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.
Machine learning is one of the most important trends in tech today, but several applications cause concern, such as its potential impact on employment or its use for purposes that we might consider unethical. Another, and the topic of this talk, is the problem of machine bias. 'Machine Bias’ means that a machine learning model might find the wrong patterns, because if the sample training data isn’t representative, the output won’t be either. Meanwhile, the mechanics of ML might make this hard to spot. The most obvious and immediately concerning place that this issue can come up is in human diversity, and there are plenty of reasons why data about people might come with embedded biases.
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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.
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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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This talk will explore data bias issues in the speaker's research on detecting the spread of hate speech online. You will learn about a topical application of machine learning techniques to classify different types of hand-labeled content online and will learn how to recognize and handle issues with biased data affecting your model's results.
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