MLflow: Infrastructure for a Complete Machine Learning Life Cycle
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ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this talk, we will present MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
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mlflow: Accelerating the End-to-End ML lifecycle
mlflow: Accelerating the End-to-End ML lifecycleDatabricks Building and deploying a machine learning model can be difficult to do once. Enabling other data scientists (or yourself, one month later) to reproduce your pipeline, to compare the results of different versions, to track what’s running where, and to redeploy and rollback updated models is much harder.
In this talk, I’ll introduce MLflow, a new open source project from Databricks that simplifies the machine learning lifecycle. MLflow provides APIs for tracking experiment runs between multiple users within a reproducible environment, and for managing the deployment of models to production. MLflow is designed to be an open, modular platform, in the sense that you can use it with any existing ML library and development process. MLflow was launched in June 2018 and has already seen significant community contributions, with over 50 contributors and new features including language APIs, integrations with popular ML libraries, and storage backends. I’ll show how MLflow works and explain how to get started with MLflow.
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The recording of this session is on our YouTube Channel here: https://youtu.be/twsxcwgB0ZQ
Speaker: David Aronchick, Head of Open Source ML Strategy, Microsoft
Bio: David leads Open Source Machine Learning Strategy at Azure. This means he spends most of his time helping humans to convince machines to be smarter. He is only moderately successful at this. Previously, David led product management for Kubernetes at Google, launched GKE, and co-founded the Kubeflow project. David has also worked at Microsoft, Amazon and Chef and co-founded three startups.
Sign up for a free Machine Learning Ops Workshop: http://bit.ly/MLOps_Workshop_List
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MLFlow: Platform for Complete Machine Learning Lifecycle 
MLFlow: Platform for Complete Machine Learning Lifecycle Databricks Description
Data Science and ML development bring many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work.
MLflow addresses some of these challenges during an ML model development cycle.
Abstract
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
With a short demo, you see a complete ML model life-cycle example, you will walk away with: MLflow concepts and abstractions for models, experiments, and projects How to get started with MLFlow Using tracking Python APIs during model training Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
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Managing the Complete Machine Learning Lifecycle with MLflow
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To solve for these challenges, Databricks unveiled last year MLflow, an open source project that aims at simplifying the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
In the past year, the MLflow community has grown quickly: over 120 contributors from over 40 companies have contributed code to the project, and over 200 companies are using MLflow.
In this tutorial, we will show you how using MLflow can help you:
Keep track of experiments runs and results across frameworks.
Execute projects remotely on to a Databricks cluster, and quickly reproduce your runs.
Quickly productionize models using Databricks production jobs, Docker containers, Azure ML, or Amazon SageMaker.
We will demo the building blocks of MLflow as well as the most recent additions since the 1.0 release.
What you will learn:
Understand the three main components of open source MLflow (MLflow Tracking, MLflow Projects, MLflow Models) and how each help address challenges of the ML lifecycle.
How to use MLflow Tracking to record and query experiments: code, data, config, and results.
How to use MLflow Projects packaging format to reproduce runs on any platform.
How to use MLflow Models general format to send models to diverse deployment tools.
Prerequisites:
A fully-charged laptop (8-16GB memory) with Chrome or Firefox
Python 3 and pip pre-installed
Pre-Register for a Databricks Standard Trial
Basic knowledge of Python programming language
Basic understanding of Machine Learning Concepts
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To watch the full presentation click here: https://info.cnvrg.io/mlopsformachinelearning
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Understand aspects of MLflow APIs
Using tracking APIs during model training
Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
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- Enable more teams at Airbnb to utilize ML.
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Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.
