Machine Learning Models For Predictive Analytics

Few Lessons from Deploying and Using LLMs in Production Deploying LLMs can feel like hiring a hyperactive genius intern—they dazzle users while potentially draining your API budget. Here are some insights I’ve gathered: 1. “Cheap” is a Lie You Tell Yourself: Cloud costs per call may seem low, but the overall expense of an LLM-based system can skyrocket. Fixes: - Cache repetitive queries: Users ask the same thing at least 100x/day - Gatekeep: Use cheap classifiers (BERT) to filter “easy” requests. Let LLMs handle only the complex 10% and your current systems handle the remaining 90%. - Quantize your models: Shrink LLMs to run on cheaper hardware without massive accuracy drops - Asynchronously build your caches — Pre-generate common responses before they’re requested or gracefully fail the first time a query comes and cache for the next time. 2. Guard Against Model Hallucinations: Sometimes, models express answers with such confidence that distinguishing fact from fiction becomes challenging, even for human reviewers. Fixes: - Use RAG - Just a fancy way of saying to provide your model the knowledge it requires in the prompt itself by querying some database based on semantic matches with the query. - Guardrails: Validate outputs using regex or cross-encoders to establish a clear decision boundary between the query and the LLM’s response. 3. The best LLM is often a discriminative model: You don’t always need a full LLM. Consider knowledge distillation: use a large LLM to label your data and then train a smaller, discriminative model that performs similarly at a much lower cost. 4. It's not about the model, it is about the data on which it is trained: A smaller LLM might struggle with specialized domain data—that’s normal. Fine-tune your model on your specific data set by starting with parameter-efficient methods (like LoRA or Adapters) and using synthetic data generation to bootstrap training. 5. Prompts are the new Features: Prompts are the new features in your system. Version them, run A/B tests, and continuously refine using online experiments. Consider bandit algorithms to automatically promote the best-performing variants. What do you think? Have I missed anything? I’d love to hear your “I survived LLM prod” stories in the comments!

Most ML systems don’t fail because of poor models. They fail at the systems level! You can have a world-class model architecture, but if you can’t reproduce your training runs, automate deployments, or monitor model drift, you don’t have a reliable system. You have a science project. That’s where MLOps comes in. 🔹 𝗠𝗟𝗢𝗽𝘀 𝗟𝗲𝘃𝗲𝗹 𝟬 - 𝗠𝗮𝗻𝘂𝗮𝗹 & 𝗙𝗿𝗮𝗴𝗶𝗹𝗲 This is where many teams operate today. → Training runs are triggered manually (notebooks, scripts) → No CI/CD, no tracking of datasets or parameters → Model artifacts are not versioned → Deployments are inconsistent, sometimes even manual copy-paste to production There’s no real observability, no rollback strategy, no trust in reproducibility. To move forward: → Start versioning datasets, models, and training scripts → Introduce structured experiment tracking (e.g. MLflow, Weights & Biases) → Add automated tests for data schema and training logic This is the foundation. Without it, everything downstream is unstable. 🔹 𝗠𝗟𝗢𝗽𝘀 𝗟𝗲𝘃𝗲𝗹 𝟭 - 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 & 𝗥𝗲𝗽𝗲𝗮𝘁𝗮𝗯𝗹𝗲 Here, you start treating ML like software engineering. → Training pipelines are orchestrated (Kubeflow, Vertex Pipelines, Airflow) → Every commit triggers CI: code linting, schema checks, smoke training runs → Artifacts are logged and versioned, models are registered before deployment → Deployments are reproducible and traceable This isn’t about chasing tools, it’s about building trust in your system. You know exactly which dataset and code version produced a given model. You can roll back. You can iterate safely. To get here: → Automate your training pipeline → Use registries to track models and metadata → Add monitoring for drift, latency, and performance degradation in production My 2 cents 🫰 → Most ML projects don’t die because the model didn’t work. → They die because no one could explain what changed between the last good version and the one that broke. → MLOps isn’t overhead. It’s the only path to stable, scalable ML systems. → Start small, build systematically, treat your pipeline as a product. If you’re building for reliability, not just performance, you’re already ahead. Workflow inspired by: Google Cloud ---- If you found this post insightful, share it with your network ♻️ Follow me (Aishwarya Srinivasan) for more deep dive AI/ML insights!

