jacopotagliabue / no-ops-machine-learning
A PaaS End-to-End ML Setup with Metaflow, Serverless and SageMaker.
☆37Updated 3 years ago
Related projects ⓘ
Alternatives and complementary repositories for no-ops-machine-learning
- Template-based generation of DAG cards from Metaflow classes, inspired by Google cards for machine learning models.☆30Updated 2 years ago
- A repository that showcases how you can use ZenML with Git☆64Updated 3 months ago
- Materials for my 2021 NYU class on NLP and ML Systems (Master of Engineering).☆96Updated last year
- Joining the modern data stack with the modern ML stack☆192Updated last year
- Ingesting data with Pulumi, AWS lambdas and Snowflake in a scalable, fully replayable manner☆68Updated 2 years ago
- 🐍 Material for PyData Global 2021 Presentation: Effective Testing for Machine Learning Projects☆81Updated 2 years ago
- The project completed for MLops Engineering Lab #1 by Team #1. See our wiki for more info☆16Updated 3 years ago
- Best practices for engineering ML pipelines.☆37Updated 2 years ago
- Deploy A/B testing infrastructure in a containerized microservice architecture for Machine Learning applications.☆39Updated last year
- Listens MLFlow model registry changes and deploy models based on configurations☆21Updated last year
- Enterprise Solution for Text Classification (using BERT)☆10Updated last year
- MLOps pipeline for NVIDIA Merlin on GKE☆41Updated 3 years ago
- Metaflow tutorials for ODSC West 2021☆65Updated 2 years ago
- ☆17Updated 11 months ago
- Scaling Python Machine Learning☆44Updated last year
- In-Session Personalization Workshop for eCommerce, April 2021, and the MICES Workshop in June 2021.☆21Updated 3 years ago
- Recommendations at "Reasonable Scale": joining dataOps with recSys through dbt, Merlin and Metaflow☆229Updated last year
- Project demonstrating dual model deployment scenarios using Vertex AI (GCP).☆35Updated 2 years ago
- Code samples for the Effective Data Science Infrastructure book☆110Updated last year
- An efficient, to-the-point, and easy-to-use checklist to following when deploying an ML model into production.☆31Updated last year
- Projects developed by Domino's R&D team☆76Updated 2 years ago
- ☆12Updated 5 months ago
- Example project with a complete MLOps cycle: versioning data, generating reports on pull requests and deploying the model on releases wit…☆44Updated 2 years ago
- Fast model deployment on AWS Lambda☆14Updated 8 months ago
- Examples of using Evidently to evaluate, test and monitor ML models.☆18Updated 2 months ago
- Instant search for and access to many datasets in Pyspark.☆34Updated 2 years ago
- ☆18Updated 3 years ago
- ForML - A development framework and MLOps platform for the lifecycle management of data science projects☆104Updated last year
- Introduction to Ray Core Design Patterns and APIs.☆61Updated 9 months ago
- Python library to run ML/data pipelines on stateless compute infrastructure (that may be ephemeral or serverless). Please see the documen…☆17Updated last year