Search Results for "lifecycle"

Device Activity Tracker is a platform created to monitor and log the activity of digital devices across networks, giving users visibility into usage patterns, connection events, app launches, and interaction timelines that can be applied for security monitoring, parental oversight, productivity tracking, or device lifecycle analytics. It integrates with devices via sensors or APIs, continually capturing activity metrics and reporting them to a centralized dashboard that visualizes patterns over time, highlights anomalies, and correlates events across systems or users. Because it is designed with privacy and transparency in mind, the tracker offers configurable retention policies and granular consent controls, ensuring administrators can tailor what gets logged and how long data is stored.

The Red Hat Ansible Automation Workshops project is intended for effectively demonstrating Ansible's capabilities through instructor-led workshops or self-paced exercises. These interactive learning scenarios provide you with a pre-configured Ansible Automation Platform environment to experiment, learn, and see how the platform can help you solve real-world problems. The environment runs entirely in your browser, enabling you to learn more about our technology at your pace and time. The...

...The repository serves as both a learning resource and a reference, combining explanations, examples, and structured insights into a cohesive framework. It is especially useful for engineers transitioning into AI-focused roles who need to understand the full lifecycle of AI applications.

...The roadmap is organized to guide learners systematically: starting with Python fundamentals and math/statistics, then progressing through classical machine-learning, deep-learning, data preprocessing, feature engineering, and onto domain-specific applications like computer vision or NLP, ending with deployment, real-world project construction, and best practices for production readiness. What makes it particularly valuable is its holistic nature: rather than focusing only on modeling or theory, it also addresses the broader lifecycle of data-science work, data ingestion, cleaning, EDA, feature engineering, model building, validation, deployment, etc.

...It delivers a carefully curated, yet extensible toolkit in a single compressed filesystem (.ress archive) that runs on Linux® via chroot and on Windows® via the Windows Subsystem for Linux (WSL2). The Windows installer deploys a command-line management utility for zero-friction environment lifecycle operations. Built on Debian GNU/Linux, Ouress anchors its package manager to fixed-timestamp Debian snapshot repositories for reproducible, temporally stable analysis pipelines. The WSL command-line utility is built using the Lazarus IDE and Free Pascal Compiler.

The data-science-on-gcp repository is a comprehensive collection of code examples and end-to-end workflows that accompany the book Data Science on the Google Cloud Platform, designed to teach developers how to build scalable data science and machine learning systems using Google Cloud services. It provides structured, chapter-aligned implementations that guide users through the full lifecycle of a data science project, including data ingestion, storage, processing, analysis, model training, and deployment. The repository is organized into multiple directories that reflect real-world pipelines, such as ingesting data, running SQL-based analytics, streaming data processing, using Spark and Dataproc, applying BigQuery ML, and deploying models with Vertex AI. ...