Machine Learning in Task Management

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 

I burned $47 in API calls watching an agent check the same API endpoint every 30 seconds for 6 hours straight. It was supposed to monitor a deployment status. Instead, it just… kept checking. No breaks. No strategy. Just pure, expensive anxiety. That's the problem with current AI agents: they're brilliant at one-time tasks but absolutely terrible at waiting. Microsoft Research just released something that fixes this: SentinelStep. It's now open-sourced in their Magentic-UI system, and honestly, this changes how we think about agent workflows. Here's what makes it work: The system breaks monitoring into three components: actions (what to check), conditions (when to stop), and polling intervals (how often to check). Simple concept, but the execution is clever. Dynamic polling is where it gets interesting. The agent doesn't blindly check every minute. It makes an educated guess based on task urgency. Monitoring quarterly earnings? Less frequent checks. Tracking an urgent email? More aggressive polling. Then it adjusts based on observed patterns. Now, here's my take on what's probably happening behind the scenes: The system likely maintains a state snapshot after the first check, basically freezing what the agent knows at that moment. Think of it like taking a photo of the agent's brain. For each subsequent check, instead of carrying forward the entire conversation history (which would expand the context window), it loads the frozen snapshot, performs the new check, compares the results, and determines whether the condition is met. The polling adjustment probably uses something straightforward maybe exponential backoff with task-specific multipliers. If nothing changes after a few checks, wait longer next time. If patterns emerge (like "emails usually arrive between 9-11 AM"), the interval shrinks during those windows. No fancy ML needed, just sensible heuristics. Context management is the real win here. Without it, a 2-day monitoring task would accumulate thousands of tokens of redundant checks. With state snapshots, each check stays isolated and lightweight. They tested it with SentinelBench and showed success rates jumping from 5.6% to 33-39% for 1-2 hour tasks. But here's what I think matters more than those numbers: where you'd actually use this. Imagine monitoring CI/CD pipelines that take hours to complete, tracking competitor pricing that updates sporadically, or watching for specific social media mentions across days. These aren't hypothetical—they're tasks we currently handle with clunky cron jobs or manual checking. pip install magentic-ui right now and start experimenting. The foundation is solid, though you'll want to test thoroughly for production use cases (Microsoft's transparency note calls this out explicitly). This feels like one of those unglamorous infrastructure pieces that quietly enable a whole new category of automation. Not flashy, but exactly what we needed.

