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Here is a draft blog post on Swipe Keypad ML: Why Swipe keypad, also known as swipe-to-type, revolutionized the way we interact with mobile devices. Its impact was significant, transforming long-form entry on mobile apps and websites. In this post, we’ll delve into the technical details of how machine learning and NLP enabled this seamless interaction. Introduction Before the advent of swipe keypad, users had to tediously tap individual characters on their mobile keyboards. This process was slow, cumbersome, and prone to errors. The introduction of swipe-to-type changed everything. It allowed users to enter long-form text by simply swiping their fingers across the screen. This transformation was made possible by machine learning and NLP techniques. ...

You’re absolutely right. Traditional ML, deep learning (DL), and transformer-led large language models (LLMs) each have distinct requirements for pipelines and workflows because of their unique demands on data, compute, and deployment processes. Let’s break down the differences: 1. Traditional Machine Learning Pipelines Traditional ML workflows focus on structured data and simpler algorithms. These pipelines are less compute-intensive but require more effort in data preparation and feature engineering. Pipeline Characteristics: Input Data: Structured/tabular data from sources like databases, CRMs, or spreadsheets. Data Preparation: Heavy reliance on manual feature engineering (e.g., creating new columns from existing data). Pipelines include cleaning, normalization, and splitting data into training/testing sets. Model Training: Use of lightweight algorithms (e.g., linear regression, decision trees, random forests). Relatively low computational requirements compared to DL or LLMs. Deployment: Models are often static and deployed in batch workflows or simple REST APIs. Requires minimal monitoring for model drift or performance degradation. Pipeline Example: Tools: Scikit-learn, XGBoost, Alteryx. Workflow: Ingest sales data from Snowflake. Perform feature engineering (e.g., creating “total sales” from raw transaction data). Train a gradient-boosted model to predict customer churn. Deploy to a lightweight API for periodic batch predictions. 2. Deep Learning (DL) Pipelines Deep learning pipelines handle high-dimensional, unstructured data (images, audio, video) and rely on large neural networks like CNNs or RNNs. These workflows are more complex and compute-intensive. ...

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Speculative decoding is a technique used to speed up the inference process in large language models. It essentially reduces the time to generate responses by predicting multiple tokens at once and then verifying those predictions in parallel. Here’s how it works and why it’s useful: How Speculative Decoding Works 1. Generate “Speculative” Tokens: Instead of generating one token at a time, speculative decoding involves generating multiple tokens (a batch or chunk) at once using a smaller, faster model. This smaller model is trained to mimic the output of the larger model but at a much faster rate. ...