[DEMO] Define features once, then use the same definitions through training and production. See how Databricks Feature Store helps teams build batch and streaming features with Feature Views, create training sets with automatic point-in-time joins, and productionize those features with built-in governance and observability. Plus, see how Genie Code and MLflow support iteration along the way.

how it started. how it is going. it was just a 2 week research side project which i launched at 12am in tokyo (jet lagged). and went to sleep. woke up in the middle of the night and it went viral. the start of it was easy. maintenance, business and productionization was hard. thank you to @0xkydo, @zk_asv, @OpenRouter team (@shashankgoyal95, eddie, @OpenRouterMatt, @pingToven) and especially the providers for all the support and help along the way. it wouldn’t have been possible without yall. accelerating the future with everyone is a beautiful feeling.

This week I delivered a 60-minute workshop to CFOs, CHROs, and CLOs teaching them how to go from desired outcome (i.e. offer acceptance >60%) to designed AI pilot in 6 steps. Here are the 6 steps: *If you want the deck & worksheet I used for the workshop, check the link below. 1) Identify the outcome Transformation isn't about AI. It's about driving outcomes and using tools (like AI) to solve problems that stand in the way. Before deciding what work/product to transform, you need to decide the outcome. To do that, fill in the blanks: Improve [measure] from [baseline] to [target] by [date]. Examples: overdue invoices 18% to 12%. Month-end close 10 days to 6. Cash-forecast updates 2 days to 2 hours. Offer acceptance 40% to 60%. 2) Find the workflow Every business is plagued with inefficiency, even the most AI-native ones. Make a list of all of the workflows within your function/company that sit close enough to your outcome to impact it. Shorten the list to the workflows that pass the PAIN IN THE ASS TEST, and then circle one workflow that you believe, if made maximally efficient, would have the greatest impact on driving your outcome. One exercise for brainstorming workflows is forcing yourself to answer two questions: If you imagine work 12 months from now... 1) What is one specific way work happens differently? 2) What measurable result does that change create? 3) Design the new work First, create a process diagram of the workflow you chose as it stands today. It should look like 5-7 steps connected by lines. Second, draw a new process diagram of the workflow in its future, most efficient form. Next to each step put a label for what drives the work. Three labels to choose from: AI-led, AI-assisted, Human-only. Example: Hiring outreach process Current: Define hiring criteria --> Search for prospects --> Research fit and contact history --> Write outreach messages --> Approve, send, and log in ATS --> Copy activity into a separate tracker --> Handle replies and hand off New: Define criteria and permitted sources (Human) --> Find prospects using approved sources (AI-led) --> Review AI research; choose contacts (AI-assisted) --> Draft outreach from approved context (AI-led) --> Approve (Human) --> send, and log in ATS (AI-led) --> Handle replies and hand off (AI-assisted) 4) Assess readiness Once you've reimagined the work, you still can't press go. There's a list of 8 items that must be checked (GREEN/YELLOW/RED) before proceeding. You don't necessarily need all items to be GREEN ahead of a pilot, but you definitely do ahead of production. • Value at stake - You can name the specific benefit to your business (you've built out the ROI case) and a plausible path from this workflow to it • Process clarity and measurability - You know the (new/old) steps, start and finish, exceptions, and how to establish a baseline • Data and context readiness - The required context exists, is fit for the test, and can be accessed within agreed boundaries • Deployment capability - Someone (internally/externally) can configure, connect, test, support, and roll back the pilot • User adoption - Intended users help design it, understand their role, and will try it in real work • Change capacity - The team can spare time for training, review, feedback, and workflow changes • Ownership and decision rights - A business owner owns the result. People know who approves changes, resolves issues, and stops the test • Legal risk and controls - Required approvals, access limits, human review, logging, and stop rules are in place for the pilot. 5) Design & run pilot Every pilot should be documented & pitched internally with the following considerations included: • Scope: Who's included, what's the work they're doing, for how long? - Example: Two recruiters. One engineering role family. Four weeks, after permissions and controls are cleared. • Success: What's the business result you're looking to drive and what are early signals you need to see? - Example: Compare with the manual baseline: ≥25% less net prospecting time, no drop in prospect relevance, and saved time used for candidate care. Track offer acceptance longer. • Economics: Which benefit and cost assumptions need testing? - Example: Measure setup and running costs, including recruiter review time. Use observed time savings and prospect quality to update the business-case assumptions. Track actual agency fees avoided and additional delivery contribution over a longer period. • Guardrails: What must not happen? - Example: No unauthorized outreach or material opt-out breach. • Decision: What must be true to expand, revise, or stop the pilot? - Example: At week 4: do the early results justify continuing a bounded test? Continue if early gates pass; revise if fixes are viable; stop for a material breach or no credible path to value. Full ROI is not yet proven. 6) Scale scope & autonomy Based on performance of pilot & state of readiness criteria (from step 4), progressive productionizing of the workflow, rollout to the org, and autonomy for AI occurs. Link to the workshop & worksheet:

Production @Tesla Semi. The one so called “experts” said was impossible: • Travels 500+ real-world miles fully loaded (82K lbs), and about 600 miles half loaded. • 1.7 kWh/mile efficiency. ~7x less efficient than a Model Y despite being ~20x heavier (loaded). • Does 0-60 mph in 12.5 seconds. 24 seconds fully loaded. • The first Tesla with a power-coated exterior. • The drive unit is based on the Plaid motor (from S/X), but the carbon fiber sleeved rotor has been swapped for a steel-caged rotor. It makes it cheaper, lighter, more efficient, and more reliable. Total output is 1,073 hp. • The gearbox and motor share the same oil. Semi can last onwards of 250K+ miles without a fluid change vs 30-50K miles traditionally with diesel. • Semi uses fully electric power steering (there’s still a physical connection), it’s said to feel like a sports car rather than a typical semi. • Semi's thermal system was designed together with Cybercab. It shares the same pumps, compressor, and heat exchangers, and the Semi just has a bigger radiator. • Just like other Teslas, the driver profile carries over settings, seating positions, media, etc. All through their phone. Such a beast.

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Production conversations can reveal customer needs the original agent was not designed to handle. @LATAMAirlines found that 13% of messages to its Concierge agent were being classified as out of scope. After reviewing production traces, the team discovered that 95% were legitimate passenger needs. Adding a customer-care specialist reduced the out-of-scope rate from 13% to 1% and improved the return rate by 6%. Learn how LATAM Airlines and other CX teams are using production feedback to improve agent performance:

Production alert fires. You bounce between logs, dashboards, and traces — then ping an SRE anyway. STAROps in Qoder changes that: ask in plain language, get the root cause and the fix, right in your IDE. Alert → fix in minutes. Zero context switching. 🔗 #AIOps# #Qoder# #CloudNative# #UModel#

Production diary: raw materials, processing, and assembly are all proceeding as scheduled. Experience the gentle, soothing sensation of flowing water on tired nerves. The continuous movement and soft sounds help relieve mental tension and effectively calm summer irritability.

Production is more than a business; it is a passion for quality. Gentle sunlight filters through the zoo's lush greenery, brightening the active small animals and bathing every warm interaction between humans and animals in soft, healing light.

Production AI demands infrastructure that holds up at scale. NVIDIA and @AWSCloud announce three advances that strengthen every layer of the AI infrastructure stack. 1️⃣ AWS EC2 G7 instances bring NVIDIA Blackwell GPUs to AWS, delivering up to 4.6x AI inference performance over G6 2️⃣ Amazon OpenSearch Serverless NextGen now uses GPU-accelerated vector indexing powered by NVIDIA cuVS as default, enabling vector indexing up to 10x faster at a quarter of the cost 3️⃣ AWS Achieves NVIDIA Exemplar Cloud Status for GB300 Training Performance 🔗