𝗔𝗜 𝗶𝘀𝗻'𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗶𝗻𝗴 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗺𝗮𝗻𝗮𝗴𝗲𝗿𝘀. 𝗜𝘁'𝘀 𝗰𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗮 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆 𝗻𝗲𝘄 𝘁𝘆𝗽𝗲 𝗼𝗳 𝗣𝗠. While most PMs drown in status reports and guess at resource allocation, AI-powered project managers operate with predictive insights. Here's how AI is reshaping project management: 𝗦𝗛𝗜𝗙𝗧 𝗙𝗥𝗢𝗠 𝗥𝗘𝗔𝗖𝗧𝗜𝗩𝗘 𝗧𝗢 𝗣𝗥𝗘𝗗𝗜𝗖𝗧𝗜𝗩𝗘 𝗥𝗜𝗦𝗞 𝗠𝗔𝗡𝗔𝗚𝗘𝗠𝗘𝗡𝗧 Traditional risk management waits for problems. AI analyzes historical data and real-time signals - sprint velocity, scope changes, communication patterns - to predict bottlenecks before they happen. Early warning alerts for schedule slippage and budget overruns. You intervene weeks before crisis mode. → Use AI dashboards to monitor project health scores → Automate contingency plans based on risk patterns 𝗜𝗡𝗧𝗘𝗟𝗟𝗜𝗚𝗘𝗡𝗧 𝗥𝗘𝗦𝗢𝗨𝗥𝗖𝗘 𝗔𝗟𝗟𝗢𝗖𝗔𝗧𝗜𝗢𝗡 AI analyzes skill sets, workloads, and historical performance to match the right person to specific tasks. Prevent burnout and optimize delivery speed. → Model "what-if" staffing scenarios in real-time → See how resource changes affect milestone dates 𝗛𝗬𝗣𝗘𝗥-𝗔𝗨𝗧𝗢𝗠𝗔𝗧𝗜𝗢𝗡 𝗢𝗙 𝗔𝗗𝗠𝗜𝗡𝗜𝗦𝗧𝗥𝗔𝗧𝗜𝗩𝗘 𝗧𝗔𝗦𝗞𝗦 Status reporting eats 6-8 hours weekly. AI automatically compiles updates from emails, Slack, JIRA, and meetings to generate board-ready reports. → Convert project calls into action items automatically → Generate executive summaries from scattered data 𝗣𝗥𝗘𝗖𝗜𝗦𝗜𝗢𝗡 𝗣𝗟𝗔𝗡𝗡𝗜𝗡𝗚 𝗔𝗡𝗗 𝗘𝗙𝗙𝗢𝗥𝗧 𝗘𝗦𝗧𝗜𝗠𝗔𝗧𝗜𝗢𝗡 Human estimation is notoriously optimistic. AI analyzes thousands of similar historical projects for realistic timelines and cost variances. Project charters become data-driven instead of wishful thinking. → Use text-to-project generators for initial work breakdown structures → Get estimates based on actual complexity, not gut feelings 𝗘𝗡𝗛𝗔𝗡𝗖𝗘𝗗 𝗗𝗘𝗖𝗜𝗦𝗜𝗢𝗡 𝗦𝗨𝗣𝗣𝗢𝗥𝗧 AI analyzes multiple scenarios and recommends optimal paths based on cost, time, and risk. Present data-backed options to executives with clear trade-offs. → Query project documentation: "What caused delays in our last three cloud migrations?" → Get scenario analysis for critical decisions 𝗦𝗬𝗦𝗧𝗘𝗠𝗔𝗧𝗜𝗖 𝗞𝗡𝗢𝗪𝗟𝗘𝗗𝗚𝗘 𝗠𝗔𝗡𝗔𝗚𝗘𝗠𝗘𝗡𝗧 "Lessons learned" documents are buried in folders. Tribal knowledge that leaves with departing team members. AI makes your organization's entire project history searchable and actionable. → Scan legacy documents for relevant risks automatically → Get pattern recognition across similar project types The PMs adopting these approaches aren't just more efficient; they're also more effective. They're operating at a different strategic level, while others manually update Gantt charts. Follow Dr. Brian Ables, PMP, for more insights on the future of project management. ♻️ Share this with other project managers who need to see where PM is heading.

What does your 2 a.m. call cost? Most trade companies do not lose margin on the work they expect. They lose it when a system fails mid-project, a technician gets pulled into an emergency, or a unit finally gives out after weeks of quiet signals nobody had time to connect. The real issue is not only the breakdown. It is the operational whiplash that follows across teams, schedules, budgets, and customer trust. Early Warning Operations: Machine learning can help read the patterns people rarely have time to see in the daily flow of work. It looks across equipment performance, service histories, and job site conditions to surface warnings before failure turns into a fire drill. That can include recurring service calls on the same unit, subtle performance shifts, or environmental conditions that have shown up before HVAC, plumbing, or electrical problems. The value for teams is simple. Clear language. Timely signals. Better prioritization. Instead of sending technicians out based on fixed schedules or gut feel, companies can plan maintenance around real conditions and focus time on the jobs most likely to create disruption. That shift creates fewer emergency surprises and a steadier way to run the business. Core Ideas: • Pattern-based alerts - Use service history, performance signals, and site conditions together so teams can spot risk earlier than manual review allows. • Condition-led planning - Build maintenance priorities around what systems are showing now, not only what the calendar says. • Technician focus - Give teams plain-language warnings so they know where to start work without sorting through raw data first. • Lower disruption risk - Catching issues before failure helps protect timelines, budgets, and customer confidence across active jobs. The strongest use of machine learning in the trades is helping teams act before hidden system risk becomes an expensive emergency. #TradeOperations, #PredictiveMaintenance, #HVAC, #FieldService, #AI #RefreshWithRyza

