#python #big_data #data_engineering #data_quality #data_science #feature_store #features #machine_learning #ml #mlops

https://github.com/feast-dev/feast

#jupyter_notebook #automated_machine_learning #automl #classification #data_science #deep_learning #finetuning #hyperparam #hyperparameter_optimization #jupyter_notebook #machine_learning #natural_language_generation #natural_language_processing #python #random_forest #regression #scikit_learn #tabular_data #timeseries_forecasting #tuning

https://github.com/microsoft/FLAML

#python #data_mining #data_science #forecasting #machine_learning #scikit_learn #time_series #time_series_analysis #time_series_classification #time_series_regression

https://github.com/sktime/sktime

#python #ai #automl #data_science #deep_learning #devops_tools #hacktoberfest #llm #llmops #machine_learning #metadata_tracking #ml #mlops #pipelines #production_ready #pytorch #tensorflow #workflow #zenml

https://github.com/zenml-io/zenml

#go #data_science #deep_learning #distributed_training #hyperparameter_optimization #hyperparameter_search #hyperparameter_tuning #kubernetes #machine_learning #ml_infrastructure #ml_platform #mlops #pytorch #tensorflow

https://github.com/determined-ai/determined

#typescript #analytics #apache #apache_superset #asf #bi #business_analytics #business_intelligence #data_analysis #data_analytics #data_engineering #data_science #data_visualization #data_viz #flask #python #react #sql_editor #superset

Superset is a powerful business intelligence tool that helps you explore and visualize data easily. It offers a no-code interface for building charts, a robust SQL Editor for advanced queries, and support for nearly any SQL database or data engine. You can create beautiful visualizations, define custom dimensions and metrics quickly, and use a lightweight caching layer to reduce database load. Superset also provides extensible security roles and authentication options, an API for customization, and a cloud-native architecture designed for scale. This makes it easier to analyze and present your data in a user-friendly way, replacing or augmenting proprietary BI tools effectively.

https://github.com/apache/superset

#python #data_analysis #data_science #data_visualization #deep_learning #deploy #gradio #gradio_interface #hacktoberfest #interface #machine_learning #models #python #python_notebook #ui #ui_components

Gradio is a Python package that helps you quickly build and share web demos for your machine learning models or any Python function. You don't need to know JavaScript, CSS, or web hosting to use it. With just a few lines of Python code, you can create a demo and share it via a public link. Gradio offers various tools like the `Interface` class for simple demos, `ChatInterface` for chatbots, and `Blocks` for more complex custom applications. It also allows easy sharing of your demos with others by generating a public URL in seconds. This makes it easy to showcase your work without technical hassle.

https://github.com/gradio-app/gradio

#jupyter_notebook #data_analysis #data_science #data_visualization #pandas #python

This curriculum is designed to help beginners learn data science over 10 weeks with 20 detailed lessons. Each lesson includes pre- and post-lesson quizzes, step-by-step guides, knowledge checks, and assignments to ensure you retain the information. You'll learn about data ethics, statistics, working with different types of data, data visualization, and the entire data science lifecycle. The project-based approach helps you build practical skills while learning. Additionally, there are resources for students and teachers to make the learning process flexible and engaging. This curriculum is beneficial because it provides a structured and interactive way to gain hands-on experience in data science, making it easier to understand and apply these skills in real-world scenarios.

https://github.com/microsoft/Data-Science-For-Beginners

#python #analytics #dagster #data_engineering #data_integration #data_orchestrator #data_pipelines #data_science #etl #metadata #mlops #orchestration #python #scheduler #workflow #workflow_automation

Dagster is a tool that helps you manage and automate your data workflows. You can define your data assets, like tables or machine learning models, using Python functions. Dagster then runs these functions at the right time and keeps your data up-to-date. It offers features like integrated lineage and observability, making it easier to track and manage your data. This tool is useful for every stage of data development, from local testing to production, and it integrates well with other popular data tools. Using Dagster, you can build reusable components, spot data quality issues early, and scale your data pipelines efficiently. This makes your work more productive and helps maintain control over complex data systems.

https://github.com/dagster-io/dagster

#jupyter_notebook #aws #data_science #deep_learning #examples #inference #jupyter_notebook #machine_learning #mlops #reinforcement_learning #sagemaker #training

