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Full reference of LinkedIn answers 2024 for skill assessments (aws-lambda, rest-api, javascript, react, git, html, jquery, mongodb, java, Go, python, machine-learning, power-point) linkedin excel test lösungen, linkedin machine learning test LinkedIn test questions and answers
A curated list for awesome kubernetes sources :ship::tada:
The Cloud Native AI Platform
Machine Learning、Deep Learning、PostgreSQL、Distributed System、Node.Js、Golang
A hyperparameter optimization framework
Learn ML engineering for free in 4 months! Register here 👇🏼
A curated list of references for MLOps
A curated list of awesome Amazon Web Services (AWS) libraries, open source repos, guides, blogs, and other resources. Featuring the Fiery Meter of AWSome.
The Triton Inference Server provides an optimized cloud and edge inferencing solution.
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Open Machine Learning Course
🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading
Build, Manage and Deploy AI/ML Systems
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Instant Kubernetes-Native Application Observability
Simple and Distributed Machine Learning Python Library porting ML algorithms for Spark
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
AI Infra / AI Orchestration / AI Control Plane
🛠 All-in-one web-based IDE specialized for machine learning and data science.
Accelerated deep learning R&D
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
.NET for Apache® Spark™ makes Apache Spark™ easily accessible to .NET developers.