Automate your admin work or AI automates your manager role. Some PMs think it's "our job" to move tasks on a board. It's NOT. I recently spoke with several executives. They are already moving basic PM tasks to AI. 𝗛𝗲𝗿𝗲'𝘀 𝗵𝗼𝘄 𝗔𝗜 𝗲𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗲𝘀 𝘁𝗵𝗲 𝗮𝗱𝗺𝗶𝗻: 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 → AI generates task lists and timelines from briefs 𝗦𝘁𝗮𝘁𝘂𝘀 𝗥𝗲𝗽𝗼𝗿𝘁𝘀 → AI collects updates and creates weekly summaries 𝗥𝗶𝘀𝗸 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 → AI spots problems before they become critical 𝗧𝗲𝗮𝗺 𝗔𝘀𝘀𝗶𝗴𝗻𝗺𝗲𝗻𝘁 → AI matches team capacity to tasks automatically 𝗠𝗲𝗲𝘁𝗶𝗻𝗴 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 → AI turns recordings into action items 𝗕𝘂𝗱𝗴𝗲𝘁 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴 → AI predicts overruns and alerts in real-time 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 → AI validates documents and deliverables 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗨𝗽𝗱𝗮𝘁𝗲𝘀 → AI suggests timeline adjustments based on progress Simple projects won't need dedicated PMs. We're already seeing this change. But there's a big opportunity here, too. AI has a major blind spot: → It can't understand the team dynamics. → It can't handle complex stakeholder politics. → It can't connect business goals to motivation. AI can't lead and handle people's problems. The manager role is about leadership, not admin. Want to stay relevant? Here are skills to work on. Learn to handle team dynamics ↳ Navigate politics, egos, and priorities Master stakeholder management ↳ Make sure everyone is aligned Turn business goals into team motivation ↳ Make people excited about the project Direct AI with human context ↳ Give AI the right instructions to work with The PM industry is changing. We must adapt accordingly to stay relevant. ♻️ Repost to help your network learn about AI. Follow Alex Barády for practical AI content.

Focusing on AI’s hype might cost your company millions… (Here’s what you’re overlooking) Every week, new AI tools grab attention—whether it’s copilot assistants or image generators. While helpful, these often overshadow the true economic driver for most companies: AI automation. AI automation uses LLM-powered solutions to handle tedious, knowledge-rich back-office tasks that drain resources. It may not be as eye-catching as image or video generation, but it’s where real enterprise value will be created in the near term. Consider ChatGPT: at its core, there is a large language model (LLM) like GPT-3 or GPT-4, designed to be a helpful assistant. However, these same models can be fine-tuned to perform a variety of tasks, from translating text to routing emails, extracting data, and more. The key is their versatility. By leveraging custom LLMs for complex automations, you unlock possibilities that weren’t possible before. Tasks like looking up information, routing data, extracting insights, and answering basic questions can all be automated using LLMs, freeing up employees and generating ROI on your GenAI investment. Starting with internal process automation is a smart way to build AI capabilities, resolve issues, and track ROI before external deployment. As infrastructure becomes easier to manage and costs decrease, the potential for AI automation continues to grow. For business leaders, identifying bottlenecks that are tedious for employees and prone to errors is the first step. Then, apply LLMs and AI solutions to streamline these operations. Remember, LLMs go beyond text—they can be used in voice, image recognition, and more. For example, Ushur is using LLMs to extract information from medical documents and feed it into backend systems efficiently—a task that was historically difficult for traditional AI systems. (Link in comments) In closing, while flashy AI demos capture attention, real productivity gains come from automating tedious tasks. This is a straightforward way to see returns on your GenAI investment and justify it to your executive team.

