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 Education12 559...

📈 Analytical overview of Telegram channel DataSpoof

Channel DataSpoof (@dataspoof) in the English language segment is an active participant. Currently, the community unites 16 138 subscribers, ranking 12 559 in the Education category and 26 707 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 16 138 subscribers.

According to the latest data from 20 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -151 over the last 30 days and by 0 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.89%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
Post reach: On average, each post receives 0 views. Within the first day, a publication typically gains 0 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
Thematic interests: Content is focused on key topics such as api, llm, pipeline, +9183182, engineer.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Learn Data Science https://dataspoof4081.graphy.com/membership Artificial Intelligence Machine Learning Data Science Deep learning Computer vision NLP Big data”

Thanks to the high frequency of updates (latest data received on 21 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

16 138

Subscribers

No data24 hours

-397 days

-15130 days

1 273

Post views

No data24 hours

No data48 hours

7.89%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

16 139

[ 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/