Completed review of yet another capstone project of a peer at #mlopszoomcamp @DataTalksClub: this is about a MLOPS platform suppose to sustain a hotel reservation cancellation prediction service and its prolonged efficacy.

Learnings from the peer review:

Got a peek into ZenML orchestration real-world example. Another, significant learning from this review - a solid implementation of feature store for managing and serving features with Feast.

Another peer review at #mlopszoomcamp @DataTalksClub just done: a project on a MLOps Pipeline for Smart Mobility Analytics. Solid works on the deployment module!
Just reviewed one capstone project of a peer at #mlopszoomcamp @DataTalksClub: patient-readmission-prediction. Interesting to note the use of AWS Simple Notification Service for alert mechanism in MLOPS monitoring pipeline.

Could come back to #llmzoomcamp after a hiatus, engaging with #mlopszoomcamp and its associated capstone project @DataTalksClub. Took on the dlt-Cognee workshop & its homework.

Learnt about:

✅ Ingesting, indexing and querying with dlt &Cognee

✅ Knowledge graphs with Cognee

✅How by integrating ontologies, a Cognee knowledge graph can be made far better.

🧪 Phase 7: Integration Testing
The final touch: integration testing for all pipelines! Ensuring smooth functionality between training, deployment, and monitoring 🚦. #MLOpsZoomcamp #DataTalksClub
📊 Phase 6: Model Monitoring
Set up the monitoring pipeline for detecting data and model drift with Evidently 🔎. Grafana dashboards are live, and I’m tracking model performance in real time! #MLOpsZoomcamp #DataTalksClub
🚀 Phase 5: Deploying the Model
Deploying the model using BentoML 🧑‍💻. Serving it as a scalable API in Docker for production. Everything is automated and ready to go live! #MLOpsZoomcamp #DataTalksClub
⚙️ Phase 4: Model Training Pipeline
Finalizing the training pipeline for my optimized model 🏅. It’s now tracked in MLflow and promoted to production! Let’s prepare it for deployment. #MLOpsZoomcamp #DataTalksClub
🛠️ Phase 3: Tech Stack Setup
All set up with ZenML, MLflow, Optuna, and Docker-Compose for this MLOps project 🧰. Now the integration begins for seamless pipeline orchestration and experiment tracking! #MLOpsZoomcamp #DataTalksClub
🔧 Phase 2: Model Training & HPO
Feature engineering and model training in full swing! Using XGBRegressor for bike trip predictions 🚴 and optimizing with Optuna to get the best-performing model. #MLOpsZoomcamp #DataTalksClub