DataSpoof

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Learn Data Science https://dataspoof4081.graphy.com/membership Artificial Intelligence Machine Learning Data Science Deep learning Computer vision NLP Big data

India26 707 Educación12 559...

📈 Análisis del canal de Telegram DataSpoof

El canal DataSpoof (@dataspoof) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 16 138 suscriptores, ocupando la posición 12 559 en la categoría Educación y el puesto 26 707 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 16 138 suscriptores.

Según los últimos datos del 20 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -151, y en las últimas 24 horas de 0, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 7.89%. Durante las primeras 24 horas tras publicar, el contenido suele obtener N/A% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 0 visualizaciones. En el primer día suele acumular 0 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 0.
Intereses temáticos: El contenido se centra en temas clave como api, llm, pipeline, +9183182, engineer.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Learn Data Science https://dataspoof4081.graphy.com/membership Artificial Intelligence Machine Learning Data Science Deep learning Computer vision NLP Big data”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 21 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.

16 138

Suscriptores

Sin datos24 horas

-397 días

-15130 días

1 273

Visitas de la publicación

Sin datos24 horas

Sin datos48 horas

7.89%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

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[ Power Query ] - cheatsheet.pdf1.07 KB

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10 great Python packages for Data Science not known to many: 1️⃣ CleanLab Cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset. 2️⃣ LazyPredict A Python library that enables you to train, test, and evaluate multiple ML models at once using just a few lines of code. 3️⃣ Lux A Python library for quickly visualizing and analyzing data, providing an easy and efficient way to explore data. 4️⃣ PyForest A time-saving tool that helps in importing all the necessary data science libraries and functions with a single line of code. 5️⃣ PivotTableJS PivotTableJS lets you interactively analyse your data in Jupyter Notebooks without any code 🔥 6️⃣ Drawdata Drawdata is a python library that allows you to draw a 2-D dataset of any shape in a Jupyter Notebook. 7️⃣ black The Uncompromising Code Formatter 8️⃣ PyCaret An open-source, low-code machine learning library in Python that automates the machine learning workflow. 9️⃣ PyTorch-Lightning by @LightningAI Streamlines your model training, automates boilerplate code, and lets you focus on what matters: research & innovation. 🔟 Streamlit A framework for creating web applications for data science and machine learning projects, allowing for easy and interactive data viz & model deployment.

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Linux Essential Operations - CheatSheet.pdf1.09 KB

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MLOPS Tools available in Market 1. Version Control and Experiment Tracking: - DVC (Data Version Control): Manages datasets and models using version control, similar to how Git handles code. - MLflow: An open-source platform to manage the ML lifecycle, including experiment tracking, model versioning, and deployment. - Weights & Biases: Offers experiment tracking, model management, and visualization tools. 2. Model Deployment: - Kubeflow: An open-source toolkit that runs on Kubernetes, designed to make deployments scalable and portable. - AWS SageMaker: Amazon’s fully managed service that provides tools for building, training, and deploying machine learning models at scale - TensorFlow Serving: A flexible, high-performance serving system for machine learning models, designed for production environments. 3. CI/CD for Machine Learning: - GitHub Actions: Automates CI/CD pipelines for machine learning projects, integrating with other MLOps tools. - Jenkins: An automation server that can be customized to manage CI/CD pipelines for machine learning. 4. Model Monitoring and Management: - Prometheus & Grafana: Combined, they provide powerful monitoring and alerting solutions, often used for ML model monitoring. - Seldon Core: An open-source platform for deploying, scaling, and managing thousands of machine learning models on Kubernetes. 5. Data Pipeline Management: - Apache Airflow: An open-source platform to programmatically author, schedule, and monitor workflows. - Prefect: A modern workflow orchestration tool that handles complex data pipelines, including those involving ML models.

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Who will be interested in live recording class of AWS in Hindi. It's more than 50 hour

Anonymous voting

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https://yt.openinapp.co/cz8vq

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https://youtu.be/tHtoyLKonHo?si=GwmVbhT8KiODlxxc

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Lifecycle policies in S3 bucket Purpose: Automate the management of objects in S3 buckets over time. Components: Define rules based on object age or storage class. Actions: Transition objects to different storage classes or delete them. Benefits: Reduce storage costs, optimize performance, and ensure compliance. https://youtu.be/XkDu-haZgvc?si=99Ci3blI9lb0hTpA

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A python library to call all LLM API

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AWS #video3 * Like it and subscribe to our channel for daily aws content https://youtu.be/BaVQoUnWAYE?si=rMjSq_ckC-oBi3hQ

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Build delightful web apps quickly in Python using Google Mesop library https://github.com/google/mesop

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AWS #video2 * Like It and subscribe to our channel for daily aws content https://youtu.be/LSMLtBBfNzE?si=fbRaXrx18wUlESw3

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Giveaway Complete AWS for data science will be totally free on our YouTube channel. * On next 100 days 100 videos will be published * Like it and subscribe to our channel * Daily video will be released at 7pm ist https://youtu.be/Bzrf13Xd-KU?si=1m1M4lsA6ePDKqKs

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Pdoc is a python library which help us to visualize the code step by step. pip install pdoc3 How to run pdoc filename.py

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ETL Using Pyspark.pdf2.23 MB

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100 Data Engineering Interview Questions.pdf8.59 MB

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IMG-20240704-WA0004.jpg0.99 KB

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https://www.dataspoof.info/post/lifecycle-policies-in-s3-bucket/