How to Choose Components for Production Deployment

𝐌𝐲 𝟕-𝐬𝐭𝐞𝐩 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 𝐭𝐡𝐚𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐠𝐞𝐭𝐬 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐭𝐨 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧. After building 40+ Production Agentic Systems,  successful deployments follow this exact progression: 𝟏. 𝐏𝐈𝐂𝐊 𝐀𝐍 𝐋𝐋𝐌 • Choose a model that fits your task, cost, and tooling needs • Examples: Gemini, Claude • Tip: Start with hosted APIs. Fine-tune only when needed. 𝟐. 𝐂𝐎𝐍𝐍𝐄𝐂𝐓 𝐓𝐎𝐎𝐋𝐒 & 𝐀𝐏𝐈𝐒 • Let agents interact with the real world • Use cases: Databases, external services • Examples: LangChain, Zapier, Make • Tip: Define strict input/output schemas. 𝟑. 𝐃𝐄𝐅𝐈𝐍𝐄 𝐀𝐆𝐄𝐍𝐓 𝐋𝐎𝐆𝐈𝐂 • Decide how the agent thinks and acts • Common patterns: ReAct, Plan → Execute • Examples: LangChain, LlamaIndex • Tip: Begin simple. Evolve logic gradually. 𝟒. 𝐀𝐃𝐃 𝐌𝐄𝐌𝐎𝐑𝐘 (𝐒𝐇𝐎𝐑𝐓 + 𝐋𝐎𝐍𝐆) • Give agents context and continuity • Memory types: Short-term (conversation context), Long-term (user preferences, history) • Examples: LangGraph, Zep, Chroma, MemGPT • Tip: Combine chat history + vector memory. 𝟓. 𝐀𝐒𝐒𝐈𝐆𝐍 𝐀 𝐒𝐏𝐄𝐂𝐈𝐅𝐈𝐂 𝐉𝐎𝐁 • One agent → one clear responsibility • Good tasks: "Analyze feedback and suggest improvements", "Summarize support tickets" • Bad tasks: "Be helpful" • Tip: Start with hosted APIs. Fine-tune only when needed. 𝟔. 𝐁𝐔𝐈𝐋𝐃 𝐌𝐔𝐋𝐓𝐈-𝐀𝐆𝐄𝐍𝐓 𝐒𝐘𝐒𝐓𝐄𝐌𝐒 • Scale by splitting work across specialized agents • Roles: Researcher, Analyzer • Frameworks: CrewAI, LangGraph • Tip: Use coordinators to manage handoffs. 𝟕. 𝐃𝐄𝐏𝐋𝐎𝐘 & 𝐒𝐂𝐀𝐋𝐄 • Move from prototype to production safely • Infrastructure: APIs, Serverless, Microservices • Examples: AWS, Docker, Kubernetes • Tip: Start with hosted APIs. Fine-tune only when needed. 𝐖𝐡𝐚𝐭 𝐭𝐞𝐚𝐦𝐬 𝐠𝐞𝐭 𝐰𝐫𝐨𝐧𝐠: They jump to Step 6 (Multi-Agent Systems) without Mastering Steps 1-5. 𝐑𝐞𝐬𝐮𝐥𝐭:   Agents that can not maintain context, do not handle tools reliably, and fail in production. 𝐌𝐲 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧: Spend 60% of time on Steps 1-5 before attempting multi-agent systems. Single-agent mastery > Multi-agent complexity. 𝐓𝐡𝐞 𝐩𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞: Scalable agents are not built by adding more agents,  they are built by mastering each capability layer first. One reliable agent beats ten unreliable ones. 𝐖𝐡𝐢𝐜𝐡 𝐬𝐭𝐞𝐩 𝐚𝐫𝐞 𝐲𝐨𝐮 𝐜𝐮𝐫𝐫𝐞𝐧𝐭𝐥𝐲 𝐚𝐭? 𝐖𝐡𝐢𝐜𝐡 𝐨𝐧𝐞 𝐚𝐫𝐞 𝐲𝐨𝐮 𝐫𝐮𝐬𝐡𝐢𝐧𝐠 𝐩𝐚𝐬𝐭? ♻️ Repost this to help your network ➕ Follow Prashant Rathi for more insights on Enterprise AI PS. Opinions expressed are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently McKinsey & Company) or affiliates. #GenAI #EnterpriseAI #AgenticAI