Many AI agents look impressive in demos, but crash in real-world production. Why? Because scaling agents requires engineering discipline, not just clever prompts. Moving from prototype to production means tackling memory, observability, scalability, and resilience challenges. Let’s explore the design principles that make AI agents production-ready. 🔸Why AI Agents Fail Monolithic designs, missing scalability, and poor observability often break agents under real-world traffic. 🔸Microservices Architecture Break agents into services like inference, planning, memory, and tools for flexibility and fault tolerance. 🔸Containerization & Orchestration Use containers for packaging and Kubernetes for orchestration. Make it a habit from prototype to multi-agent production. 🔸Message Queues & Async Processing Prevent bottlenecks with task queues, event sourcing, and non-blocking communication. 🔸Continuous Delivery (CI/CD) Automate deployments with a three-stage pipeline for faster, safer updates. 🔸Load Balancing for Real Traffic Distribute 50–5,000+ requests/minute with API gateways, application layers, and service mesh. 🔸Scalable Memory Layer Use Redis for short-term context, SQL/NoSQL for structure, and Vector DBs for knowledge. 🔸Observability & Monitoring Log calls, monitor latency, and enable human-in-the-loop reviews for deeper debugging. The real test for AI agents goes beyond a demo to survive production traffic at scale. Have you had this experience? #AIAgent

Explainable AI strengthens accountability and integrity in automation by making algorithmic reasoning transparent, ensuring fair governance, detecting bias, supporting compliance, and nurturing trust that sustains responsible innovation. Organizations that aim to integrate AI responsibly face a common challenge: understanding how decisions are made by their systems. Without clarity, compliance becomes fragile and ethics remain theoretical. Explainable AI brings visibility into this process, translating complex model logic into a language that regulators, auditors, and executives can actually understand. Transparency is not a luxury. It is a structural requirement for building trust in automated decision-making. When models are explainable, teams can trace outcomes, identify hidden biases, and take timely corrective action before risk escalates. This level of insight also helps align technology with existing regulatory frameworks, from GDPR principles to sector-specific governance standards. Embedding explainability within AI governance frameworks creates a bridge between innovation and responsibility. It helps organizations evolve without compromising accountability, ensuring that progress remains both human-centered and sustainable. #ExplainableAI #EthicalAI #AIGovernance #Compliance #Trust

The promise of large language models is to allow patients and physicians to interact with AI through human-like discussions, text. The promise of machine learning models is to elevate how we deal with repetitive, data-based medical tasks. But what if we combine the two? Authors of a new study developed a Digital Twin—GPT (a sort of LLM) to extend LLM-based forecasting solutions to clinical trajectory prediction. "Benchmarking on non-small cell lung cancer, intensive care unit, and Alzheimer’s disease datasets, DT-GPT outperformed state-of-the-art machine learning models, reducing the scaled mean absolute error by 3.4%, 1.3% and 1.8%, respectively." Essentially, it creates virtual patient “digital twins” from electronic health records to forecast disease progression and treatment outcomes in real time. Source: https://lnkd.in/e2tuu8A5

