Machine Learning for Time Optimization

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?

Time is the most expensive line item we never budget. So I gave AI 90 days of my calendar. I asked, “Where am I wasting my time and payroll?” It found the leaks. I closed them. These 9 shifts returned 15 hours/week and sharper decisions: 1. Meeting Transcription + Action Mining Old way: 45-minute meeting, 30 minutes writing notes after New way: AI captures everything, extracts actions in 2 minutes Tools I use: → Otter.ai records and transcribes → Claude analyzes for decisions and commitments 💡 You need actions, not archives. 2. Email Triage on Autopilot Old way: Start day with 47 emails, lose 90 minutes New way: AI pre-sorts, drafts responses, flags only what needs me My setup: → Superhuman AI categorizes by urgency → ChatGPT drafts routine responses 💡 80% of emails don't require leadership thinking. 3. Calendar Defense System Old way: Back-to-back meetings, zero thinking time New way: AI blocks focus time based on energy patterns What changed: → Reclaim.ai analyzes my productivity patterns → Automatically blocks deep work when I'm sharpest 💡 Your calendar reflects your priorities. Let AI be the bouncer. 4. Decision Documentation Old way: Decisions made, context lost, repeated discussions New way: AI creates decision logs with full context The system: → Every decision recorded with why, who, when → Searchable knowledge base 💡 Leaders waste 5 hours/week revisiting old decisions. 5. Prep Work Automation Old way: 30 minutes prep per meeting New way: AI briefs me in 3 minutes AI handles: → Participant background summaries → Related past decisions 💡Most prep work is information gathering, not strategic thinking. 6. Real-Time Coaching Notes Old way: Try to remember feedback for 1:1s New way: AI captures moments as they happen My process: → Voice note immediate observations → 1:1s have rich, specific examples 💡 The best feedback happens in the moment. 7. Strategic Thinking Amplification Old way: Brainstorm alone or in groups New way: AI as thought partner How I use it: → Feed challenge into Claude/ChatGPT → Get 10 perspectives I hadn't considered 💡 AI doesn't replace thinking. It accelerates it. 8. Delegation Optimization Old way: Guess who's best for what New way: AI matches tasks to skills/capacity The system tracks: → Team member strengths → Development opportunities 💡 Bad delegation costs 3x more time than doing it yourself. 9. Energy Management Old way: Push through energy dips New way: AI optimizes task-energy matching What AI revealed: → My peak decision hours: 9-11 AM → Creative energy: 2-4 PM 💡 Working against your energy patterns is counterproductive. The result? I stopped being busy and started being strategic. Most leaders are drowning in tasks AI could handle in seconds. They're so busy being busy, they can't lead. ♻️ Repost if a leader needs to see this. Follow Carolyn Healey for more AI insights.

If you’re an AI engineer trying to optimize your LLMs for inference, here’s a quick guide for you 👇 Efficient inference isn’t just about faster hardware, it’s a multi-layered design problem. From how you compress prompts to how your memory is managed across GPUs, everything impacts latency, throughput, and cost. Here’s a structured taxonomy of inference-time optimizations for LLMs: 1. Data-Level Optimization Reduce redundant tokens and unnecessary output computation. → Input Compression:  - Prompt Pruning, remove irrelevant history or system tokens  - Prompt Summarization, use model-generated summaries as input  - Soft Prompt Compression, encode static context using embeddings  - RAG, replace long prompts with retrieved documents plus compact queries → Output Organization:  - Pre-structure output to reduce decoding time and minimize sampling steps 2. Model-Level Optimization (a) Efficient Structure Design → Efficient FFN Design, use gated or sparsely-activated FFNs (e.g., SwiGLU) → Efficient Attention, FlashAttention, linear attention, or sliding window for long context → Transformer Alternates, e.g., Mamba, Reformer for memory-efficient decoding → Multi/Group-Query Attention, share keys/values across heads to reduce KV cache size → Low-Complexity Attention, replace full softmax with approximations (e.g., Linformer) (b) Model Compression → Quantization:  - Post-Training, no retraining needed  - Quantization-Aware Training, better accuracy, especially <8-bit → Sparsification:  - Weight Pruning, Sparse Attention → Structure Optimization:  - Neural Architecture Search, Structure Factorization → Knowledge Distillation:  - White-box, student learns internal states  - Black-box, student mimics output logits → Dynamic Inference, adaptive early exits or skipping blocks based on input complexity 3. System-Level Optimization (a) Inference Engine → Graph & Operator Optimization, use ONNX, TensorRT, BetterTransformer for op fusion → Speculative Decoding, use a smaller model to draft tokens, validate with full model → Memory Management, KV cache reuse, paging strategies (e.g., PagedAttention in vLLM) (b) Serving System → Batching, group requests with similar lengths for throughput gains → Scheduling, token-level preemption (e.g., TGI, vLLM schedulers) → Distributed Systems, use tensor, pipeline, or model parallelism to scale across GPUs My Two Cents 🫰 → Always benchmark end-to-end latency, not just token decode speed → For production, 8-bit or 4-bit quantized models with MQA and PagedAttention give the best price/performance → If using long context (>64k), consider sliding attention plus RAG, not full dense memory → Use speculative decoding and batching for chat applications with high concurrency → LLM inference is a systems problem. Optimizing it requires thinking holistically, from tokens to tensors to threads. Image inspo: A Survey on Efficient Inference for Large Language Models ---- Follow me (Aishwarya Srinivasan) for more AI insights!

