI capabilities demands a similar approach. If you're building features powered by a single LLM or orchestrating complex AI agents, a critical design principle is key: Abstract your AI implementation! ⚠️ The problem: Coupling your core application logic directly to a specific AI model endpoint, a particular agent framework or a sequence of AI calls can create significant difficulties down the line, similar to the challenges of tightly coupled distributed systems: ✴️ Complexity: Your application logic gets coupled with the specifics of how the AI task is performed. ✴️ Performance: Swapping for a faster model or optimizing an agentic workflow becomes difficult. ✴️ Governance: Adapting to new data handling rules or model requirements involves widespread code changes across tightly coupled components. ✴️ Innovation: Integrating newer, better models or more sophisticated agentic techniques requires costly refactoring, limiting your ability to leverage advancements. 💠 The Solution? Design an AI Abstraction Layer. Build an interface (or a proxy) between your core application and the specific AI capability it needs. This layer exposes abstract functions and handles the underlying implementation details – whether that's calling a specific LLM API, running a multi-step agent, or interacting with a fine-tuned model. This "abstract the AI" approach provides crucial flexibility, much like abstracting external services in a distributed system: ✳️ Swap underlying models or agent architectures easily without impacting core logic. ✳️ Integrate performance optimizations within the AI layer. ✳️ Adapt quickly to evolving policy and compliance needs. ✳️ Accelerate innovation by plugging in new AI advancements seamlessly behind the stable interface. Designing for abstraction ensures your AI applications are not just functional today, but also resilient, adaptable and easier to evolve in the face of rapidly changing AI technology and requirements. Are you incorporating these distributed systems design principles into your AI architecture❓ #AI #GenAI #AIAgents #SoftwareArchitecture #TechStrategy #AIDevelopment #MachineLearning #DistributedSystems #Innovation #AbstractionLayer AI Accelerator Institute AI Realized AI Makerspace

We've been diving deep into Amazon Bedrock over the past couple of months, exploring the fascinating capabilities it unlocks for our customers. 💭 Some of you may remember my skepticism when the preview was announced in April of this year… Well, I’m happy to report that I was wrong. After getting hands-on-keyboard with the service over the last two months, I now firmly believe this service propels AWS ahead of the curve and paves the way for the democratization of GenAI. Bedrock gives builders unfettered access to LLMs from multiple providers through a consistent API that’s deeply integrated with AWS. 🚀     🌟 My favorite features?   ✅ Speed. We’ve been getting about 20 tokens per second on Claude V2, not accounting for network latency. On Claude Instant, we’ve seen 100s of tokens per second. ✅ Scale. Despite taxing the service pretty aggressively, we have yet to hit any rate limits. ✅ Growing set of model options. So far at Caylent, we’ve been working with Antrhopic’s Claude V2, Stability’s Stable Diffusion XL v2, and AI21’s Jurassic-2. Last Wednesday, AWS announced the addition of Cohere’s Command model, which I can’t wait to try. ✅ Privacy. Your data is never used to retrain the models for other customers. No inference request’s input or output is used to train any model. Model deployments are inside an AWS account owned and operated by the Bedrock service team. Model vendors have no access to customer data. ✅ Security. You can customize the FMs privately and retain control over how your data is used and encrypted. Your data, prompts, and responses are all encrypted in transit (TLS 1.2) and at rest with AES-256 KMS keys. You can use PrivateLink to connect Bedrock to your VPCs. Your data never leaves the region you’re using Bedrock in. IAM integration enables RBAC, ABAC, and resource-based policies that allow your organization to customize access based on your organizational policies. ✅AWS Integration. For existing AWS customers, the deep integration of Bedrock into tooling like CloudWatch, CloudTrail, and IAM means Bedrock is production ready as soon as it’s generally available.   💼 We’ve given 100+ demos of Bedrock over the last 60 days, and it’s thrilling to see customers start to move beyond experimentation and into production. All of these demos and customer conversations led to the creation of our Generative AI Knowledge Base Catalyst that connects Amazon Bedrock with Amazon Kendra to deliver bespoke enterprise scale retrieval augmented generation capabilities to any AWS Customer. This is already powering our internal knowledge base at Caylent and even providing weekly summaries of updates. 🔜 What's next on the horizon? I'm eagerly awaiting access to Bedrock's game-changing feature, Agents.   💡With all the above, it's no wonder we're thrilled to help customers #MoveToBedrock and #BuildOnBedrock.   #GenAI #AWS #AWSBedrock

TL;DR: For building Enterprise #genai applications consider doing RAG WITH Fine-tuning to improve performance, lower cost and reduce hallucinations There are two common application engineering patterns to building GenAI applications: RAG and LLM Fine-tuning RAG: This involves an unmodified LLM but using various semantic retrieval techniques (like ANN) and then providing that as context to an LLM to help the LLM generate a response. How to RAG in Amazon Web Services (AWS): (https://lnkd.in/eZC3FH_p) Pros: -- Easy to get started -- Hallucinations can be reduced by a lot -- Will always get the freshest data Cons: -- Slower as multiple hops are needed -- If using commercial LLM, more tokens are passed around and that means more $$$ Fine-tuning: This involves updating an LLM (weights etc) with enterprise data more commonly now using techniques like PEFT How to Fine-tune in Amazon Web Services (AWS): https://lnkd.in/eRDg9X5M) Pros: -- Higher performance both latency and accuracy wise -- Lower cost as the number of tokens passed into LLMs can be reduced significantly Cons -- Even with PEFT, fine-tune is a non trivial task and costs $$ -- Hallucinations will still happen Based on what we see with customers they want to get the best of both worlds. Do RAG with a fine-tuned LLM How: Start by fine-tuning an LLM with enterprise "reference" data. This is data that does not change frequently or at all. This could also be data that you want to be consistent, like a brand voice. Then use that fine-tuned model as the base for your RAG. For the retrieval part you store your "fast-moving" data for semantic searches. This way you lower costs (fewer token costs), improve latency and potentially accuracy (as model is updated with your data) and reduce hallucinations (via RAG and Prompt Eng). To unlock all this effectively you really need a solid data strategy. More on that in future posts.