Artificial Intelligence

 AI Engineering Stack Our AI practice must be structured around the three layers of the AI application stack to enable scalable and reliable application development Three layers of the AI application stack to enable scalable and reliable application development Application Development AI inference Prompt engineering Context Construction Evaluation Model Development Inference optimization Dataset engineering Modelling & Training Evaluation Infrastructure Compute management Data management Serving Monitoring Below is a detailed elaboration of each layer in the diagram you provided, expanding on the corresponding bullet points on the right side. This can be used directly in a technical presentation , architecture document, or strategy deck for an AI/ML or Agentic Enterprise practice. AI Practice Architecture: Layer-by-Layer Explanation 1. Application Development Layer This layer focuses on building end-user AI applications and agentic systems by orchestrating models, context, and...

 How to detect the laptop battery anomaly through AI/ML Detecting laptop battery anomalies using AI/ML involves gathering data about the battery's performance and applying machine learning models to detect abnormal behavior. The process can be broken down into several key steps: 1. Data Collection To detect anomalies, you need a consistent stream of data regarding the battery's health, charge/discharge cycles, temperature, voltage, current, and other relevant metrics. Common data sources include: Battery health information (e.g., charge cycles, capacity, design capacity, etc.). Charging/discharging patterns (voltage, current, charging time, etc.). System parameters (temperature, CPU load, battery status). Historical battery failure data (if available). You can gather battery data on most laptops using the following methods: Windows : Use powercfg in the command line to generate a battery report. You can also access data from WMIC commands. Linux : You c...

Model Performance Metrics  1. MAE — Mean Absolute Error Formula: MAE = 1 n ∑ i = 1 n ∣ y i − y ^ i ∣ \text{MAE} = \frac{1}{n}\sum_{i=1}^{n} |y_i - \hat{y}_i| MAE = n 1 ​ i = 1 ∑ n ​ ∣ y i ​ − y ^ ​ i ​ ∣ Meaning: It measures the average magnitude of errors in a set of predictions, without considering their direction (positive or negative). Lower is better. It’s in the same units as the target variable. ✅ Intuitive: “On average, my predictions are off by X units.” ⚠️ Downside: It treats all errors equally — large and small errors have the same weight. 2. MSE — Mean Squared Error Formula: MSE = 1 n ∑ i = 1 n ( y i − y ^ i ) 2 \text{MSE} = \frac{1}{n}\sum_{i=1}^{n} (y_i - \hat{y}_i)^2 MSE = n 1 ​ i = 1 ∑ n ​ ( y i ​ − y ^ ​ i ​ ) 2 Meaning: It measures the average squared difference between predicted and actual values. Larger errors have exponentially more weight because of the squaring. ✅ Highlights large errors — useful when large deviations are ...

  Machine Learning Algorithms  are a  set of rules  that help systems learn and make decisions without giving explicit instructions. They analyze data to  find patterns and hidden relationships . And using this information, they make predictions on new data and help solve problems. This  cheatsheet  will cover  most common machine learning algorithms . For example, they can  recognize images, make predictions for the future using the historical data or group similar items together  while continuously learning and improving over time. It is a smart way for computers to evolve and become better at different tasks. There are different types of machine learning algorithms, each used to solve a different kind of problems. They can be divided into 4 types: Supervised Learning Unsupervised Learning Reinforcement Learning Semi-Supervised Learning Supervised Learning Algorithms Supervised Learning  involves training a model on a  labele...