Machine Learning models are critical not only for many customer-facing products like recommendation algorithms but also very important for unlocking value in backend tasks to enable efficient operations and save business costs. This blog, written by the machine learning engineering team at Netflix, shares the team's approaches to automatically leverage machine learning to remediate failed jobs without human intervention. -- Problem: At Netflix, millions of workflow jobs run daily in their big data platform. Although failed jobs represent a small portion, they still incur significant costs given the large base. The team currently has a rule-based approach to categorize error messages. However, for memory configuration errors, engineers still need to remediate the jobs manually due to their intrinsic complexity. -- Solution: The team uses the existing rule-based classifier as the first pass to classify errors, and then develops a new Machine Learning service as the second pass to provide recommendations for memory configuration errors. This Machine Learning system has two components: one is a prediction model that jointly estimates the probability of retry success and the retry cost, and the other is an optimizer that recommends a Spark configuration to minimize the linear combination of retry failure probability and cost. -- Result: Such a solution demonstrated big success, with more than 56% of all memory configuration errors being remediated and successfully retried without human intervention. This also decreased the compute cost by about 50% because the new configurations make the retry successful or disable unnecessary retries. Whenever there is a place with inefficiencies, it's helpful to think about a better solution. Machine learning is the way to introduce intelligence into such solutions, and this blog serves as a nice reference for those interested in leveraging machine learning to improve their operational efficiency. #machinelearning #datascience #operation #efficiency #optimization #costsaving #snacksweeklyondatascience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gRcaifKR 

Conquering Time in Machine Learning 🕰️🤖 Time is tricky. As humans, we struggle to grasp its fluidity. But for machines, making sense of time complexities is even harder. Time2Vec is a model-agnostic approach to represent time as learnable vector embeddings rather than simple scalar inputs. Sine waves encode temporal qualities like progression and periodicity, while linear layers handle monotonic time progression. 🔄➡️ Together, they distill time complexity in a form algorithms can easily digest. The researchers demonstrated this approach to improve performance across a range of models and problems: 1️⃣ The vector approach automatically extracts useful signals, no need to hand-engineer custom time features. 2️⃣ Time2Vec integrates into sequence models like RNNs, no architecture change required. 3️⃣ Outperformed baselines in both synchronous and asynchronous tasks. For anyone grappling with temporal modeling in their work, I definitely recommend exploring this paper. 💪 https://lnkd.in/ehSgdr-s The references are a treasure trove of perspectives on handling time. 📚🤓

This simple optimization cut agent response time by 54% and reduced tokens by 95%. 🤯 Here's what I have learned after building and deploying agents. Many developers reach for tools or MCP servers by default. Without considering if there's a simpler way. One of the first questions I ask when building an agent is this: - Does your agent need to look up this information, or can you just provide it? For example: Getting Current Date/Time ❌ Approach 1: Give agent a current_time tool - 2 LLM calls (agent decides → invokes tool → processes result) - 4.78 seconds - 1,734 tokens - Agent has to reason about using the tool ✅ Approach 2: Include date/time in system prompt - 1 LLM call (information already there) - 2.18 seconds - 94 tokens - Instant access The Impact at Scale: - 54% faster (2.18s vs 4.78s) - 95% fewer tokens (94 vs 1,734) - Better UX (no extra latency) - Lower cost per interaction Imagine this at scale for 1M agent calls/month: - Tool approach: ~1.7B tokens - Context approach: ~94M tokens - Savings: $hundreds to $thousands (depending on your model) I can't stress this enough, not everything needs to be a tool. Let alone an MCP server. This technique shown is called dynamic context injection. This is where you update the agent's context with live data instead of making it use tools to fetch data you already have. You can inject this via system prompt, user prompt or even during the agent event loop. This is just one of the many topics I intend to cover. So if you have questions ask below or have a comment drop it below. 👇🏾 #AIAgents #ProductionAI #CostOptimization #StrandsAgents #AWSreInvent #LLMOptimization

