Nasscom ml ops webinar
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This document outlines a presentation on revamping machine learning pipelines with MLOps. The presentation covers the machine learning lifecycle and challenges with ML productization. It provides examples of end-to-end ML platforms like Uber's Michaelangelo and Google's TFX. The presentation discusses MLOps best practices and methodologies, including build, retrain and release pipelines. It demonstrates MLflow and shows demos of Airflow, Tensorflow model serving, and TFX-based MLOps systems on Google Cloud and Azure.
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Nasscom ml ops webinar
- 2. 2 Presented by Sameer Mahajan Principal Architect Sameer Mahajan has 25 years of experience in the software industry. He has worked for companies like Microsoft and Symantec across areas like machine learning, storage, cloud, big data, networking and analytics in the United States & India. Sameer holds 9 US patents and is an alumnus of IIT Bombay and Georgia Tech. He not only conducts hands-on workshops and seminars but also participates in panel discussions in upcoming technologies like machine learning and big data. Sameer is one of the mentors for the Machine Learning Foundations course at Coursera.
- 3. • Background • ML Lifecycle • Challenges with ML Productization • Examples of end-to-end ML platforms • MLOps Best Practices • MLOps Methodologies • Build, Retrain and Release Pipelines • Mlflow and demo Agenda • Airflow demo • Model Serving Pipeline • Tensorflow Model Serving • Tensorflow js demo • TFX-based MLOps system on Google Cloud • Azure MLOps • Conclusion • Q & A
- 4. • ML spend will reach $57.6 billion by 2021 • More and more ML systems are going into production • Gartner 2019 Survey suggests that i. 59% have AI deployed today ii. The average number of deployed AI projects to increase to 35 by 2022 • Streamline ML lifecycle • Machine Learning Operations • Started traction in 2018 Background
- 5. ML Lifecycle
- 6. Process model – Option B • Reality we are trying to model • Source of data Ingest data from sensors, devices, databases • Cleanse and transform data • Signal processing Visual analytics to capture trends indicative of underlying model processes Use the models in the real world applications and processes for predictions, insights etc. Data Engineering Data Capturing Data Preparation Data Visualization Machine Learning Train models that reflect the real- world phenomena InferenceWorld
- 7. Challenges • Dealing with data, models and code • Deployment and automation • Collaboration : data engineers, data scientists, ML engineers, business analysts, operations • Continuous Integration (CI), Deployment (CD), Training (CT) • Reproducibility of results • Transformations • Hyperparameters • Initializers • Hardware
- 8. More Challenges • Complex pipelines 1. Ensemble 2. Retraining 3. Transfer learning 4. Multiple prediction pipelines in parallel (Canary) • Self-updating ML pipelines • Governance : tracing failed result back to data or code • Scalability
- 9. Examples of end-to-end ML platforms 1. Uber’s Michaelangelo 2. Facebook’s FBLearner 3. Google has TFX 4. Airbnb has BigHead 5. Databricks introduced mlflow which is now open source 6. Sagemaker 7. Azure 8. Datarobot 9. Polyaxon and KubeFlow
- 10. • Background • ML Lifecycle • Challenges with ML Productization • Examples of end-to-end ML platforms • MLOps Best Practices • MLOps Methodologies • Build, Retrain and Release Pipelines • Mlflow and demo Agenda • Airflow demo • Model Serving Pipeline • Tensorflow Model Serving • Tensorflow js demo • TFX-based MLOps system on Google Cloud • Azure MLOps • Conclusion • Q & A
- 11. Best Practices • Data Pipeline: Discoverable and Accessible Data - data lake, data mesh • Versioned control: github, Data Science Version Control (DVC), mlflow Projects • Data Exploration: Jupyter, pandas, numpy, seaborn • ML: scikit-learn • CI/CD: Jenkins • Packaging: Docker • Orchestrator: Airflow, Kubernetes • Monitoring: ELK, Prometheus
- 12. Methodologies 1. Combination of DevOps (CI/CD), Software Engineering and ML 2. ML experiments are captured as runs 3. Each run captures all its steps, its data, parameters, hyper parameters, code, initializers, model evaluations, artifacts like trained models and business results after deployment 4. Packaging a model: container
