https://learn.pranaymahendrakar.com/ Free, beginner-friendly documentation for AI developers. Machine Learning, Deep Learning, LLMs, Computer Vision, NLP, Generative AI and MLOps — explained in very simple words. en Wed, 02 Sep 2026 00:05:00 +0530 Wed, 02 Sep 2026 00:05:00 +0530 learn.pranaymahendrakar.com https://learn.pranaymahendrakar.com/learn/scaling-and-traffic/autoscaling-on-the-right-metric https://learn.pranaymahendrakar.com/learn/scaling-and-traffic/autoscaling-on-the-right-metric Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Scaling and Traffic Management mlops scaling autoscaling metrics production Autoscaling adds and removes machines automatically, but only helps if it watches the number that actually predicts trouble. https://learn.pranaymahendrakar.com/learn/text-evaluation/bleu-and-translation-metrics https://learn.pranaymahendrakar.com/learn/text-evaluation/bleu-and-translation-metrics Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Evaluating Text Systems nlp evaluation translation bleu metrics BLEU scores a translation by counting matching word chunks against a reference, which is fast but blind to correct paraphrases. https://learn.pranaymahendrakar.com/learn/classical-nlp/bag-of-words https://learn.pranaymahendrakar.com/learn/classical-nlp/bag-of-words Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Classical NLP That Still Works nlp classical-nlp bag-of-words vectorization scikit-learn Bag of words turns a sentence into a list of word counts, throwing away word order but keeping enough signal to search and classify text cheaply. https://learn.pranaymahendrakar.com/learn/messy-text/spelling-correction https://learn.pranaymahendrakar.com/learn/messy-text/spelling-correction Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Messy Real-World Text nlp messy-text spelling edit-distance A spell checker guesses the intended word from a misspelled one by finding the closest real word, the same way you guess a word mumbled in a noisy market. https://learn.pranaymahendrakar.com/learn/chunking-long-documents/chunking-strategies https://learn.pranaymahendrakar.com/learn/chunking-long-documents/chunking-strategies Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Chunking and Long Documents nlp rag chunking retrieval text-splitting Chunking cuts a long document into smaller pieces so a search system can find and hand over only the part that answers a question, and the cutting method decides whether those pieces still make sense. https://learn.pranaymahendrakar.com/learn/medical-imaging/dicom-and-medical-images https://learn.pranaymahendrakar.com/learn/medical-imaging/dicom-and-medical-images Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Medical Imaging AI medical-imaging dicom ct windowing A medical scan is not a photograph — its pixels store a physical measurement, and you choose which slice of that range to actually look at. https://learn.pranaymahendrakar.com/learn/batching-and-throughput/dynamic-batching https://learn.pranaymahendrakar.com/learn/batching-and-throughput/dynamic-batching Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Batching and Concurrency mlops batching throughput serving fastapi Dynamic batching holds a few incoming requests for a short moment so one model call can serve all of them together, trading a little latency for a lot more throughput. https://learn.pranaymahendrakar.com/learn/caching-and-cost/training-cost-estimation https://learn.pranaymahendrakar.com/learn/caching-and-cost/training-cost-estimation Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Caching and Cost Control cost training estimation budgeting gpu mlops Before you start a training run, you can work out roughly how long it will take and what it will cost, the same way a contractor quotes a job before picking up a tool. https://learn.pranaymahendrakar.com/learn/question-answering/extractive-qa https://learn.pranaymahendrakar.com/learn/question-answering/extractive-qa Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Question Answering nlp question-answering bert squad extractive-qa Extractive QA finds the exact words that answer a question inside a given passage, instead of writing a new sentence from scratch. https://learn.pranaymahendrakar.com/learn/summarisation/extractive-summarisation https://learn.pranaymahendrakar.com/learn/summarisation/extractive-summarisation Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Summarisation nlp summarisation extractive textrank information-retrieval Extractive summarisation builds a summary by picking the most important sentences straight out of the original text, without writing a single new word. https://learn.pranaymahendrakar.com/learn/accessibility-ai/alt-text-generation https://learn.pranaymahendrakar.com/learn/accessibility-ai/alt-text-generation Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar AI for Accessibility alt-text accessibility accessibility-ai computer-vision screen-readers Alt text generation describes an image in words for a screen reader, and the honest version says less rather than guessing wrong with confidence. https://learn.pranaymahendrakar.com/learn/text-embeddings/glove https://learn.pranaymahendrakar.com/learn/text-embeddings/glove Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar The Embedding Family nlp text-embeddings glove word-vectors embeddings GloVe learns a number list for every word by counting, across an entire corpus, how often word pairs appear near each other, then compressing those counts into vectors. https://learn.pranaymahendrakar.com/learn/multilingual-nlp/neural-machine-translation https://learn.pranaymahendrakar.com/learn/multilingual-nlp/neural-machine-translation Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Multilingual and Indic NLP nlp multilingual translation seq2seq attention Neural machine translation reads a whole sentence into one model, then generates the translation word by word, using attention to look back at the right source words. https://learn.pranaymahendrakar.com/learn/manufacturing-ai/industrial-sensor-data https://learn.pranaymahendrakar.com/learn/manufacturing-ai/industrial-sensor-data Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Manufacturing and Predictive Maintenance manufacturing sensors historian industrial-data iot A factory's sensors report on different schedules and occasionally freeze or drift, so the raw data has to be aligned and checked before any model can trust it. https://learn.pranaymahendrakar.com/learn/semantic-search/keyword-vs-semantic-search https://learn.pranaymahendrakar.com/learn/semantic-search/keyword-vs-semantic-search Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Semantic Search and Reranking semantic-search keyword-search bm25 embeddings information-retrieval Keyword search matches the exact words you typed. Semantic search matches what you meant. The difference shows up the