How to Speed Up Generative AI Deployment

Recently helped a client cut their AI development time by 40%. Here’s the exact process we followed to streamline their workflows. Step 1: Optimized model selection using a Pareto Frontier. We built a custom Pareto Frontier to balance accuracy and compute costs across multiple models. This allowed us to select models that were not only accurate but also computationally efficient, reducing training times by 25%. Step 2: Implemented data versioning with DVC. By introducing Data Version Control (DVC), we ensured consistent data pipelines and reproducibility. This eliminated data drift issues, enabling faster iteration and minimizing rollback times during model tuning. Step 3: Deployed a microservices architecture with Kubernetes. We containerized AI services and deployed them using Kubernetes, enabling auto-scaling and fault tolerance. This architecture allowed for parallel processing of tasks, significantly reducing the time spent on inference workloads. The result? A 40% reduction in development time, along with a 30% increase in overall model performance. Why does this matter? Because in AI, every second counts. Streamlining workflows isn’t just about speed—it’s about delivering superior results faster. If your AI projects are hitting bottlenecks, ask yourself: Are you leveraging the right tools and architectures to optimize both speed and performance?

Generative AI (GenAI) is transforming DevOps by addressing inefficiencies, reducing manual effort, and driving innovation. Here's a practical breakdown of where and how GenAI shines in the DevOps lifecycle—and how you can start implementing it.  Key Applications of GenAI in DevOps  𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀   - Automatically generate well-defined 𝘂𝘀𝗲𝗿 𝘀𝘁𝗼𝗿𝗶𝗲𝘀 and documentation from business requests.   - Translate technical specifications into simple, 𝗵𝘂𝗺𝗮𝗻-𝗿𝗲𝗮𝗱𝗮𝗯𝗹𝗲 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 to improve clarity across teams.  𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁   - Automate 𝗯𝗼𝗶𝗹𝗲𝗿𝗽𝗹𝗮𝘁𝗲 𝗰𝗼𝗱𝗲 generation and unit test creation to save time.   - Assist in debugging by analyzing 𝗰𝗼𝗱𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 and suggesting potential fixes.  𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁   - Generate test cases from 𝘂𝘀𝗲𝗿 𝘀𝘁𝗼𝗿𝗶𝗲𝘀 𝗮𝗻𝗱 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀 to ensure robust testing coverage.   - Automate deployment pipelines and 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗽𝗿𝗼𝘃𝗶𝘀𝗶𝗼𝗻𝗶𝗻𝗴, reducing errors and deployment times.  𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀   - Analyze 𝗹𝗼𝗴 𝗱𝗮𝘁𝗮 in real-time to identify potential issues before they escalate.   - Provide actionable insights and 𝗵𝗲𝗮𝗹𝘁𝗵 𝘀𝘂𝗺𝗺𝗮𝗿𝗶𝗲𝘀 of systems to keep teams informed.  How To Implement GenAI: A Step-by-Step Approach  𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗣𝗮𝗶𝗻 𝗣𝗼𝗶𝗻𝘁𝘀   Start by pinpointing 𝘁𝗶𝗺𝗲-𝗰𝗼𝗻𝘀𝘂𝗺𝗶𝗻𝗴, 𝗿𝗲𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲, 𝗼𝗿 𝗲𝗿𝗿𝗼𝗿-𝗽𝗿𝗼𝗻𝗲 𝘁𝗮𝘀𝗸𝘀 in your DevOps workflow. Focus on areas where GenAI can deliver measurable value.  𝗖𝗵𝗼𝗼𝘀𝗲 𝗧𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗧𝗼𝗼𝗹𝘀   Explore GenAI solutions tailored for DevOps use cases. Look for tools that integrate seamlessly with your existing CI/CD pipelines, testing frameworks, and monitoring tools.  𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻   Ensure your data is 𝗰𝗹𝗲𝗮𝗻, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱, 𝗮𝗻𝗱 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁 to the GenAI models you're implementing. Poor data quality can hinder GenAI's performance.  𝗣𝗶𝗹𝗼𝘁 𝗦𝗺𝗮𝗹𝗹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀   Start with a 𝘀𝗶𝗻𝗴𝗹𝗲 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲 in a controlled environment. Measure the outcomes and gather feedback before scaling up across your organization.  𝗠𝗼𝗻𝗶𝘁𝗼𝗿 & 𝗥𝗲𝗳𝗶𝗻𝗲   Continuously evaluate your GenAI implementation for accuracy, efficiency, and impact. Be ready to retrain models and refine your approach as needed.  𝗧𝗵𝗲 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀  ✅ Faster development and deployment cycles.   ✅ Improved collaboration through simplified communication.   ✅ Enhanced system reliability with proactive monitoring.   ✅ Reduced manual effort, enabling teams to focus on innovation.  By adopting GenAI in DevOps strategically, you can unlock its potential to create a faster, more efficient, and innovative development environment.  𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝘁𝗮𝗸𝗲?   How do you see GenAI reshaping the future of DevOps in your organization?

