tsterbak / pydataberlin-2019
This Repository contains the material for my tutorial "Managing the end-to-end machine learning lifecycle with MLFlow" at pyData/pyCon Berlin 2019.
☆39Updated last year
Related projects ⓘ
Alternatives and complementary repositories for pydataberlin-2019
- Tutorial for a new versioning Machine Learning pipeline☆81Updated 3 years ago
- ☆65Updated 4 years ago
- Guide on creating an API for serving your ML model☆65Updated 2 years ago
- Content for the Model Interpretability Tutorial at Pycon US 2019☆41Updated 3 months ago
- ☆43Updated last year
- ∞ Priceloop Engineering Conventions for Scala, Python, Git Workflow etc☆102Updated 2 years ago
- MLFlow Spark Summit 2019 Presentation☆67Updated 5 years ago
- Code demonstrating a simple Machine Learning model abstract base class and its uses.☆14Updated last year
- Data Analysis Baseline Library☆131Updated 2 weeks ago
- Using Kafka-Python to illustrate a ML production pipeline☆108Updated last year
- ☆20Updated 4 months ago
- ☆30Updated 5 months ago
- Content for Applied ML Workshop @ DataHack Summit 2019☆25Updated 4 years ago
- ☆54Updated last year
- Example project for the course "Testing & Monitoring Machine Learning Model Deployments"☆134Updated 8 months ago
- Tutorial given at PyData LA 2018☆97Updated 2 months ago
- Capturing model drift and handling its response - Example webinar☆106Updated 5 years ago
- Assorted exercises and proof-of-concepts to understand and study machine learning and statistical learning theory☆45Updated 6 years ago
- Production Machine Learning Pipeline for Text Classification with fastText☆32Updated 3 years ago
- 🐍 Material for PyData Global 2021 Presentation: Effective Testing for Machine Learning Projects☆81Updated 2 years ago
- Contains all tutorials and hands-on examples for the ODSC 2019 Workshop☆37Updated 4 years ago
- Repository for the research and implementation of categorical encoding into a Featuretools-compatible Python library☆50Updated 2 years ago
- This repository is to host template for calculating ROI on Artificial Intelligence projects☆44Updated 5 years ago
- ☆155Updated 4 years ago
- Load Testing ML Microservices for Robustness and Scalability☆14Updated 2 years ago
- Best practices for engineering ML pipelines.☆37Updated 2 years ago
- Metaflow tutorials for ODSC West 2021☆65Updated 2 years ago
- A python script for a PyTorch feed forward neural network for tabular data using categorical embeddings.☆67Updated 5 years ago