- 13. Closer look at some pipelines 1. Build pipeline • Triggered on schedule or when new code checked in / data becomes available • Building code and running unit tests • Data tests: schema and distribution conformance 2. Retrain pipeline • Triggered on a schedule or when new data becomes available • Train, evaluate and register model 3. Release pipeline • Triggered every time a new artifact is available • Package, test, deploy to production, start monitoring
- 14. • Background • ML Lifecycle • Challenges with ML Productization • Examples of end-to-end ML platforms • MLOps Best Practices • MLOps Methodologies • Build, Retrain and Release Pipelines • Mlflow and demo Agenda • Airflow demo • Model Serving Pipeline • Tensorflow Model Serving • Tensorflow js demo • TFX-based MLOps system on Google Cloud • Azure MLOps • Conclusion • Q & A
- 15. MLflow Tracking Record and query experiments: code, data, config, and results mlflow MLflow Projects Package data science code in a format to reproduce runs on any platform MLflow Models Deploy machine learning models in diverse serving environments MLflow Registry Store, annotate, discover, and manage models in a central repository
- 16. mlflow demo
- 17. Airflow demo
- 18. Model serving Embedded model 1. Serialized pickle file 2. Language agnostic exchange formats like PMML, PFA and ONNX 3. H2O exports a POJO in a JAR Separate service 1. Cloud providers’ tools and SDKs wrapping models 2. Kubeflow 3. mlflow models Published as data 1. Typically used in streaming / real time scenarios
- 20. Tensorflow.js model serving demo 1. Open google chrome 2. Open chrome://apps/ 3. Start web server 4. RockPaperScissorsTensorflow.jsDemo (based on a courser assignment) 5. Open http://127.0.0.1:8887 in chrome 6. Open developer tools 7. Demo retraining and predictions
- 21. • Background • ML Lifecycle • Challenges with ML Productization • Examples of end-to-end ML platforms • MLOps Best Practices • MLOps Methodologies • Build, Retrain and Release Pipelines • Mlflow and demo Agenda • Airflow demo • Model Serving Pipeline • Tensorflow Model Serving • Tensorflow js demo • TFX-based MLOps system on Google Cloud • Azure MLOps • Conclusion • Q & A
- 22. TFX-based MLOps system on Google Cloud
- 23. MLOps using Azure Machine Learning
- 24. • Evolving field • Applying learning from other fields like DevOps, Software Engineering • Taking holistic view • Upcoming tools and practices • Key in making ML productization successful Conclusion
- 25. • https://www.brighttalk.com/webcast/6793/365540/machine-learning-operations-mlops-deploy-at-scale • https://www.linkedin.com/pulse/mlops-101-modern-operation-machine-learning-payam-mokhtarian/ • https://en.wikipedia.org/wiki/MLOps • https://cloud.google.com/solutions/machine-learning/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build • https://docs.microsoft.com/en-us/samples/microsoft/mlopspython/mlops-with-azure-ml/ • https://ibm-cloud-architecture.github.io/refarch-data-ai-analytics/methodology/MLops/ • https://www.kdnuggets.com/2018/04/operational-machine-learning-successful-mlops.html • https://www.pgs-soft.com/blog/more-effective-machine-learning-production-with-mlops/ • https://www.xenonstack.com/blog/mlops/ • https://towardsdatascience.com/mlops-reducing-the-technical-debt-of-machine-learning-dac528ef39de • https://towardsdatascience.com/ml-ops-challenges-solutions-and-future-trends-d2e59b74dc6b References
- 26. • https://www.bristoldatascientists.org/wp-content/uploads/sites/5/2020/02/Luke-The-Future-of-MLOps.pdf • https://www.cognilytica.com/2020/03/03/ml-model-management-and-operations-2020-mlops/ • https://martinfowler.com/articles/data-monolith-to-mesh.html • https://emilygorcenski.com/post/data-versioning/ • https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf • https://martinfowler.com/articles/cd4ml.html • https://mlflow.org/docs/latest/index.html • https://airflow.apache.org/docs/stable/ • https://www.tensorflow.org/ • https://www.coursera.org/specializations/tensorflow-in-practice • https://www.coursera.org/specializations/tensorflow-data-and-deployment References (contd.)
- 27. Thank you! [email protected] For more information please reach out to me at https://in.linkedin.com/in/sameersmahajan