As generative AI shifts from pilot to production, efficiency, cost, and scalability matter a lot more. Founded 2 years ago as "AWS for Generative AI," Together AI has raised $240M to provide cloud compute optimized for AI workloads. In this week's episode of my #AskMoreOfAI podcast, CEO/founder Vipul Ved Prakash talks about innovations to make models faster and smarter including: 🔹 FlashAttention: Smart GPU-aware tricks to reduce memory needed for calculating attention and rearrange calculations to speed up inference. 🔹 Speculative decoding: Speeds up inference by predicting multiple tokens in advance instead of one at a time, then selects the best ones and prunes the rest. 🔹 Model quantization: Reduce model size and speed up inference by reducing precision of numerical representations used in models without significantly degrading performance. In most LLMs, parameters are stored as 32-bit floating-point numbers, which consume a lot of memory and processing power. Quantization converts these to lower sig figs, eg 16-bit floats or even 8-bit integers.   🔹 Mixture of Agents, combining use of multiple specialized models (agents) that work together, with each agent handling a different aspect of a problem such as a sales agent, sales manager agent, deal desk agent, and legal contracts agents collaborating together. Vipul predicts that cloud compute for #GenAI will surpass the traditional hyperscaler business within 2-3 years. Salesforce Ventures is proud to have led the Series A earlier this year, and customers running models on Together can BYOM with Einstein Model Builder. 🎧 Listen or watch here! https://lnkd.in/g6XX4KCR

When working with 𝗟𝗟𝗠𝘀, most discussions revolve around improving 𝗺𝗼𝗱𝗲𝗹 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆, but there’s another equally critical challenge: 𝗹𝗮𝘁𝗲𝗻𝗰𝘆. Unlike traditional systems, these models require careful orchestration of multiple stages, from processing prompts to delivering output, each with its own unique bottlenecks. Here’s a 5-step process to minimize latency effectively:  1️⃣ 𝗣𝗿𝗼𝗺𝗽𝘁 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Optimize by caching repetitive prompts and running auxiliary tasks (e.g., safety checks) in parallel.  2️⃣ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Summarize and cache context, especially in multimodal systems. 𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘐𝘯 𝘥𝘰𝘤𝘶𝘮𝘦𝘯𝘵 𝘴𝘶𝘮𝘮𝘢𝘳𝘪𝘻𝘦𝘳𝘴, 𝘤𝘢𝘤𝘩𝘪𝘯𝘨 𝘦𝘹𝘵𝘳𝘢𝘤𝘵𝘦𝘥 𝘵𝘦𝘹𝘵 𝘦𝘮𝘣𝘦𝘥𝘥𝘪𝘯𝘨𝘴 𝘴𝘪𝘨𝘯𝘪𝘧𝘪𝘤𝘢𝘯𝘵𝘭𝘺 𝘳𝘦𝘥𝘶𝘤𝘦𝘴 𝘭𝘢𝘵𝘦𝘯𝘤𝘺 𝘥𝘶𝘳𝘪𝘯𝘨 𝘪𝘯𝘧𝘦𝘳𝘦𝘯𝘤𝘦.  3️⃣ 𝗠𝗼𝗱𝗲𝗹 𝗥𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀: Avoid cold-boot delays by preloading models or periodically waking them up in resource-constrained environments.  