||MLOps APIs are a set of endpoints that allow external systems to interact with the Wallaroo platform programmatically from their ecosystem (CI/CD, ML Platforms etc.) and perform the necessary model operations. MLOps APIs support user management, workspace management, model upload, pipeline deployment, model version management, pipeline version management, pipeline inferencing, model serving, generating inference logs, generating model monitoring assays.
||ML Workload orchestration allows data scientists and ML Engineers to automate and scale production ML workflows in Wallaroo to ensure a tight feedback loop and continuous tuning of models from training to production. Wallaroo platform users (data scientists or ML Engineers) have the ability to deploy, automate and scale recurring batch production ML workloads that can ingest data from predefined data sources to run inferences in Wallaroo, chain pipelines, and send inference results to predefined destinations to analyze model insights and assess business outcomes.
||A model or Machine Learning (ML) model is an algorithm developed using historical datasets (also known as training data) to generate a specific set of insights. Trained models can operate on future datasets (non-training sets) and offer predictions (also known as inferences). Inferences help inform decisions based on the similarity between historical data and future data.
Some examples of using a ML model are:
- Approving credit card transaction based on fraud predictions.
- Recommending a specific therapy to a patient based on diagnosis predictions.
- Recommending a specific product to purchase in an e-commerce experience based on consumer’s likelihood to be interested in it, their predicted shopping budgets as well as projected revenue from this consumer.
Model in Wallaroo refers to the resulting object from converting the model file artifact. For example, a model file would typically be produced from training a model (e.g .zip file, .onnx file etc) outside of Wallaroo. Uploading the model file to be able to run in a given Wallaroo runtime (onnx, TensorFlow etc.) results in a Wallaroo model object. Model artifacts imported to Wallaroo may include other files related to a given model such as preprocessing files, postprocessing files, training sets, notebooks etc.
||Artifacts or model artifacts are specific files and elements used or generated during model training to develop, test and track the algorithm from early experimentation to a fully trained model. Artifacts are intended to represent everything an AI team would need to be able to run and track a model from development to production.
Artifacts typically include:
- Test datasets
- Model worksheets/notebooks
- Model test results
- Model files generated from training a model (
.zip files etc
- Pre-processing methods to prepare the data for consumption by the model.
- Post-processing methods to format the data for use by external services.
As models transition from the development stage to the production stage, it is important to keep track of model artifacts. This guarantees a smooth transition from the development to production, but enable AI teams developing the models to continuously optimize/tune their models leveraging production insights.
||Model Serving is the process of integrating a ML model with operations that consume its predictions to make a decision. In Wallaroo, model serving is managed leveraging ML pipelines, which expose an integration endpoint (also call inference endpoints) to consume the predictions/inferences from a model.
||Model version refers to the version of the model object in Wallaroo. In Wallaroo, a model version update happens when we upload a new model file (artifact) against the same model object name.