Category: machine learning

Zhi Yang Presenting “Hierarchical Topic Modeling in Cancer Patients’ Mutational Profiles”

UPDATED: Video Added from the Conference!

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Introducing Zhi Yang > @zhiiiyang < presenting “Hierarchical Topic Modeling in Cancer Research”.

Topic models have been widely applied to extract topics from various range of documents or collections of texts, i.e., online customers reviews, medical records, scientific
journals, legal documents, books and etc. Its application facilitates the process for us to quickly understand the most featured and commonly shared information embedded texts without actually reading through the entire collection. In addition, topic models also allow us to access the contribution of each topic and its representations across different documents. Human genomes have been exposed to an assortment of mutational processes by contributing to unique patterns of somatic mutations. What would happen if we apply the same concept to the somatic mutations obtained from the cancer patients and look for “topics” of mutations? What would these “topics” tell us about the most important information for our health, genetic, risk factors for cancer and
something more that slip under the radar?

Shiraishi et al’s have proposed a topic model targeted for somatic mutations to capture the characteristics and burdens contributed by mutational processes. By closely examining the burdens, we’d like to compare them across different categories, say, for example, time, cancer subtype, ethnicity, smoking history, etc. Then, we’d like to develop the statistical machinery to infer the difference between the mutational profiles across different categories and associate the variations with the know exposures. This tool is potentially useful for identifying novel and existing mutational processes and correlating them with risk factors in which later can be used to monitor any treatment effects in personalized medicine and targeted therapy.

Read the publication here at biorxiv and come check out Zhi Yang’s talk at ML4ALL happening April 28th-30th in amazing Portland, Oregon! Get your tickets to attend here. For the schedule, our excellent sponsors docs for the conference, check out the ML4ALL Conference Site!

Sachi Parikh Presenting “My Journey Learning ML and AI through Self Study as a High School Student”

UPDATED: Video Added from the Conference!

sachi-parikhIntroducing Sachi Parikh > @parikhsachi < presenting “My Journey Learning ML and AI through Self Study as a High School Student”.

Sachi is a high school student in the Bay Area who is interested in AI and Machine Learning and loves to code, read and learn. In the talk she’s put together for us she’s delved into the path she’s taken to get into this topic. I’ve seen an outline of this path and I’ll admit, I’m impressed, but you’ll have to come and attend to talk to see the outline!

Come check out Sachi Parikh’s talk and learn about this learning path at ML4ALL happening April 28th-30th in amazing Portland, Oregon! Get your tickets to attend here. For the schedule, our excellent sponsors docs for the conference, check out the ML4ALL Conference Site!

Karl Weinmeister Presenting “Build, train, and serve your ML models on Kubernetes with Kubeflow”

UPDATED: Video Added from the Conference!

karl-weinmeisterIntroducing Karl Weinmeister > @kweinmeister < presenting “Build, train, and serve your ML models on Kubernetes with Kubeflow”.

Karl is a Developer Advocacy Manager from Google’s Developer Relations Artificial Intelligence and Machine Learning team.  Karl has worked extensively in cloud and mobile, and was a contributor to one of the first AI-based crossword puzzle solvers that is still referenced today.

Distributing ML workloads across multiple nodes has become common. To achieve higher and higher levels of accuracy, data scientists are using more data and more complex models than ever before.

Kubeflow is an open-source platform for model building, serving, and training. It is built on industry standard Kubernetes infrastructure and runs in multiple clouds and on-premises.

In this session, we’ll discuss the problems that Kubeflow solves, and how you can use it to create reproducible ML workflows.

Come check out Karl Weinmeister’s talk at ML4ALL happening April 28th-30th in amazing Portland, Oregon! Get your tickets to attend here. For the schedule, our excellent sponsors docs for the conference, check out the ML4ALL Conference Site!

Aeva van der Veen Presenting “Gaming Rigs and ML Pipelines: how to get started with the tools you already have”

UPDATED: Video Added from the Conference!

That isn’t all though, Aeva gave a lightning talk too, included below!

aeva-vanIntroducing Aeva van der Veen > @aevavoom < presenting “Gaming Rigs and ML Pipelines: how to get started with the tools you already have”.

Aeva is an outspoken open source advocate with over a decade experience contributing to F/OSS software and communities. They have been building distributed systems on Linux-based systems since ’99, and are most well known for their work in the OpenStack community wherein they founded Ironic, the Bare-Metal-as-a-Service project. Aeva lives in rainy Seattle and enjoys staying home when not travelling for work.

If you think that only big tech companies or PhD scientists can use ML & AI, I’d like to show you that an individual open-source enthusiast can build and train a model on commodity hardware using Open Data – and then scale that up on a public cloud.

And if you’re a PC gamer, you probably already have all the tools you need!

  • fast.ai, an easy-to-learn Python ML framework
  • nvidia-docker on an Ubuntu Gaming PC
  • public-domain GIS imagery
  • a couple terabytes of storage space and a fast internet connection

This talk grew out of a startup competition last year: we tried to use public-domain satellite imagery to help predict and prevent forest fires. Even though we chose not to pursue this as a business, it’s an excellent example of how combine open source software, public data, and a gaming PC to build an ML pipeline.

Come check out Aeva van der Veen’s talk at ML4ALL happening April 28th-30th in amazing Portland, Oregon! Get your tickets to attend here. For the schedule, our excellent sponsors docs for the conference, check out the ML4ALL Conference Site!

Jules S. Damji Presenting “MLflow: Infrastructure for a Complete Machine Learning Life Cycle”

jules-damjiIntroducing Jules S. Damji > @2twitme < presenting “MLflow: Infrastructure for a Complete Machine Learning Life Cycle”.

Jules S. Damji is an Apache Spark Community and Developer Advocate at Databricks. He is a hands-on developer with over 20 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, @Home, LoudCloud/Opsware, VeriSign, ProQuest, and Hortonworks, building large-scale distributed systems. He holds a B.Sc and M.Sc in Computer Science and MA in Political Advocacy and Communication from Oregon State University, Cal State, and Johns Hopkins University respectively.

He’s also the Program Chair for Spark + AI Summit

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 (mlflow.org), 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

Come check out Jules S. Damji’s talk at ML4ALL happening April 28th-30th in amazing Portland, Oregon! Get your tickets to attend here. For the schedule, our excellent sponsors docs for the conference, check out the ML4ALL Conference Site!