4️⃣ 𝗠𝗼𝗱𝗲𝗹 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Focus on metrics like 𝗧𝗶𝗺𝗲 𝘁𝗼 𝗙𝗶𝗿𝘀𝘁 𝗧𝗼𝗸𝗲𝗻 (𝗧𝗧𝗙𝗧) and 𝗜𝗻𝘁𝗲𝗿-𝗧𝗼𝗸𝗲𝗻 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 (𝗜𝗧𝗟). Techniques like 𝘁𝗼𝗸𝗲𝗻 𝘀𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 and 𝗾𝘂𝗮𝗻𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 can make a big difference.  5️⃣ 𝗢𝘂𝘁𝗽𝘂𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: Stream responses in real-time and optimize guardrails to improve speed without sacrificing quality. It’s ideal to think about latency optimization upfront, avoiding the burden of tech debt or scrambling through 'code yellow' fire drills closer to launch. Addressing it systematically can significantly elevate the performance and usability of LLM-powered applications. #AI #LLM #MachineLearning #Latency #GenerativeAI

Stop over-engineering your AI projects from day one. If you want to move in the genAI world, "build to scale" is likely outdated advice. Here's why 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗶𝗻𝗴 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 𝗼𝘃𝗲𝗿 𝗶𝗻𝗶𝘁𝗶𝗮𝗹 𝘀𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 can save you time, resources, and ultimately lead to more successful AI implementations. 𝗧𝗟𝗗𝗥; 𝘐𝘯 𝘵𝘩𝘦 𝘢𝘨𝘦 𝘰𝘧 𝘨𝘦𝘯𝘈𝘐 𝘢𝘯𝘥 𝘤𝘭𝘰𝘶𝘥, 𝘱𝘳𝘪𝘰𝘳𝘪𝘵𝘪𝘻𝘦 𝘷𝘢𝘭𝘪𝘥𝘢𝘵𝘪𝘯𝘨 𝘺𝘰𝘶𝘳 𝘈𝘐 𝘪𝘥𝘦𝘢 𝘸𝘪𝘵𝘩 𝘢 𝘴𝘤𝘢𝘭𝘢𝘣𝘭𝘦 𝘤𝘭𝘰𝘶𝘥 𝘣𝘢𝘴𝘦𝘥 𝘔𝘝𝘗 𝘰𝘷𝘦𝘳 𝘣𝘶𝘪𝘭𝘥𝘪𝘯𝘨 𝘢 𝘤𝘰𝘮𝘱𝘭𝘦𝘹, 𝘱𝘳𝘦𝘮𝘢𝘵𝘶𝘳𝘦𝘭𝘺 𝘴𝘤𝘢𝘭𝘦𝘥 𝘴𝘺𝘴𝘵𝘦𝘮. I was a firm believer in "build to scale", I really was. But with today's cloud-native architectures and the super fast iteration cycles enabled by genAI, this is changing. You do not need a monolith designed for millions of users on day one. You need to prove your AI-powered idea actually works. Whether it works is determined 20% by the way you engineered it and 80% the feedback your users give you (implicit or explicit). GenAI success is determined by adoption and not technology. Last year I built genAI applications that are loved by 5,000+ distinct enterprise users and here's the new playbook: 𝟭) 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗳𝗶𝗿𝘀𝘁. Build a lean, mean Minimum Viable Product (MVP) focused on validating your core hypothesis. Does your genAI solution actually solve a problem for your target audience? 𝟮) 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗰𝗹𝗼𝘂𝗱 𝘀𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆. Use cloud platforms (AWS, Azure, GCP) and their serverless offerings (e.g. Cloud Run) to handle initial user loads. These services scale up and down automatically based on demand, will cost you very little and will carry you a long way on simple architectures and implementations. 𝟯) 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 𝗳𝗿𝗲𝗾𝘂𝗲𝗻𝘁𝗹𝘆 𝘄𝗶𝘁𝗵 𝗚𝗲𝗻𝗔𝗜. GenAI can significantly speed up your initial development, allowing you to get to market faster and start gathering real-world data. Your learning model is probably more important than your inference model in the beginning. In the last 12 months we had half a dozen MVPs where we released new versions every other day! 