moment your words don't match the document's words. https://learn.pranaymahendrakar.com/learn/latency-and-load-testing/latency-budgets https://learn.pranaymahendrakar.com/learn/latency-and-load-testing/latency-budgets Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Latency, Load Testing and Capacity mlops latency performance serving budgeting A latency budget splits your total allowed response time across every step of a request, so you know exactly which step is eating the most time before it becomes a problem. https://learn.pranaymahendrakar.com/learn/legal-compliance-ai/legal-documents-as-data https://learn.pranaymahendrakar.com/learn/legal-compliance-ai/legal-documents-as-data Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Legal and Compliance AI nlp legal-ai contracts text-preprocessing Legal documents are numbered and hierarchical rather than free-flowing prose, and that numbering is the most reliable structure available for processing them. https://learn.pranaymahendrakar.com/learn/bert-family/masked-language-modelling https://learn.pranaymahendrakar.com/learn/bert-family/masked-language-modelling Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar The BERT Family masked-language-modelling bert pretraining mlm self-supervised Masked language modelling trains a model by hiding random words and asking it to guess them from context on both sides, which is how BERT learned language without any human-written labels. https://learn.pranaymahendrakar.com/learn/production-monitoring/measuring-data-drift https://learn.pranaymahendrakar.com/learn/production-monitoring/measuring-data-drift Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Monitoring Models in Production mlops monitoring drift data-quality psi Data drift is when the inputs a live model sees stop looking like the inputs it was trained on, and PSI is the simplest number that tells you how much. https://learn.pranaymahendrakar.com/learn/model-registry-and-reproducibility/model-registry https://learn.pranaymahendrakar.com/learn/model-registry-and-reproducibility/model-registry Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Registries, Artifacts and Environments mlops registry reproducibility versioning A model registry is the one place that lists every trained version of a model, and which one is actually live. https://learn.pranaymahendrakar.com/learn/ml-incident-response/on-call-for-ml-systems https://learn.pranaymahendrakar.com/learn/ml-incident-response/on-call-for-ml-systems Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Incident Response for ML Systems mlops incident-response on-call alerting sre On-call means someone is reachable and ready to act when a model-serving system breaks, on a rotating schedule, so a 2 a.m. failure never depends on luck. https://learn.pranaymahendrakar.com/learn/sequence-labelling/pos-tagging https://learn.pranaymahendrakar.com/learn/sequence-labelling/pos-tagging Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Sequence Labelling and Structure pos-tagging sequence-labelling spacy grammar nlp Part-of-speech tagging labels every word with its grammatical role in that exact sentence, which is the first structural clue almost every later NLP step depends on. https://learn.pranaymahendrakar.com/learn/production-feature-pipelines/point-in-time-correctness https://learn.pranaymahendrakar.com/learn/production-feature-pipelines/point-in-time-correctness Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Feature and Data Pipelines in Production feature-pipelines mlops data-leakage feature-store Point-in-time correctness means every number shown to a model was actually known at that exact moment in the past, never filled in using something from later. https://learn.pranaymahendrakar.com/learn/finance-ai/financial-data-and-point-in-time https://learn.pranaymahendrakar.com/learn/finance-ai/financial-data-and-point-in-time Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar AI in Finance and Fraud finance point-in-time data-leakage look-ahead-bias finance-ai Financial numbers change after they are first published, so a model must only ever see the version that existed on that day, never the corrected one. https://learn.pranaymahendrakar.com/learn/retail-supply-chain/retail-demand-data https://learn.pranaymahendrakar.com/learn/retail-supply-chain/retail-demand-data Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Retail, Demand and Supply Chain retail demand-forecasting supply-chain time-series data Retail demand data is a giant table of what sold, where and when, and its shape decides everything you can build on top of it. https://learn.pranaymahendrakar.com/learn/robotics-control/robot-sensors-and-time-sync https://learn.pranaymahendrakar.com/learn/robotics-control/robot-sensors-and-time-sync Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Robotics, Control and Autonomy robotics sensors time-sync lidar imu A robot's sensors each run on their own clock, so their data is unreliable together until it is lined up in time. https://learn.pranaymahendrakar.com/learn/cybersecurity-ai/security-telemetry-basics https://learn.pranaymahendrakar.com/learn/cybersecurity-ai/security-telemetry-basics Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar AI in Cybersecurity cybersecurity telemetry logs security-data cybersecurity-ai Every login, connection and file access leaves a record — security telemetry is that flood of records, and it is the raw material every security model is built from. https://learn.pranaymahendrakar.com/learn/opinion-mining/fine-grained-sentiment https://learn.pranaymahendrakar.com/learn/opinion-mining/fine-grained-sentiment Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Sentiment, Opinion and Text Mining nlp sentiment-analysis fine-grained text-classification Fine-grained sentiment rates text on a scale, like one to five stars, instead of forcing every opinion into only positive or negative. https://learn.pranaymahendrakar.com/learn/model-serving-in-production/fastapi-model-server https://learn.pranaymahendrakar.com/learn/model-serving-in-production/fastapi-model-server Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Serving Models in Production model-serving fastapi mlops concurrency Taking a FastAPI model server from working to production-ready means understanding how it handles many requests arriving at once, not repeating the basics of loading and validation. https://learn.pranaymahendrakar.com/learn/releasing-models/shadow-deployment https://learn.pranaymahendrakar.com/learn/releasing-models/shadow-deployment Wed, 02 Sep 2026 00:05:00 +0530 Pranay Mahendrakar Releasing Models Safely releasing-models shadow-deployment mlops deployment testing A shadow deployment lets a new model answer every real request in secret, alongside the model actually serving users, so you can compare them honestly without ever risking a wrong answer reaching anyone.