AI and ML

Computer Vision and Speech Processing for Real World Apps Real world apps combine what a camera sees with what a microphone hears. Vision and speech systems can work together to improve user experiences, automate tasks, and help people. This article shares practical steps to build reliable, respectful solutions that work outside labs. Common challenges appear in the real world. Lighting changes, different angles, and busy backgrounds upset vision models. Noise and overlapping speech make speech harder to hear. Devices have limited power, memory, and sometimes poor networks. Privacy and data protection must be planned from the start. ...

AI debugging and model monitoring AI debugging and model monitoring mix software quality work with data-driven observability. Models in production face data shifts, new user behavior, and labeling quirks that aren’t visible in development. The goal is to detect problems early, explain surprises, and keep predictions reliable, fair, and safe for real users. What to monitor helps teams act fast. Track both system health and model behavior. Latency and reliability: response time, error rate, timeouts. Throughput and uptime: how much work the system handles over time. Prediction errors: discrepancies with outcomes when labels exist. Data quality: input schema changes, missing values, corrupted features. Data drift: shifts in input distributions compared with training data. Output drift and calibration: changes in predicted probabilities versus reality. Feature drift: shifts in feature importance or value ranges. Resource usage: CPU, memory, GPU, and memory leaks. Incidents and alerts: correlate model issues with platform events. How to instrument effectively is essential. Start with a simple observability stack. ...

Speech Recognition: From Algorithms to Apps Speech recognition has moved from research labs into everyday apps. Today, many products use voice to save time, boost accessibility, and connect people with technology more naturally. With careful design, you can bring reliable speech features to phones, desktops, or devices at home. How the technology works Most systems rely on three parts: acoustic models, language models, and decoders. The acoustic model turns sound into a sequence of sounds or phonemes. The language model helps choose word sequences that fit the context. The decoder ties these pieces together and outputs the final text, balancing accuracy and speed. ...

Deep Learning Accelerators: GPUs and TPUs Modern AI work often relies on specialized hardware to speed up work. GPUs and TPUs are the two big families of accelerators. They are built to handle large neural networks, but they do it in different ways. Choosing the right one can save time, money, and energy. GPUs at a glance They are flexible and work well with many models and frameworks. They have many cores and high memory bandwidth, which helps with large data and complex operations. They support mixed precision, using smaller numbers to run faster without losing accuracy in many tasks. Software is broad: CUDA and cuDNN on NVIDIA GPUs power popular stacks like PyTorch and TensorFlow. TPUs at a glance ...

Speech Processing for Voice Apps and Assistants Speech processing is the backbone of modern voice apps and assistants. It turns sound into useful actions. Three parts work together: Automatic Speech Recognition (ASR) converts speech to text; Natural Language Understanding (NLU) finds the user’s intent; Text-To-Speech (TTS) turns a text reply into spoken words. The better these parts work, the easier the app is to use, even in noisy rooms or during a busy morning. ...

Computer Vision and Speech Processing at Scale Building computer vision and speech systems at scale means more than bigger models. It requires clean data, stable tools, and predictable performance across devices and users. When vision and speech share a common workflow, teams can deliver features like searchable video, live captions, and voice-enabled images. The aim is end-to-end reliability as data grows. Data pipelines fuel scale. Start with labeled data for vision tasks and for speech tasks. Use a data lake with raw media, transcripts, and labels, plus strong versioning and privacy controls. Add automated quality checks, human review, and feedback loops so labels stay accurate. Include synthetic data to cover rare cases and test edge conditions while keeping labeling costs reasonable. ...

Edge AI: On-Device Intelligence at Power and Speed Edge AI means running AI models directly on devices such as smartphones, cameras, sensors, and wearables. This brings intelligence closer to users, so apps respond faster, work offline, and keep data private. You can often avoid sending raw data to the cloud, reducing risk and bandwidth. Why on-device intelligence matters On-device inference delivers real-time responses and more reliable performance. It helps when internet access is slow or unstable, and it reduces cloud costs. Local processing also strengthens privacy, since sensitive data stays on the device. ...

Hardware Accelerators: GPUs, TPUs, and Beyond Hardware accelerators unlock speed for AI, graphics, and data tasks. They come in several forms, from general GPUs to purpose-built chips. This guide explains how GPUs, TPUs, and other accelerators fit into modern systems, and how to choose the right one for your workload. GPUs are designed for parallel work. They hold thousands of small cores and offer high memory bandwidth. They shine in training large neural networks, running complex simulations, and accelerating data pipelines. In many setups, a CPU handles control while one or more GPUs do the heavy lifting. Software libraries and drivers help map tasks to the hardware, making it easier to use parallel compute without manual tuning. ...