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺. That’s one of the biggest misconceptions in AI today. Strong AI systems don’t succeed because of a single breakthrough model. They succeed because multiple components work together correctly, from data pipelines to deployment infrastructure. When companies struggle with AI projects, the issue is rarely the algorithm. It’s usually the system around the model. Here’s a simple way to think about the core components behind modern AI systems. 1️⃣ 𝗗𝗮𝘁𝗮 Everything starts with data. This includes: • Data collection • Cleaning and filtering • Annotation and labelling High-quality data is the fuel that powers the entire model pipeline. Poor data leads to poor models, no matter how advanced the algorithm is. 2️⃣ 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 Algorithms define how the model learns patterns. Examples include: • Deep learning models • Reinforcement learning systems These algorithms determine how knowledge is extracted from data. 3️⃣ 𝗠𝗼𝗱𝗲𝗹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 Architecture defines how the model processes information. Common architectures include: • CNNs for vision tasks • RNNs for sequential data Choosing the right architecture depends heavily on the problem being solved. 4️⃣ 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 This is where models actually learn. Key components include: • Loss functions • Hyperparameter tuning Training workflows determine how efficiently models converge. 5️⃣ 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 & 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 You cannot improve what you cannot measure. Evaluation includes: • Accuracy, precision, recall, F1 scores • ROC-AUC and benchmarking This stage ensures models behave reliably and responsibly. 6️⃣ 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 A trained model only becomes useful when it can serve predictions in production. This involves: • Scalable inference infrastructure • API integration • Dockerized deployments Production reliability is where many AI projects fail. 7️⃣ 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 & 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 AI systems are never “finished.” They require continuous improvement through: • Model monitoring • Drift detection • Retraining pipelines Without this, even strong models degrade over time. The real power of AI doesn’t come from one component. It comes from the entire system working together. At 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗚𝗿𝗼𝘂𝗻𝗱, this is exactly what we help organisations design complete AI systems that combine data pipelines, model architecture, deployment infrastructure, and continuous monitoring. Because building AI isn’t just about models. It’s about engineering reliable AI systems. Come hang out on 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗢𝗽𝘀 𝗛𝘂𝗯 𝗗𝗶𝘀𝗰𝗼𝗿𝗱: https://lnkd.in/dF6nNhK4 𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dzQpf5uQ #AIEngineering #MachineLearning #MLOps #ArtificialIntelligence #AISystems #ServicesGround

Shipping a model is easy. Keeping it alive in production is the hard part. AI deployment isn’t one pattern you “pick once.” It’s a toolbox you apply based on risk, latency, scale, and business impact. Here’s how real teams deploy AI when reliability actually matters. - Real-time inference Used when users expect instant responses. Optimized for low latency, validation, caching, and safe fallbacks. - Batch inference Runs on schedules for large datasets. Ideal for forecasting, scoring, and analytics where speed matters less than scale. - Streaming inference Handles continuous event-driven data. Powering anomaly detection, monitoring, and live signals. - Human-in-the-loop Keeps humans in control for high-risk decisions. Common in finance, healthcare, and compliance-heavy workflows. - A/B model testing Compares models on real traffic. Decisions are driven by outcomes, not offline benchmarks. - Shadow deployment Tests new models silently in production. You learn before users feel anything. - Canary releases Rolls out models gradually. Problems surface early, rollbacks stay cheap. - Multi-model routing Chooses models dynamically based on cost, complexity, or intent. Quality and spend stay balanced. - Rollback & failover Assumes failure will happen. Systems recover automatically instead of collapsing. - Containerized services Best for standardized, scalable enterprise deployments with strong operational control. - Serverless deployment Handles bursty workloads efficiently. You pay for execution, not idle time. - Edge deployment Runs models close to users or devices. Critical for low-latency or disconnected environments. - Blue–green deployment Switches traffic instantly between environments. Zero downtime during major changes. Production AI isn’t about picking the “best” model. It’s about choosing the right deployment pattern for the risk you’re taking. If your AI works in demos but feels fragile in production, the problem isn’t the model, it’s the deployment strategy. Follow Vaibhav Aggarwal For More Such AI Insights!!

