Your models and datasets need a home that isn't your laptop. Today we set up S3 buckets, lock down access with IAM, move files with boto3, and connect DVC straight to the cloud. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 18)
Overview of AWS, GCP, and Azure ML services. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 17)
Move from experimentation to production by spinning up a REST API for your trained model with a single MLflow command. Learn mlflow models serve, model signatures, input schemas, and how to send real-time predictions. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 16)
A trained model sitting in a .pkl file helps nobody. Today your iris classifier gets a front door: a REST API that any application, in any language, can call over HTTP. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 15)
Two weeks in, you have all the pieces: Git and DVC for versioning, MLflow for tracking, and Docker for reproducible environments. A single docker compose up that spins up a tracking server, runs a versioned experiment, and logs every result. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 14)
Stop running MLflow and your training script in separate terminals. Today you'll wire them together with Docker Compose , one command, a full local ML platform, ready to log every experiment automatically. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 13)
Example: Kubernetes, Terraform, Docker, AWS, MLOps...