I’ve heard hundreds of time management tips over the years, but 90% of them aren’t practical for daily use. Why? Because they’re:   🚫 too theoretical 🚫 too abstract 🚫 too rigid These 5️⃣ are the ones I actually use every day—plus how to boost each one with AI (and the exact prompts I use). 👇 1️⃣ Prioritize ruthlessly Not every task deserves your time. Ask: If I only do one thing today, what will matter most? 🤖 AI prompt: “Here’s my to-do list: [paste list]. Please organize these using the Eisenhower Matrix—urgent vs. important—and suggest which I should do, delegate, defer, or delete.” 2️⃣ Use AI on the $10 task so you can focus on the $10K task If it’s low-impact or repetitive, delegate it to AI. Free yourself up for meaningful work. 🤖 AI prompt: “Here’s a list of my current tasks: [paste list]. For each one, tell me if it’s a $10 task or a $10,000 task. Recommend which I should delegate to AI and which I should prioritize myself.” 3️⃣ Eat the frog Tackle your hardest or highest-impact task first—before distractions set in. 🤖 AI prompt: “Here’s my calendar and to-do list for the week: [paste or describe]. Identify which tasks are most critical and when I’m best positioned (energy-wise or schedule-wise) to tackle them first thing in the day.” 4️⃣ Time-block more than meetings Protect chunks of time for deep, focused work—not just calls. 🤖 AI prompt: “Here’s my weekly calendar: [paste or describe]. Help me find 3 time blocks for deep work. Optimize my schedule to reduce context switching and maximize focus.” 5️⃣ Every ‘yes’ to something trivial is a ‘no’ to something meaningful Practice saying “no” with intention—your time is your most valuable asset. 🤖 AI prompt: “Act as my personal scheduler and productivity coach. I’ll list recent tasks, meetings, or requests. For each one, ask: Does this align with my priorities? What am I giving up by saying yes? Is this the best use of my time? Then recommend whether I should accept, delegate, delay, or decline—and how to respond.” ✨ Real game-changer: I don’t treat AI as a shortcut—I use it as a force multiplier. What’s your go-to time management tip? Drop it below 👇

Stop staring at a blinking cursor. Most productivity advice focuses on personal discipline: • Time blocking • Pomodoro timers • “Eat the frog” They help… but only to a point. If you’re still summarizing meetings, drafting reports, and organizing tasks manually, you’re doing work AI can already handle. Many people talk about using AI. Far fewer have actually embedded it into their daily workflow. That’s where your organization’s approved AI assistant starts to change how work gets done. For many organizations, that might be tools like: • Microsoft 365 Copilot • Google Gemini for Workspace • ChatGPT Enterprise • Claude (If you’re not sure which AI tools are approved in your organization, check with your internal security or IT teams before using them with company data.) These assistants are increasingly integrated into workplace tools to help draft documents, summarize meetings, analyze data, and automate routine work. This isn’t just about speed. It’s about offloading cognitive busywork so you can focus on strategy, decisions, and impact. ⸻ 🚀 Here are 9 ways people are already using enterprise AI at work: 1️⃣ AI Task Capture Auto-generate action items from meetings and messages. 2️⃣ Deep Work Prep Get summarized briefings before you even start. 3️⃣ Meeting Compression Turn a 60-minute meeting into a 2-minute read. 4️⃣ AI Drafting Start from a draft instead of a blank page. 5️⃣ Task Breakdown Turn large projects into clear, executable steps. 6️⃣ Data Analysis Identify trends and visuals without writing complex formulas. 7️⃣ Knowledge Search Search your company’s internal knowledge, not just the web. 8️⃣ Workflow Automation Eliminate repetitive status updates and reports. 9️⃣ Decision Support Use AI as a thinking partner to evaluate risks and options. ⸻ Why “Enterprise-Ready” Matters Public AI tools are everywhere. But enterprise platforms keep data secure, governed, and inside your organization’s environment while still delivering the benefits of modern AI. ⸻ Bottom line You’re not “behind” yet. But if you’re not using tools like these, you’re likely leaving hours of productivity on the table every week. Which of these 9 would save you the most time today? #Productivity #EnterpriseAI #FutureOfWork #AI

Mental Model 1: AI is not a tool. It is a new execution layer. Most enterprises are still approaching AI with a narrow lens: “Let us add a chatbot on top of our systems.” That is understandable. Chatbots are visible. They are easy to demo. They create the first impression of AI. But I believe the more powerful mental model is this: AI is becoming a reasoning and execution layer across enterprise systems. In this model, AI does not just answer questions. It starts to sit across ERP, CRM, HR, finance, supply chain, knowledge systems, workflow platforms, and productivity tools — helping users move from information to action. A mature enterprise AI layer should eventually be able to: Find information. Understand business context. Summarize insights. Recommend the next best action. Execute tasks. Escalate exceptions. Learn from feedback. So the AI maturity path is not simply: Chatbot → Better chatbot It is more likely: Search → Insight → Agent → Workflow → Autonomous Process This distinction matters. Because when AI is treated as a tool, it becomes another application employees have to visit. But when AI becomes an execution layer, it becomes part of how work actually gets done. It can help a sales rep identify the right customer nudge. It can help a finance analyst summarize payment exceptions. It can help a plant manager detect operational risk. It can help a frontline worker complete a process without navigating five different systems. The debate then becomes: Should enterprise AI begin with knowledge search or with task automation? My view: for most enterprises, intelligent search is the safest beachhead. It builds trust. It improves access to enterprise knowledge. It creates adoption. It helps users experience value without immediately changing core processes. But task automation is where ROI becomes more visible. That is where cycle times reduce. Where handoffs shrink. Where exception handling improves. Where productivity gains become measurable. The real opportunity is not choosing one over the other. It is building a progression: Start with trusted knowledge. Move to contextual insights. Introduce agents. Orchestrate workflows. Then automate processes where trust, controls, and value are clear. That is the enterprise AI journey I believe we need to design for. Not AI as a feature. Not AI as a chatbot. But AI as the new execution layer of the enterprise. #EnterpriseAI #AITransformation #AgenticAI #DigitalTransformation #FutureOfWork #GenAI #AIAdoption

