A step-by-step guide to building an MLOps pipeline

1

neptune.ai

Experiment tracking tools like MLflow, Weights and Biases, and Neptune.ai provide a pipeline that automatically tracks meta-data and artifacts generated from each experiment you run. Although they have varying features and functionalities, experiment tracking tools provide a systematic structure that handles the iterative model development approach.

#Data Science And Machine Learning #Data Science Notebooks #Machine Learning Tools 23 social mentions

2

Minio

The meta-data and model artifacts from experiment tracking can contain large amounts of data, such as the training model files, data files, metrics and logs, visualizations, configuration files, checkpoints, etc. In cases where the experiment tool doesn't support data storage, an alternative option is to track the training and validation data versions per experiment. They use remote data storage systems such as S3 buckets, MINIO, Google Cloud Storage, etc., or data versioning tools like data version control (DVC) or Git LFS (Large File Storage) to version and persist the data. These options facilitate collaboration but have artifact-model traceability, storage costs, and data privacy implications.

#Cloud Storage #Cloud Computing #Object Storage 156 social mentions

3

Git Large File Storage

The meta-data and model artifacts from experiment tracking can contain large amounts of data, such as the training model files, data files, metrics and logs, visualizations, configuration files, checkpoints, etc. In cases where the experiment tool doesn't support data storage, an alternative option is to track the training and validation data versions per experiment. They use remote data storage systems such as S3 buckets, MINIO, Google Cloud Storage, etc., or data versioning tools like data version control (DVC) or Git LFS (Large File Storage) to version and persist the data. These options facilitate collaboration but have artifact-model traceability, storage costs, and data privacy implications.

#Git #Development #Code Collaboration 101 social mentions

4

Docker Hub

Configure a container registry such as Docker hub or GitHub container registry.

#Developer Tools #Code Collaboration #Git 314 social mentions

5

Amazon S3

The meta-data and model artifacts from experiment tracking can contain large amounts of data, such as the training model files, data files, metrics and logs, visualizations, configuration files, checkpoints, etc. In cases where the experiment tool doesn't support data storage, an alternative option is to track the training and validation data versions per experiment. They use remote data storage systems such as S3 buckets, MINIO, Google Cloud Storage, etc., or data versioning tools like data version control (DVC) or Git LFS (Large File Storage) to version and persist the data. These options facilitate collaboration but have artifact-model traceability, storage costs, and data privacy implications.

#Cloud Hosting #Object Storage #Cloud Storage 175 social mentions