𝟰) 𝗦𝗰𝗮𝗹𝗲 𝘀𝗺𝗮𝗿𝘁 𝗮𝗻𝗱 𝗻𝗼𝘁 𝗽𝗿𝗲𝗺𝗮𝘁𝘂𝗿𝗲𝗹𝘆. Once you've validated your concept and see traction, then – and only then – start optimizing for larger-scale performance. Use the data you've gathered to inform your architectural decisions. That's what great product management is all about. 𝟱) 𝗥𝗲𝗳𝗮𝗰𝘁𝗼𝗿 𝘄𝗵𝗲𝗻 𝗻𝗲𝗲𝗱𝗲𝗱. Don't be afraid to refactor your architecture as you grow. Cloud-native architectures are designed for this kind of flexibility. You can rewrite parts of your application to improve performance and handle higher user loads without having to start from scratch.  Are you still building to scale from the start? What are your experiences with a "validate first" approach in your AI projects? Share your thoughts in the comments please!

EXCELLENT 𝗚𝗲𝗻 𝗔𝗜 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸 from Anthropic Anthropic released this excellent playbook for organizations towards adoption of Generative AI. We have all seen that early adopters are already seeing staggering results: 𝟮𝟬-𝟯𝟱% 𝗳𝗮𝘀𝘁𝗲𝗿 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲𝘀 𝟭𝟱% 𝗿𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗶𝗻 𝗰𝗼𝗱𝗶𝗻𝗴 𝘁𝗶𝗺𝗲 𝗳𝗼𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝟯𝟬-𝟱𝟬% 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗰𝗿𝗲𝗮𝘁𝗶𝗼𝗻 𝟭𝟬% 𝗼𝗳 𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀 𝗮𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝘁𝗼 𝗚𝗲𝗻𝗔𝗜 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻𝘀 (𝗠𝗰𝗞𝗶𝗻𝘀𝗲𝘆 𝟮𝟬𝟮𝟰) But success isn’t accidental. From Anthropic’s real-world enterprise deployments, here’s what works: 1. 𝗦𝘁𝗮𝗿𝘁 𝗦𝗺𝗮𝗿𝘁: 𝗣𝗶𝗹𝗼𝘁 𝗛𝗶𝗴𝗵-𝗜𝗺𝗽𝗮𝗰𝘁 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀 Focus on projects that: ✅ Leverage LLM strengths (e.g., unstructured data processing) ✅ Have measurable ROI (e.g., ticket routing accuracy → cost savings) ✅ Balance ambition with low-risk scalability (e.g., internal docs processing before customer-facing chatbots) “We reduced Clinical Study Report time from 12 weeks to 10 minutes—each day saved adds ~$15M in revenue.” —Novo Nordisk 𝟮. 𝗕𝘂𝗶𝗹𝗱 𝗳𝗼𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻: 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 > 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 Many teams rush to fine-tune models, but 80% of performance gains often come from: 🔧 Structured prompts (task + rules + few-shot examples) 💡 Chain-of-Thought prompting (break complex tasks into steps) 📊 Automated evaluations (test edge cases at scale) Pro Tip: Use tools like Anthropic’s Console Evaluator to compare prompt versions side-by-side. 𝟯. 𝗦𝗰𝗮𝗹𝗲 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠𝗢𝗽𝘀: 𝟱 𝗡𝗼𝗻-𝗡𝗲𝗴𝗼𝘁𝗶𝗮𝗯𝗹𝗲𝘀 Deploying AI at scale requires: ◼️ Robust monitoring (track token usage, hallucinations) ◼️Prompt version control (treat prompts like code) ◼️Security-by-design (data privacy, access controls) ◼️Cost-aware infrastructure (caching, model sizing) ◼️Continuous feedback loops (human-in-the-loop refinement) “GenAI cut Lonely Planet’s content costs by 80%, freeing creators to focus on innovation.” 