SageMaker-Core is a new Python SDK for Amazon SageMaker that makes it easier to work with machine learning resources. It provides an object-oriented interface, which means you can manage resources like training jobs, models, and endpoints more intuitively. The SDK simplifies code by allowing resource chaining, eliminating the need to manually specify parameters. It also includes features like auto code completion, comprehensive documentation, and type hints, making it faster and less error-prone to write code. This helps developers customize their ML workloads more efficiently and streamline their development process.

https://github.com/aws/amazon-sagemaker-examples

#python #airflow #apache #apache_airflow #automation #dag #data_engineering #data_integration #data_orchestrator #data_pipelines #data_science #elt #etl #machine_learning #mlops #orchestration #python #scheduler #workflow #workflow_engine #workflow_orchestration

Apache Airflow is a tool that helps you manage and automate workflows. You can write your workflows as code, making them easier to maintain, version, test, and collaborate on. Airflow lets you schedule tasks and monitor their progress through a user-friendly interface. It supports dynamic pipeline generation, is highly extensible, and scalable, allowing you to define your own operators and executors.

Using Airflow benefits you by making your workflows more organized, efficient, and reliable. It simplifies the process of managing complex tasks and provides clear visualizations of your workflow's performance, helping you identify and troubleshoot issues quickly. This makes it easier to manage data processing and other automated tasks effectively.

https://github.com/apache/airflow

#python #autogluon #automated_machine_learning #automl #computer_vision #data_science #deep_learning #ensemble_learning #forecasting #gluon #hyperparameter_optimization #machine_learning #natural_language_processing #object_detection #python #pytorch #scikit_learn #structured_data #tabular_data #time_series #transfer_learning

AutoGluon makes machine learning easy and fast. With just a few lines of code, you can train and use high-accuracy models for images, text, time series, and tabular data. This means you can quickly build and deploy powerful machine learning models without needing to write a lot of code. It supports Python 3.8 to 3.11 and works on Linux, MacOS, and Windows, making it convenient for various users. This saves time and effort, allowing you to focus on other parts of your project.

https://github.com/autogluon/autogluon

#python #artificial_intelligence #dag #data_science #data_visualization #dataflow #developer_tools #machine_learning #notebooks #pipeline #python #reactive #web_app

Marimo is a powerful tool for Python users that makes working with notebooks much easier and more efficient. Here’s what it offers When you run a cell or interact with UI elements, marimo automatically updates dependent cells, keeping your code and outputs consistent.
- **Interactive** Marimo ensures no hidden state and deterministic execution, making your work reliable.
- **Executable** Notebooks are stored as `.py` files, making version control easy.
- **Modern Editor**: It includes features like GitHub Copilot, AI assistants, and more quality-of-life tools.

Using marimo helps you avoid errors, keeps your code organized, and makes sharing and deploying your work simpler.

https://github.com/marimo-team/marimo

#python #automation #data #data_engineering #data_ops #data_science #infrastructure #ml_ops #observability #orchestration #pipeline #prefect #python #workflow #workflow_engine

Prefect is a tool that helps you automate and manage data workflows in Python. It makes it easy to turn your scripts into reliable and flexible workflows that can handle unexpected changes. With Prefect, you can schedule tasks, retry failed operations, and monitor your workflows. You can install it using `pip install -U prefect` and start creating workflows with just a few lines of code. This helps data teams work more efficiently, reduce errors, and save time. You can also use Prefect Cloud for more advanced features and support.

https://github.com/PrefectHQ/prefect

#other #ai #data_science #devops #engineering #federated_learning #machine_learning #ml #mlops #software_engineering

This resource is a comprehensive guide to Machine Learning Operations (MLOps), providing a wide range of tools, articles, courses, and communities to help you manage and deploy machine learning models effectively.

**Key Benefits** Access to numerous books, articles, courses, and talks on MLOps, machine learning, and data science.
- **Community Support** Detailed guides on workflow management, feature stores, model deployment, testing, monitoring, and maintenance.
- **Infrastructure Tools** Resources on model governance, ethics, and responsible AI practices.

Using these resources, you can improve your skills in designing, training, and running machine learning models efficiently, ensuring they are reliable, scalable, and maintainable in production environments.

https://github.com/visenger/awesome-mlops