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![mlflow.set_tracking_uri(self.sagemaker_configs["mlflow_host"])
mlflow.create_experiment(experiment_name)
mlflow.start_run(experiment_id=experiment_id)
mlflow.log_param(key=k, value=quant_hyper_params[k])
mlflow.log_artifact(output + ".bin")
mlflow.log_metric("precision{}".format(metric_type), prec_score)
mlflow.set_tag("promote", "true")
Training module code snippets](https://image.slidesharecdn.com/mlflowaspartofyourmlcicd-190916055754/75/MLFlow-as-part-of-ML-CI-CD-at-Avalara-11-2048.jpg)
Uploaded byManoj Mahalingam
MLFlow as part of ML CI/CD at Avalara
The document discusses the integration of MLflow into the CI/CD workflow at Avalara, highlighting its capabilities in managing machine learning models and data pipelines. It details the scales of data being processed and the various components involved in the machine learning lifecycle, including data transformation, model training, and deployment. Additionally, it mentions the open-source plugin connecting MLflow with GoCD for improved CI/CD processes.
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Similar to MLFlow as part of ML CI/CD at Avalara
MLFlow as part of ML CI/CD at Avalara
- 1. MLFlow as a coredriver of our ML CI/CD workflow @Avalara
- 2. About me Manoj Mahalingam PrincipalEngineer @Avalara
- 3. Scale of Data@ Avalara (Indix) 2.1 Billion Product URLs 8 TB HTML Data Crawled Daily 1B Unique Products 7000 Categories 120 B Price Points 3000 Sites
- 4. Crawling Pipeline Data PipelineML AggregateMatchStandardizeExtractAttributesClassifyDedupe Parse Crawl Data CrawlSeed Brand & Retailer Websites Feeds Pipeline Transform Clean Connect Feed Data Brand & Retailer Feeds Product Catalog Customizable Feeds Search & Analytics Index Indexing PipelineReal Time Index Analyze Derive Join API (Bulk & Synchronous) Product Data Transformation Service Data Pipeline @ Avalara (Indix)
- 5.
- 6. Model Hierarchy BMV Ensemble Toplevel Ensemble ShoesModels Clothing Models Electronics Models ... Predict if Media - Books, Music, Videos categories Assign one of the other applicable top levelsInterm ediate Subca tegory Leaf Gend er jackets & coats men, women, boy, girl, baby, unisex plus-size, active, maternity women’s plus-size fleece jackets Clothing Models
- 7. Principles from CD SourceCode and Artifact Repository for Reproducibility Source Code, Data and Model Repository for Reproducibility Continuous Delivery for Software Continuous Delivery for Machine Learning
- 8. Requirements from amodel repo ● Similar to an artifact repository like Maven, Ivy ○ Directory Structure, Versioning, Publishing of models ● Has clients to publish models for most commonly used frameworks ○ scikit-learn, Spark MLLib, Keras ● For a model, ○ Data ■ Stored in S3 ■ In Different formats ● Parquet (Spark MLLib), Scikit-Learn - Pickle, Keras - HDF5 ○ Metadata ■ Training/Validation/Test Datasets ■ Hyper-parameters used ■ Evaluation Metrics
- 9. Model Promotion Training Data ● Taggingthe “latest good” version that needs to be deployed ● Not all models need/can be promoted ○ Experimental models ○ Models that fail the test set or performance/latency metrics ● Easy rollback - tag the “last good” version as the latest Pre-process Data (Spark Job) Build Model (Python) Evaluate Model (Python) Publish Model Promote Model Manual Step Training Pipeline
- 10. MLFlow ● End-to-end machinelearning platform ● By DataBricks (creators of Spark) ● Three components as shown below..
- 11. mlflow.set_tracking_uri(self.sagemaker_configs["mlflow_host"]) mlflow.create_experiment(experiment_name) mlflow.start_run(experiment_id=experiment_id) mlflow.log_param(key=k, value=quant_hyper_params[k]) mlflow.log_artifact(output +".bin") mlflow.log_metric("precision{}".format(metric_type), prec_score) mlflow.set_tag("promote", "true") Training module code snippets
- 12. MLFlow integration withGoCD ● Open source plugin for GoCD that enables MLFlow to be a material (source) for CI/CD pipelines on GoCD - https://github.com/indix/mlflow-gocd
- 13. Questions? I blog athttps://stacktoheap.com Twitter and most other platforms @manojlds