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 GenAI isn’t a moonshot—it’s a methodical journey from pilot to enterprise-wide transformation. Companies like Pfizer, DoorDash, and WPP prove that the right strategy (plus partners like Anthropic + AWS) can compress 12-month timelines into weeks. RealAIzation #Anthropic

𝗧𝗟:𝗗𝗥: Building and deploying GenAI based applications follows a somewhat specific 𝗚𝗲𝗻𝗔𝗜 𝗮𝗽𝗽 𝗹𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲. Knowing the various stages is key as you build your apps and the supporting GenAI platform. Amazon Web Services (AWS) Bedrock has you well covered with more coming at re:Invent 2024 As we learn more about building, deploying and running GenAI powered applications the various stages of the lifecycle are getting clearer: 𝟭. 𝗦𝗲𝗹𝗲𝗰𝘁 𝗙𝗠(𝘀): Pick on or more Foundation Models to tackle the use case at hand – from conversations to summaries to reasoning to embeddings to multimodality 𝟮. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Less necessary compared to last year as LLMs have improved, but still needed especially when chaining prompt flows across models  𝟯. 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗗𝗮𝘁𝗮 𝗮𝗻𝗱 𝗙𝗠 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Use mostly techniques like RAG (including advanced options like chunking, re-ranking) and sometimes fine-tuning and distillation to connect your data with the power of the FM 𝟰. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰: Build agentic workflows for use cases that warrant the need to tap into the reasoning power of LLMs. Agentic can be combined with RAG and model tuning 𝟱. 𝗠𝗼𝗱𝗲𝗹 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻: Have an evaluation harnesses for the various use cases to select the right model, data set, prompt etc 𝟲. 𝗔𝗽𝗽 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Use streaming cross model APIs to integrate models and apps 𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝘆𝗼𝘂𝗿 𝗺𝗼𝗱𝗲𝗹 𝗮𝗻𝗱 𝗔𝗽𝗽: Once deployed track for cost, performance etc 𝟴. 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀: Lower hallucinations, stop bad conversations! 𝟵. 𝗖𝗼𝘀𝘁 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Using specialized compute purchases and caching Bedrock does most of the above today but will announce more innovations on each of the above categories leading up to and at re:Invent. Can’t share what yet :) but will share a summary after the keynotes using the same categories. In case you missed it, the team launched Prompt Optimization (https://go.aws/3AWgHb8) while also releasing Prompt Flows (https://go.aws/3Z1gifD). Hear what's next at re:Invent. See you there!

