Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
DataSpoof
Kanalga Telegram’da o‘tish
Learn Data Science https://dataspoof4081.graphy.com/membership Artificial Intelligence Machine Learning Data Science Deep learning Computer vision NLP Big data
Ko'proq ko'rsatish📈 Telegram kanali DataSpoof analitikasi
DataSpoof (@dataspoof) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 16 138 obunachidan iborat bo'lib, Taʼlim toifasida 12 559-o'rinni va Hindiston mintaqasida 26 707-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 16 138 obunachiga ega bo‘ldi.
20 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -151 ga, so‘nggi 24 soatda esa 0 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 7.89% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining N/A% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 0 marta ko‘riladi; birinchi sutkada odatda 0 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 0 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent api, llm, pipeline, +9183182, engineer kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Learn Data Science
https://dataspoof4081.graphy.com/membership
Artificial Intelligence
Machine Learning
Data Science
Deep learning
Computer vision
NLP
Big data”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 21 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
16 138
Obunachilar
Ma'lumot yo'q24 soatlar
-397 kunlar
-15130 kunlar
Postlar arxiv
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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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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.
16 139
Who will be interested in live recording class of AWS in Hindi. It's more than 50 hour
16 139
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
16 139
AWS #video3
* Like it and subscribe to our channel for daily aws content
https://youtu.be/BaVQoUnWAYE?si=rMjSq_ckC-oBi3hQ
16 139
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
16 139
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
16 139
Pdoc is a python library which help us to visualize the code step by step.
pip install pdoc3
How to run
pdoc filename.py