These frameworks are 50+ years old. Most people still cannot make them work!! The Eisenhower Matrix? 1954. The Pareto Principle? 1896. Warren Buffett's 5/25 Rule? Decades old. We have read about them. We have studied them. Probably tried to implement some of these. But when Monday hits and your inbox explodes? When everything is marked urgent? When your calendar is back-to-back and your team needs answers now? Those frameworks stay in the bookmark folder. The problem was never knowledge. It was bandwidth. That is where AI changes everything. Not by replacing these frameworks. By finally making them usable. Here is how AI can help (important: you have to give it your real context - your tasks, goals, constraints, thought process etc.): 𝗖𝗹𝗮𝗿𝗶𝗳𝘆 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝗲𝘀 𝗘𝗶𝘀𝗲𝗻𝗵𝗼𝘄𝗲𝗿 𝗠𝗮𝘁𝗿𝗶𝘅 - Paste your task list and ask AI to sort it into the four quadrants based on your role and goals. 𝗣𝗮𝗿𝗲𝘁𝗼 𝟴𝟬/𝟮𝟬 - Ask AI to spot which activities drive the most results vs where your time actually goes. 𝗕𝘂𝗳𝗳𝗲𝘁𝘁'𝘀 𝟱/𝟮𝟱 𝗥𝘂𝗹𝗲 - Share your goals and ask AI to challenge which 5 really deserve focus. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝘁𝗿𝗮𝗱𝗲-𝗼𝗳𝗳𝘀 𝗥𝗜𝗖𝗘 𝗠𝗲𝘁𝗵𝗼𝗱 - Give AI your projects and ask it to score them on Reach, Impact, Confidence and Effort. 𝗠𝗼𝗦𝗖𝗼𝗪 𝗠𝗲𝘁𝗵𝗼𝗱 - Ask AI to sort deliverables into Must, Should, Could and Won't based on your deadline. 𝗔𝗕𝗖𝗗𝗘 𝗠𝗲𝘁𝗵𝗼𝗱 - Paste tomorrow's to-do list and ask AI to label each task A to E with a reason. 𝗔𝗰𝘁 𝘄𝗶𝘁𝗵 𝗳𝗼𝗰𝘂𝘀 𝗘𝗮𝘁 𝗧𝗵𝗮𝘁 𝗙𝗿𝗼𝗴 - Ask AI which task you will procrastinate on and to break it into 3 starting steps. 𝗧𝗶𝗺𝗲 𝗕𝗹𝗼𝗰𝗸𝗶𝗻𝗴 - Ask AI to draft an ideal week around your priorities and energy levels. 𝗕𝗮𝘁𝗰𝗵𝗶𝗻𝗴 - Ask AI to group tasks by type and suggest which belong in the same time block. The thinking stays human. The execution gets faster. This is exactly what we teach in my AI Accelerator — how to use AI to apply strategies smart leaders have known for decades but never had the bandwidth to execute. Because knowing the framework was never the hard part. Using it consistently was. Now you can. AI is one of the core pillars in my Portfolio Career Accelerator. Cohort 1 is working through this right now. If you want to join Cohort 2, we start April 27. Learn about the accelerator here: https://lnkd.in/gidkFZVR or in my Featured section Image credit: Stephanie Hills, Ph.D. - follow her, she is amazing!