Most AI deployments fail after launch. Not because of the model, but because of missing checks. Shipping AI to production is not just deployment. It is risk management at scale. 𝐈𝐧 𝐭𝐡𝐢𝐬 𝐢𝐧𝐟𝐨𝐠𝐫𝐚𝐩𝐡𝐢𝐜 𝐈 𝐛𝐫𝐞𝐚𝐤 𝐝𝐨𝐰𝐧 10 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐜𝐡𝐞𝐜𝐤𝐥𝐢𝐬𝐭 𝐢𝐭𝐞𝐦𝐬: • Model Validation • Data Integrity • API Layer • Security Controls • Infrastructure Setup • Monitoring Systems • Observability • Versioning • Testing • Documentation 𝐄𝐚𝐜𝐡 𝐢𝐭𝐞𝐦 𝐩𝐫𝐨𝐭𝐞𝐜𝐭𝐬 𝐲𝐨𝐮 𝐟𝐫𝐨𝐦 𝐚 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐟𝐚𝐢𝐥𝐮𝐫𝐞. → Model validation ensures reliability. → Data integrity prevents silent corruption. → API layer stabilizes system access. → Security controls reduce attack surface. → Infrastructure setup enables scale. → Monitoring systems catch real-time issues. → Observability gives debugging visibility. → Versioning enables safe changes. → Testing reduces production surprises. → Documentation ensures team alignment. AI deployment is not a final step. It is the start of continuous operation. The teams that treat deployment as a checklist avoid costly production incidents. P.S. Which checklist item is most often skipped in your deployments? Follow Antrixsh Gupta for more insights

⚡ PCB Design Fundamentals Day 3: Component Selection Strategy Component selection is about electronics specifications AND about future-proofing your design. Three considerations that prevent costly revisions: 1. Availability across multiple vendors (single-source components are design time bombs) 2. Thermal characteristics under worst-case conditions 3. Common footprint alternatives when primary choice becomes unavailable I learned this lesson painfully when a specialized connector which suddenly got bought out the next day and lead time was 26 weeks. This was mid-production of our boards. This forced me to select a surface-mount version for that part that only that manufacturer made and to change the footprint, resend all boards and documentation to the manufacturer, and so on. The result was a 3-week wait...and risked millions in funding, pushing things too close to the deadline. Now whenever I have an ECAD tool, I look for how well it helps me with BOM risk management, with pre-vetted alternatives for critical parts. Some software have their own tools for this, like the Altium BOM Management tool in Altium. What's your strategy for managing component supply chain risks? #ComponentSelection #SupplyChainStrategy #DesignResilience

𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐚𝐫𝐞 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬, 𝐭𝐡𝐞𝐲 𝐚𝐫𝐞 𝐚 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐞𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦. In my experience working on large-scale, enterprise platforms, microservices only succeed when the surrounding ecosystem is designed just as thoughtfully as the services themselves. It’s never just about writing Service A or Service B. It’s about building a production-ready system that supports scalability, resilience, security, and observability from day one. Here are the core components I’ve consistently seen as essential in real-world deployments: API Gateway – A centralized entry point to manage routing, rate limiting, security enforcement, and cross-cutting concerns. Service Registry – Critical for dynamic service discovery and maintaining loose coupling across distributed environments. Service Layer – Clearly defined, domain-driven, independently deployable services with well-managed boundaries. Authorization Server – Token-based authentication and fine-grained access control (OAuth2/JWT) to secure APIs and internal communication. Database Layer – Service-owned persistence with replication strategies to ensure consistency and availability. Distributed Cache – Reduces latency and improves throughput for high-volume workloads. Distributed Messaging – Asynchronous communication patterns to enable event-driven workflows and better fault isolation. Metrics & Monitoring – System health visibility through real-time metrics and alerting. Centralized Logging – End-to-end traceability for debugging, auditing, and operational transparency. In production environments, especially in domains like banking and healthcare, missing even one of these layers often leads to scaling challenges, operational blind spots, or security risks. My view is simple: microservices are not inherently scalable or resilient. They become scalable and resilient when the supporting infrastructure, governance, and observability are intentionally designed. Curious to hear others’ perspectives, what layer do you think teams most commonly underestimate in production systems? #softwarearchitecture #systemdesign #architecturalpatterns #backendengineering #scalablesystems #distributedarchitecture #microservicearchitecture #eventdrivenarchitecture #enterprisesystems #cloudnativeapps #digitalengineering #technologyleadership #React #Angular #WebDevelopment #JavaScript #Java #SpringBoot #Nodejs #kotlin #ClaudeAI #ChatGPT #GPT4 #GeminiA #LLaMA #MistralAI #RooCode #GitHubCopilot #CursorAI #ContinueAI #Tabnine #c2c