𝐀 𝐅𝐨𝐫𝐭𝐮𝐧𝐞 𝟓𝟎𝟎 𝐜𝐨𝐦𝐩𝐚𝐧𝐲 𝐆𝐞𝐧𝐀𝐈 𝐃𝐞𝐦𝐨 𝐠𝐨𝐭 𝐚 𝐒𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐎𝐯𝐚𝐭𝐢𝐨𝐧. Two weeks in Production? A Complete Failure. 𝐖𝐡𝐚𝐭 𝐰𝐞𝐧𝐭 𝐰𝐫𝐨𝐧𝐠? Not the model. The system around it. • No observability when outputs went wrong • No fallback when the API hit rate limits • No audit trail for compliance • No cost controls when usage spiked • No way to measure if it was actually helping users This is why shipping GenAI to production is not about models it is about everything around the model. 𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐭𝐡𝐞 𝟓 𝐜𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐝 𝐟𝐨𝐫 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐫𝐞𝐚𝐝𝐲 𝐆𝐞𝐧𝐀𝐈 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬: 𝟏. 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐋𝐚𝐲𝐞𝐫 • Strong data foundations and scalable pipelines • High-quality retrieval with relevance filtering Without clean data and reliable retrieval, the model hallucinates. 𝟐. 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐋𝐚𝐲𝐞𝐫 • Prompt & policy management • Model selection and intelligent routing • Latency, cost, and performance controls This is where you optimize for speed, accuracy, and budget. 𝟑. 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 𝐋𝐚𝐲𝐞𝐫 • Full observability, evaluation, and feedback loops • Human-in-the-loop for critical decisions • Reliability, fallbacks, and continuous improvement If you can not see what is happening, you can not fix what is breaking. 𝟒. 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐋𝐚𝐲𝐞𝐫 • Security, compliance, and audit readiness • Access controls and data protection Enterprise AI dies without governance. Period. 𝟓. 𝐒𝐲𝐬𝐭𝐞𝐦 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 • The model is just one component. • The system is what makes it trustworthy, scalable, and usable. Production GenAI is an engineering discipline not a prompt experiment. 𝐓𝐡𝐞 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲: Most teams fail not because the LLM is weak but because the surrounding capabilities are missing. GenAI success looks less like a demo and more like serious platform engineering. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐡𝐚𝐫𝐝𝐞𝐬𝐭 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐭𝐚𝐤𝐢𝐧𝐠 𝐆𝐞𝐧𝐀𝐈 𝐭𝐨 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) Karuparti for more PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/exc4upeq #GenAI #AIEngineering 

How can we decrease pharmacy spend on high-cost drugs by double digits without worse outcomes? --- Uplift modeling is a common tactic in marketing to target the specific people for a promotion that otherwise wouldn’t buy the product. While marketing in general can lead to overconsumption, in healthcare/#pharmacy, the same mathematical techniques used for uplift modeling could be repurposed to support #PrecisionMedicine or personalized medicine, where the goal is to identify which patients are most likely to benefit from a specific treatment while avoiding unnecessary treatments for patients who might not respond well. Identifying the cohort that is getting most of the outcomes from a drug varies by drug, but some drugs have only a fraction of the total population driving a larger share of clinical results. --- Here's the basic process for using #UpliftModeling (you can find more details from my Milliman white paper in the comments): 1. Treatment: Identify the treatment for which you want to predict response (e.g., a high-cost brand/specialty drug like GLP-1s). This could also be done for a medical device or any intervention. 2. Data collection: Gather comprehensive data and studies about patients, including their medical history, genetic information, and any other relevant attributes. This is often the limiter of building a good model. 3. Control group: Assemble a control group of patients who are similar to those receiving the treatment but are not receiving the treatment themselves. This helps establish a baseline for comparison. 4. Outcome measurement: Measure the effectiveness of the treatment for both the treatment group and the control group. This could involve monitoring health improvements, cardiac events, or other relevant medical outcomes. For FDA-approved drugs, this could come from published research on the “absolute risk reduction” or “number needed to treat.” 5. Model building: Develop predictive models using machine learning algorithms that estimate the likelihood of a positive response to the treatment for each individual. 6. Uplift calculation: Calculate the difference in response rates between the treatment group and the control group to determine the net impact of the treatment. 7. Segment: Divide patients into different segments based on their predicted response probabilities. 8. Action: Use the insights from uplift modeling to guide treatment, coverage, or other decisions. --- A payer or employer can use this information how they’d like, but I imagine it will be used to adjust formularies or utilization management strategies. It could also be used when setting up contracts for how a drug should be used while carving out certain drugs or disease states (e.g. oncology drugs at a center of excellence). There are more potential use cases in the white paper in the comments. --- Would you use this strategy for #PharmacyBenefits or #ValueBasedCare models that take on risk for cost of care?

