LLMs, MLOps, generative AI
Why this matters right now AI in software engineering isn’t about “copilots” or “10x devs.” It’s about this: your SDLC is quietly becoming the most important AI product in your company. If you deploy AI-assisted coding and testing at scale, you are: Changing who writes which code (and how fast) Changing the shape and risk…
Why this matters right now Most ML teams on AWS are stuck in an awkward middle state: The infra looks like 2015 (manually managed EC2, EKS clusters, ad‑hoc cron boxes). The workloads look like 2024 (LLM inference, feature stores, streaming feature pipelines). The bills look like a rounding error… until you hit product‑market fit. The…
Why this matters right now Most teams don’t have a “model problem.” They have a systems problem around that model. The pattern is familiar: A promising model in the lab turns into a support ticket generator in production. Product and execs ask “is the model worse now?” when what’s changed is the data, the users,…
Why this matters right now If you run large-scale AI/ML systems, you are in the energy business whether you like it or not. Three converging trends: GPU clusters are becoming power-first, not FLOPS-first: Data centers are hitting power and cooling ceilings before they hit rack space limits. Your next capacity constraint is megawatts, not U….
Why this matters right now Most teams are quietly bolting AI into systems that were never designed to handle: New identity flows (humans, services, agents, tools) Exploding secrets surface area (API keys, vector DB creds, model endpoints) Opaque supply chains (models, weights, datasets, plugins, third‑party tools) New failure modes in incident response (prompt abuse, model…
Why this matters right now Most companies already automated the obvious stuff: RPA clicking through legacy UIs Cron-driven ETL jobs CRUD-heavy internal tools Those helped, but they’re fragile, hard to change, and blind to context. They also don’t touch a large class of “semi-structured” work that humans still grind through: Reading and routing inbound emails/tickets…
Why this matters right now Most AI teams are currently doing one of two things with privacy and governance: Pretending “we’ll clean this up before GA” Assuming “the vendor handles that” Both are wrong. The moment you put a large language model into a workflow that touches: customer support tickets source code contracts / HR…
Why this matters right now If you run a fintech or a payments-heavy product, your fraud and risk stack just quietly became your largest ML surface area. Three things are converging: Payments and onboarding are commoditized. Stripe, Adyen, banking-as-a-service, and open banking APIs mean anyone can move money and open accounts with a few endpoints….
Why this matters right now Most teams are adding AI into software engineering in a way they’d never accept for any other production dependency: No SLOs. No monitoring beyond “devs seem happy.” No rollback strategy beyond “turn it off if people scream.” Meanwhile, AI systems are already: Writing chunks of your production code. Generating tests…
Why this matters right now Most teams that “run ML on AWS” are actually running three different systems: A training stack (often on EC2 or managed services) A real-time inference API (maybe SageMaker, maybe ECS, maybe Lambda) A batch / feature / data engineering system (Glue, EMR, Step Functions, Lambda, Fargate, whatever looked least painful…
Why this matters right now Everyone “has ML in production” now. But look closer and most setups fall into one of these buckets: The model worked in the A/B test and quietly degraded over 6–18 months. The infra is overbuilt (Kafka + Feast + feature store + 3 monitoring vendors) yet nobody can answer “Did…
Why this matters right now If you’re building or running ML-heavy systems, you already feel the constraints of physical infrastructure: power density, cooling, latency, and increasingly, grid availability. Solid-state batteries (SSBs) sound like someone else’s problem—EVs, mobility, “the car guys.” But if even half of the realistic claims land, they’ll directly affect: How cheap and…
Why this matters right now You don’t have “an AI feature” anymore. You have: A zoo of model runtimes (hosted, self-managed, embedded) Secrets sprawled across notebooks, CI, and prompt templates Third‑party AI APIs plugged directly into production workflows Auto-remediation bots with write access to your infra This is a new security perimeter, and most orgs…
Why this matters right now Most “AI in business” pitch decks are fluff. But underneath the noise, something real is happening: teams are quietly ripping out brittle RPA scripts and duct-taped macros, and replacing them with AI-driven workflows and agents that: Survive UI changes and minor process drift Handle edge cases without exploding into 500-rule…
Why this matters right now If you’re embedding large language models into production systems, your default posture is: you don’t know where your users’ data is, how long it lives, or who can reconstruct it. That’s a compliance and security problem, not just a privacy feel-good issue. Three forces are colliding: LLMs are extremely data…
Why this matters right now In fintech, “we’ll add ML for fraud later” used to be a reasonable lie you told investors. It isn’t anymore. Three things have converged: Payment rails are faster (instant payouts, RTP, SEPA Instant) → fraud loss windows shrink from days to seconds. Fraud and AML/KYC pressure is higher → regulators…
Why this matters right now AI is already in your SDLC whether you planned for it or not: Your devs are using code assistants. Product wants “AI features”. Leadership is asking for “10x productivity”. Ignoring it is not a strategy. But bolting it on like another SaaS tool is how you end up with: Silent…
Why this matters right now Most teams deploying machine learning on AWS are quietly stuck in the same trap: Training happens in ad‑hoc notebooks and EC2 boxes. Inference runs in a “temporary” Docker container that became permanent. Data pipelines are stitched together with whatever the first engineer shipped. Meanwhile, infra costs and operational noise creep…
Why this matters right now Most teams now have at least one machine learning system in production: recommendations, fraud scoring, lead scoring, ranking, anomaly detection, forecasting. The initial POC usually “works” and gets shipped. Six months later: Performance silently decays and no one notices until a business KPI falls off a cliff. Retraining jobs break…
Why this matters right now If you run anything with a battery roadmap—EV fleet, grid-scale storage, robotics, or large distributed IoT—you’re probably hearing some version of: “Solid-state batteries will double range, halve charging time, and solve safety. We should wait.” That’s a dangerous mental model. For AI & ML teams, this isn’t just an “EV…
