modzy / model-deployment-checklist
An efficient, to-the-point, and easy-to-use checklist to following when deploying an ML model into production.
☆30Updated 2 years ago
Alternatives and similar repositories for model-deployment-checklist:
Users that are interested in model-deployment-checklist are comparing it to the libraries listed below
- A PaaS End-to-End ML Setup with Metaflow, Serverless and SageMaker.☆37Updated 4 years ago
- A repository that showcases how you can use ZenML with Git☆69Updated 9 months ago
- Study the temporal performance degradation of machine learning models.☆16Updated last year
- 🐍 Material for PyData Global 2021 Presentation: Effective Testing for Machine Learning Projects☆81Updated 3 years ago
- 🤗 Collection of examples on how to train, deploy and monitor HuggingFace models in Google Cloud Vertex AI☆21Updated last year
- Machine Learning with TensorFlow Extended (TFX) Pipelines☆13Updated last year
- Codes, scripts, and notebooks on various aspects of transformer models.☆27Updated 2 years ago
- The project completed for MLops Engineering Lab #1 by Team #1. See our wiki for more info☆16Updated 4 years ago
- Best practices for engineering ML pipelines.☆35Updated 2 years ago
- Check for data drift between two OpenAI multi-turn chat jsonl files.☆37Updated last year
- Framework for building and maintaining self-updating prompts for LLMs☆61Updated 10 months ago
- Deploy A/B testing infrastructure in a containerized microservice architecture for Machine Learning applications.☆40Updated 3 months ago
- Template-based generation of DAG cards from Metaflow classes, inspired by Google cards for machine learning models.☆30Updated 3 years ago
- Drift detection module for machine learning pipelines.☆23Updated last year
- ☆27Updated last year
- 🤝 Trade any tensors over the network☆30Updated last year
- Material for the series of seminars on Large Language Models☆34Updated last year
- Build fast gradio demos of fastai learners☆35Updated 3 years ago
- ☆30Updated 3 years ago
- Demo on how to use Prefect with Docker☆25Updated 2 years ago
- ☆78Updated 11 months ago
- machine learning model performance metrics & charts with confidence intervals, optimized with numba to be fast☆16Updated 3 years ago
- Learn how to monitor ML systems to identify and mitigate sources of drift before model performance decay.☆84Updated 2 years ago
- Automatic Machine Learning (AutoML) for Wave Apps☆32Updated last year
- Learn how to create reliable ML systems by testing code, data and models.☆86Updated 2 years ago
- ☆77Updated 10 months ago
- Examples of using Evidently to evaluate, test and monitor ML models.☆23Updated last week
- Confusion Matrix in Python: plot a pretty confusion matrix (like Matlab) in python using seaborn and matplotlib☆19Updated 3 years ago
- ShopME: An E2E fashion recommendation System☆20Updated 2 months ago
- ☆16Updated 2 years ago