#MLOps

ZenML streamlines MLOps for data science by easily integrating with cloud services. Build ML pipelines with minimal code changes, deploy effortlessly via CLI or dashboard, and enjoy integration with tools like MLflow. Optimize your machine learning workflows without vendor lock-in.

Explore how leading industries apply data science and machine learning in practical production scenarios. Understand the implementation of ML projects through problem framing, techniques, results, and scientific backing. Access curated resources on vital subjects like data quality, engineering, and feature stores to gauge real-world ML project returns.

Aqueduct is an open-source framework enabling MLOps across any cloud infrastructure with Python. It provides a unified interface for executing tasks on Kubernetes, Spark, and AWS Lambda, reducing the need for diverse tools. Users benefit from clear visibility into workflow data and performance, ensuring robust processes and swift problem resolution. Aqueduct seamlessly integrates into existing systems while maintaining secure operations.

Discover MLOps workflows enhanced by Rust, offering alternatives to conventional Python-based stacks like Jupyter and Pandas. This repository, styled as a practical guide, provides insights into crafting robust MLOps solutions using Rust to take advantage of its superior performance and energy efficiency. It showcases demos ranging from PyTorch model training to deploying models with Rust frameworks such as Actix. Embrace innovative and high-speed computational methods centered around containerized workflows, with in-depth examples and advanced demonstrations emphasizing infrastructure efficiency.

The course teaches how to develop serverless machine learning systems using Python, aimed at creating both real-time and batch prediction services. It simplifies infrastructure management by allowing a focus on Python programming and seamless integration with tools like GitHub and Hopsworks. Participants will learn MLOps skills including model deployment, versioning, and monitoring, and develop user interfaces for prediction services. This is suited for those who aim to demonstrate the value of machine learning models in enterprise applications beyond static datasets.

Embark on a journey from experimentation to production with machine learning. Learn to design, develop, and deploy ML applications with industry best practices. Join over 40,000 developers to enhance your skills in MLOps, scaling ML workloads, and creating CI/CD workflows. Suitable for developers, graduates, and leaders, this resource bridges academic knowledge with industry demands, offering a solid foundation in ML system development.

This repository provides an extensive collection of open-source libraries to bolster production machine learning workflows. It covers crucial aspects such as deployment, monitoring, scaling, and security, across applications including AutoML, data pipeline optimization, adversarial robustness, and tools tailored for computer vision and NLP. Features include support for distributed computing and model tracking, ensuring efficient machine learning operations. Suitable for developers and researchers requiring dependable tools to optimize their machine learning processes.

This course offers a comprehensive guide to creating production-grade AI systems through LLM and RAG technologies, covering data collection to model deployment. It introduces MLOps best practices to build an 'LLM Twin' replicating specific writing styles. Geared towards intermediate MLEs, DEs, DSs, and SWEs, the course includes practical lessons and open-source resources available for free. Participants will also explore the use of serverless tools such as Comet ML, Qdrant, and Qwak.

Explore a versatile data platform designed for machine learning, providing a robust Feature Store and seamless MLOps capabilities. Efficiently manage, govern, and serve models, while developing feature and training pipelines in a secure collaboration environment. Available in serverless or managed formats on AWS, Azure, or GCP, and compatible with platforms like Databricks and SageMaker, the system offers on-premises installation for added flexibility and compliance. Benefit from extensive documentation, APIs, and practical tutorials for applications such as fraud detection and churn prediction.

This in-depth guide provides structured training for reaching the top tier in data and AI, covering essential topics such as machine learning, NLP, and deep learning. It includes tailored study plans, video content, and adaptable resources to fit diverse schedules. The program emphasizes practical skill development for students and professionals seeking growth in leadership and expertise, while assisting in personal branding and building a strong portfolio.

FedML provides an extensive solution to efficiently manage AI workloads across decentralized GPUs, multi-cloud environments, and edge servers. Utilizing TensorOpera AI, this unified and scalable machine learning library streamlines model training, deployment, and federated learning. Features like TensorOpera Launch simplify environment management by aligning AI tasks with economical GPU resources. FedML supports use cases such as on-device training and cross-cloud deployments, offering comprehensive MLOps capabilities with TensorOpera Studio and Job Store for smooth execution of AI tasks. It capitalizes on serverless deployments and vector database searches to operate at various scales.

Discover a broad collection of MLOps resources featuring courses, articles, communities, and books. This resource list offers insights into essential elements of Machine Learning Operations, including workflow management, model deployment, testing, and infrastructure. It assists developers and product managers in optimizing ML products and procedures. Additionally, explore discussions on ethics, governance, and ML/AI economics to keep pace with industry changes. Stay informed about the latest trends and practices in the rapidly evolving MLOps field.

The template efficiently initiates deep learning projects using PyTorch Lightning and Hydra, facilitating smooth configuration management. It supports multi-GPU training, mixed precision, and rapid experimentation, reducing boilerplate while allowing exploration of diverse models and datasets. Although focused on model prototyping, it offers extensive MLOps tools for educational and research purposes. Engage in experimentation with minimal setup to enhance your deep learning skills.

SSAIS (Survey of Startups on Artificial Intelligence Software) offers a regularly updated collection of AI software startups, detailing companies from infrastructure to applications across countries including China, the USA, and Israel. The repository categorizes startups by technology, offering insights into the latest funding rounds and commercial impact. It aims to highlight the current state and innovation potential in fields such as data processing, NLP, and vision.

Discover how to use distributed machine learning patterns to build scalable systems. Authored by Yuan Tang, this book explores practical applications of Kubernetes, TensorFlow, Kubeflow, and Argo Workflows in creating efficient machine learning pipelines, automating processes, and overseeing extensive workloads.

ClearML presents an open-source suite to maximize AI workflow efficiency, featuring modules such as Experiment Manager, MLOps support, and Data Management. It includes Nvidia-Triton model serving, orchestration dashboards, and cloud-ready solutions, enhancing ML/DL productivity. ClearML supports experiment tracking and data versioning across infrastructures like Kubernetes and cloud environments. Innovations like Fractional GPUs optimize resources, making it ideal for both development and production with tools for all expertise levels.

Instill Core is a versatile AI infrastructure tool designed for efficient orchestration of data, models, and pipelines. It supports the development of AI-first applications by offering solutions like Instill VDP for unstructured data ETL, Instill Model for MLOps/LLMOps, and Instill Artifact for unified data representation. Available for cloud hosting via Instill Cloud or self-hosting, deployment using Docker is straightforward. The platform offers a user-friendly interface, comprehensive SDKs, and detailed documentation for efficient AI application management.

Neptune provides a scalable solution for tracking AI experiments, suitable for teams working on foundation models. Log millions of runs and access tools for monitoring, visualizing, and comparing model training effectively. Compatible with over 25 MLOps frameworks, Neptune supports efficient metadata management and collaboration through customizable dashboards and easy sharing.

The CDF Special Interest Group for MLOps facilitates collaboration in machine learning operations through various communication channels such as Slack, email, and routine meetings. Contributors can engage in discussions about the latest updates and review the draft 2024 document. Participate in bi-weekly virtual meetings suited for USA and APAC/Oceania time zones and access community resources to contribute to MLOps best practices.