A 6-month, 10,000-step calibration process. Compressed into 72 hours. A large chemical manufacturer needed to bring a new high-density polyethylene material online. Calibration was so complex it demanded an expert operator’s full attention, draining time from other critical processes. Here’s how they accelerated the process: ✔️ Built a simulation of the chemical reaction using real plant data. ✔️ Trained a multi-agent system across 70 degrees of freedom to optimize molecular weight distribution. ✔️ Deployed the system after two weeks of design and training. The result: a process that once tied up experts for half a year was completed in just three days. Multi-agent AI, built through machine teaching and well-engineered design, is changing what’s possible in high-stakes industrial environments. Where could that kind of time compression shift the economics of your operation? #industrialAI #industrialautomation #physicalAI #multiagentsystems

🚀 [New Preprint Alert! Hybrid ML & Optimization] I’m excited to share our latest research: “A Hybrid Machine Learning and Optimization Framework for Large-Scale Multi-Order Courier Assignment” led by Minh Vu 📝 Preprint link: https://lnkd.in/g47EMu_g With the explosive growth of on-demand delivery, efficiently matching thousands of couriers to rapidly incoming orders has become a high-stakes challenge. Traditional optimization methods often overlook a critical factor—courier behavior, especially whether couriers will accept the assignments offered to them. 🔍 What We Introduce: ✅ A high-performance machine learning model to predict courier acceptance probability (AUC = 0.924, Accuracy = 86.5%) ✅ A multi-objective MILP model that balances: • Order acceptance • Delivery time • Fair workload distribution 📈 Key Results (Real-World Dataset – 5,960 courier-order pairs): 📦 Actual acceptance rate improved from 73.2% → 81.5% ⚖️ Trade-off between fairness and efficiency made explicit and adjustable 🎯 Operators can now tune their strategy along the efficiency–fairness frontier 💡 Why It Matters: This hybrid ML + optimization framework turns a complex operational problem into a controllable decision-making process, empowering last-mile logistics platforms to align strategies with real-world service goals. If you’re working in last-mile logistics, dispatch systems, dynamic matching, or decision support, I’d love to hear your thoughts and discuss potential collaborations. #MachineLearning #Optimization #OnDemandDelivery #LastMileLogistics #DecisionSupport #OperationsResearch #AIInLogistics #Purdue #SoET #IndustrialEngineeringTechnology #PPI

When I was an Analyst at Goldman Sachs I worked countless hours and sleepless nights. Today’s “Protected Saturday” policies would have been met with a scoff. My bosses had no clue how I was spending my time. Frankly, neither did I.  We had to fill out high-level timesheets - a vague compliance checkbox everyone completed half-heartedly… So I built my own in Excel. An hour-by-hour granular breakdown of how I was spending time. The pie charts and time series data gave me a handle on exactly where I created value (or not) and a way to communicate it upward. Today I no longer need that kind of self-auditing (though I’m sure some would beg to differ), but the insight remains: you can’t optimize what you don’t quantify. One of Laurel’s professional services clients discovered their senior project leaders were spending 14% of their time formatting PowerPoints. You don’t find that in a timesheet. If British Petroleum uncovered a 1% oil-refinement gain, it’d transform their earnings. In professional services, massive inefficiencies pass unnoticed. So much of supply chains is human - time, attention, judgment - yet we still manage it with 1990s tools at best. Laurel’s shared data highlights just a glimpse of the possible.  Their AI system can turn time into structured, observable, and analyzable data: an unprecedented enterprise intelligence unlock. From 2,000+ professionals across law and accounting, their latest report found: → 30% of all work time, roughly 3 hours a day, goes unrecorded, distorting revenue and capacity planning. →  Professionals get just 15 minutes of uninterrupted focus during the day. → Firms adopting AI time solutions recover an average of 28 minutes of missed billable time, that’s $37,000 annually per professional (!). Hard supply chains are engineered, refined, simulated, and stress-tested to the inch and the second. [There are entire expert organizations like Kearney that specialize in supply chain analysis and optimization.] However, understanding and optimizing knowledge work is still largely guesswork disguised as process. To address the other 50% of the economy, we need to do what no time tracking, consultancy, or BI dashboard has done: accurately map, quantify, and analyze the physics of time and work. Check out the full report → https://lnkd.in/gSFqbBHf Full disclosure: ACME Capital is an early investor in Laurel.