ICYMI.. Hard truths by McKinsey & Company on scaling #Gen #AI Requires a strategic approach that focuses on integration, cost management, and creating value-driven teams. By addressing these challenges, companies can move past the pilot phase and achieve significant business value from Gen AI. - Eliminate the Noise, Focus on the Signal: Cut down on experiments and focus on solving important business problems. Most companies spread resources too thinly across multiple gen AI initiatives - Integration Over Individual Components: The challenge lies in orchestrating the interactions and integrations at scale, not in the individual pieces of gen AI solutions - Manage Costs: Models account for only about 15% of the overall cost. Change management, run costs, and driving down model costs are crucial. - Tame the Proliferation of Tools and Tech: Narrow down to capabilities that best serve the business and take advantage of cloud services while preserving flexibility - Create Value-Driven Teams: Teams need a broad cross-section of skills to build models and ensure they generate value safely and securely - Target the Right Data: Invest in managing the data that matters most for scaling gen AI applications - Reuse Code: Reusable code can increase development speed by 30 to 50% - Orchestration is Key: Effective end-to-end automation and an API gateway are crucial for managing the complex interactions required for gen AI capabilities - Observability Tools: These tools are necessary for monitoring gen AI applications in real-time and making adjustments as needed - Cost Optimization: Tools and capabilities like preloading embeddings can reduce costs significantly - ROI Focus: Investments in gen AI should be tied to return on investment (ROI), with different use cases requiring different levels of investment Source: https://lnkd.in/ezYN5chb

The best KPI for automation and AI in an engineering team isn’t “how much code it generated,” but “how much the release cycle got shorter.” Because the team goes through the same chain every time: idea → ticket → code → tests → review → release → monitoring → fix. And this is exactly where the real value isn’t in generic AI chats, but in generative and automated tools for engineering team tools that plug into the SDLC and take routine work off people’s hands. Here are 3 practical ways to speed up Delivery in 2026 👇 1) Generative coding tools: faster development and more consistent maintenance What to delegate: - generating boilerplate and repetitive blocks - refactoring without changing behavior - writing documentation for modules/endpoints - preparing a pull request (PR) descriptions (what changed, why, and how to test) 💡 Tools: GitHub Copilot, Cursor, Codeium 2) Automated delivery tools: from task to pull request in small iterations This speeds up not just “coding”, but the entire workflow. What to delegate: - breaking down requirements + drafting clarifying questions for the ticket - an implementation plan with a risk assessment - splitting work into subtasks and creating a readiness checklist - creating a PR with a structured description 💡 Tools: ChatGPT / Claude / Gemini + agentic integrations with your repo / IDE 3) Generative tools for QA/DevOps: tests, triage, and fewer incidents A lot of teams “speed up coding” but still get bottlenecked by testing and releases. Automation can make a very noticeable difference here. What to delegate: - generating tests. - analyzing logs and drafting a root-cause analysis (RCA) - security checks and fix suggestions - release notes, runbooks, and checklists 💡Tools: Testlum for dynamic testing, and SonarQube + Snyk for static analysis. The most common mistake teams will make in 2026 is adding automation as just another tool without changing the process. To make generative and automated tools truly accelerate delivery, think of it this way: not “we’re adding AI,” but “we’re implementing a specific use case within the SDLC.” 💭 Share in the comments what generative or automated tools you are already using in your team today, and for what exactly (code/PRs/tests/releases/monitoring)? ♻️ Save this post to try all the tools later. Share it with others who may find these helpful.

Elevating Generative AI from Pilot to Production: Key Insights for Success Transitioning Generative AI from pilot projects to full-scale deployment demands both strategic foresight and technical execution: 1️⃣ Data Quality as the Foundation 🛠 LLMs require high-quality, structured data. Implementing low-latency data pipelines and ensuring data governance are critical. Techniques like federated learning can enhance data privacy without sacrificing model performance. 2️⃣ Strategic Tech Stack Design 🧩 A hybrid model approach—combining foundational LLMs with domain-specific fine-tuning—ensures scalability and adaptability across varied applications, from healthcare to finance. 3️⃣ Optimize for Performance, Scalability, and Cost 🚀 Employ advanced orchestration frameworks to manage resources dynamically, leveraging techniques like model parallelism to balance compute costs with performance needs. The Strategic Edge 🎯 For more in-depth strategies and cutting-edge insights in Generative AI, subscribe to the Generative AI with Varun newsletter. 📰 https://lnkd.in/g89M-sgz