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MLflow: Infrastructure for a Complete Machine Learning Life Cycle
- 1. Introduction to Aaron Davidson July 19, 2018
- 2. Outline ML development challenges How MLflow tackles these Demo Roadmap
- 3. Machine Learning Development is Complex
- 4. ML Lifecycle 4 Delta Data Prep Training Deploy Raw Data μ λ θ Tuning Scale μ λ θ Tuning Scale Scale Scale Model Exchange Governance
- 5. ML Development Challenges 100s of software tools to leverage Hard to track & reproduceresults: code, data, params,etc Hard to productionizemodels Needs large scale for best results
- 6. Introducing Open machine learningplatform • Works with any ML library& language • Runs the same wayanywhere (e.g. any cloud) • Designed to be useful for 1 or 100,000 person orgs
- 7. MLflow Design Philosophy 1. “API-first”, open platform • Allow submittingruns,models,etc from anylibrary & language • Example: a “model” can justbe a lambdafunction thatMLflow can thendeploy in many places (Docker, AzureML, Spark UDF, …) Key enabler: built aroundREST APIs and CLI
- 8. MLflow Design Philosophy 2. Modular design • Let people use different components individually(e.g.,use MLflow’s project format but not its deployment tools) • Easy to integrateinto existing ML platforms & workflows Key enabler: distinct components (Tracking/Projects/Models)
- 9. Why Open Source? Everyone is solvinga similarproblem Lots of benefits in having a common API across orgs • Can open source & share individualworkflow steps • ML tool developers can easily reach lots of users – E.g. a new ML library canuse MLflow Models to reach many serving tools
- 10. MLflow Components 10 Tracking Record and query experiments: code, data, config, results Projects Packaging format for reproducible runs on any platform Models General model format that supports diverse deployment tools
- 12. Key Concepts in Tracking Parameters: key-value inputs to your code Metrics: numeric values (can update over time) Artifacts: arbitrary files, including models Source: what code ran?
- 13. Project Spec Code DataConfig LocalExecution Remote Execution MLflow Projects
- 14. Example MLflow Project my_project/ ├── MLproject │ │ │ │ │ ├── conda.yaml ├── main.py └── model.py ... conda_env: conda.yaml entry_points: main: parameters: training_data: path lambda: {type: float, default: 0.1} command: python main.py {training_data} {lambda} $ mlflow run git://<my_project> mlflow.run(“git://<my_project>”, ...)
- 15. Model Format Flavor 2Flavor 1 Run Sources InferenceCode Batch & Stream Scoring Cloud ServingTools MLflow Models Simple model flavors usableby many tools
- 16. Example MLflow Model my_model/ ├── MLmodel │ │ │ │ │ └── estimator/ ├── saved_model.pb └── variables/ ... Usable by tools that understand TensorFlowmodel format Usable by any tool that can run Python (Docker,Spark,etc!) run_id: 769915006efd4c4bbd662461 time_created: 2018-06-28T12:34 flavors: tensorflow: saved_model_dir: estimator signature_def_key: predict python_function: loader_module: mlflow.tensorflow
- 17. Demo
- 18. Roadmap
- 19. Current Status MLflow is still alpha, so expect things to break • But send input or patches on GitHub! Just made0.3.0 release • SparkML integration (model logging & serving) • GCSartifact support • Doc, example and API improvements
- 20. Longer-Term Roadmap 1. Improvingcurrent components • Pluggable execution backends for mlflow.run • Database-backed tracking store (already a pluggable API) • Model metadata (e.g. required input schema) • Easier support for multi-step workflows
- 21. Longer-Term Roadmap 2. MLflow Data component • Let MLflowprojects load data from diverse formats (e.g. CSV vs Parquet) so you don’t have to pick a format in advance • Will build on Spark’s Data SourceAPI
- 22. Longer-Term Roadmap 3. Hyperparametertuning • Integrate with common hyperparameter tuning libraries • Make it easier to launch & track many runs in parallel (already possible but kind of awkward)
- 23. Longer-Term Roadmap 4. Language and libraryintegrations • Java and R are high on our list for APIs • Built-in Spark MLlib and PyTorchintegrations • Demonstrate how to use MLflow with other libraries (it’s easy) Let us know if you have other roadmap ideas!
- 24. Contributingto MLflow Submit issues and patches on GitHub • We’re using it for all our development & issue tracking • See CONTRIBUTING.rstfor how to run dev builds Join our mailinglist: tinyurl.com/mlflow-users Join our Slack: tinyurl.com/mlflow-slack
- 25. Conclusion Powerful workflow tools can simplifythe ML lifecycle • Improve usability for both data scientists and engineers • Same way that software dev lifecycle tools simplify dev MLflow is a lightweight, open platform that integrates easily into existing workflows