AI model forecasts risk of 1,000 diseases a decade ahead >> 🔮Scientists have developed Delphi-2M, a generative AI tool that predicts the probability of more than 1,000 medical conditions from cancer and diabetes to cardiovascular and respiratory disease 🔮The model learns from medical histories, lifestyle factors, and the sequence and timing of “events” like diagnoses, smoking, or alcohol use 🔮 It was trained on 400,000 anonymised UK Biobank records and tested on 1.9 million patient records in Denmark, showing strong accuracy across different health systems 🔮 Like a weather forecast, risks are expressed as probabilities over time, with shorter-term predictions proving more reliable than long-range forecasts 🔮Delphi-2M is especially accurate for diseases with consistent progression such as diabetes, heart attacks, and certain cancers, and less so for more variable issues like mental health or pregnancy-related complications 🔮 Unlike current single-disease tools, it can forecast multiple conditions at once and model possible health trajectories up to 20 years 🔮It is likely the tool still is 5–10 years from clinical use but already shows how generative AI could model disease progression and enable more personalised prevention and treatment #digitalhealth #ai

AI explainability is critical for trust and accountability in AI systems. The report “AI Explainability in Practice” highlights key principles and practical steps to ensure AI decisions are transparent, fair, and understandable to diverse stakeholders. Key takeaways: • Explanations in AI can be process-based (how the system was designed and governed) or outcome-based (why a specific decision was made). Both are essential for trust. • Clear, accessible explanations should be tailored to stakeholders’ needs, including non-technical audiences and vulnerable groups such as children. • Transparency and accountability require documenting data sources, model selection, testing, and risk assessments to demonstrate fairness and safety. • Effective AI explainability includes providing rationale, responsibility, safety, fairness, data, and impact explanations. • Use interpretable models where possible, and when black-box models are necessary, supplement with interpretability tools to explain decisions at both local and global levels. • Implementers should be trained to understand AI limitations and risks and to communicate AI-assisted decisions responsibly. • For AI systems involving children, additional care is required for transparent, age-appropriate explanations and protecting their rights throughout the AI lifecycle. This framework helps organizations design and deploy AI that stakeholders can trust and engage with meaningfully. #AIExplainability #ResponsibleAI #HealthcareInnovation Peter Slattery, PhD The Alan Turing Institute

Why would your users distrust flawless systems? Recent data shows 40% of leaders identify explainability as a major GenAI adoption risk, yet only 17% are actually addressing it. This gap determines whether humans accept or override AI-driven insights. As founders building AI-powered solutions, we face a counterintuitive truth: technically superior models often deliver worse business outcomes because skeptical users simply ignore them. The most successful implementations reveal that interpretability isn't about exposing mathematical gradients—it's about delivering stakeholder-specific narratives that build confidence. Three practical strategies separate winning AI products from those gathering dust: 1️⃣ Progressive disclosure layers Different stakeholders need different explanations. Your dashboard should let users drill from plain-language assessments to increasingly technical evidence. 2️⃣ Simulatability tests Can your users predict what your system will do next in familiar scenarios? When users can anticipate AI behavior with >80% accuracy, trust metrics improve dramatically. Run regular "prediction exercises" with early users to identify where your system's logic feels alien. 3️⃣ Auditable memory systems Every autonomous step should log its chain-of-thought in domain language. These records serve multiple purposes: incident investigation, training data, and regulatory compliance. They become invaluable when problems occur, providing immediate visibility into decision paths. For early-stage companies, these trust-building mechanisms are more than luxuries. They accelerate adoption. When selling to enterprises or regulated industries, they're table stakes. The fastest-growing AI companies don't just build better algorithms - they build better trust interfaces. While resources may be constrained, embedding these principles early costs far less than retrofitting them after hitting an adoption ceiling. Small teams can implement "minimum viable trust" versions of these strategies with focused effort. Building AI products is fundamentally about creating trust interfaces, not just algorithmic performance. #startups #founders #growth #ai