DataSpoof

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.

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.

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

A python library to call all LLM API

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

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

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

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

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