Removing bias in ML, Deep Learning, Kubernetes with OpenShift and IBM Watson – watch January 2020 online meetup recordings
Tags: containers, kubernetes, machinelearning, ibmcloud
Published: 2020-02-04 18:01:20

---
title: Removing bias in ML, Deep Learning, Kubernetes with OpenShift and IBM Watson – watch January 2020 online meetup recordings
published: true
date: 2020-02-01 00:52:36 UTC
tags: Containers,Kubernetes,Machine Learning,IBM Cloud
canonical_url: https://maxkatz.org/2020/01/31/removing-bias-in-ml-deep-learning-kubernetes-with-openshift-and-ibm-watson-watch-january-2020-online-meetup-recordings/
cover_image: https://dev-to-uploads.s3.amazonaws.com/i/73oz98vttnzam3glfi6y.png
---

[IBM Developer SF team](https://www.meetup.com/IBM-Developer-SF-Bay-Area-Meetup/) hosts weekly online meetups on various topics. [Online events are one of the best ways to scale](https://dev.to/ibmdeveloper/using-online-meetups-to-scale-your-developer-relations-program-17li) your Developer Relations program and reach developers anywhere, anytime and for a long time after the event.

The following online meetups we hosted in January 2020 with links to watch the recordings.  I also encourage you to join our [meetup](https://www.meetup.com/IBM-Developer-SF-Bay-Area-Meetup/) so you will always know when our online meetups are scheduled. Our meetups are hosted by the wonderful [Lisa Jung](https://twitter.com/LisaHJung) 👋.

**📹 Real-Time Voice Applications with Glitch, Nexmo and IBM Watson (January 8, 2020)**

In this hands-on online meetup with [Dave Nugent](https://twitter.com/drnugent)developers learned:

- How to connect Nexmo and IBM Watson through RESTful APIs and WebSockets
- The benefits of serverless integrations
- How to do do real-time analysis of audio streams
- Performing speech-to-text decompositions on raw audio data
- Performing sentiment analysis on the resulting text
- Visualizing the resulting values

[Watch the recording](https://www.crowdcast.io/e/real-time-voice) 📺

**📹 Removing Unfair Bias in Machine Learning (January 15, 2020)**

In this online meetup with [Upkar Lidder](https://twitter.com/lidderupk) developers introduced and analyzing data using Python, Pandas and Jupyter Notebooks. Developers also learned:

- How to measure bias in your data sets & models
- How to apply the fairness algorithms to reduce bias
- An introductory look at how bias & discrimination can arise within modern machine learning techniques and the methods that can be implemented to tackle those challenges
- Learn how to evaluate the metrics using the open-source AI Fairness 360 Toolkit to check for fairness and mitigate machine learning model bias

[Watch the recording](https://www.crowdcast.io/e/removing-unfair-bias-in) 📺

**📹 Kubernetes hands-on workshop with OpenShift on IBM Cloud (January 22, 2020)**

With Red Hat OpenShift on IBM Cloud, developers have a fast and secure way to containerize and deploy enterprise workloads in Kubernetes clusters. OpenShift clusters build on Kubernetes container orchestration that offers consistency and flexibility for your development lifecycle operations

In this hands-on online meetup with [Marek Sadowski](https://twitter.com/blumareks) developers learned:

- Introduction Red Hat OpenShift
- How OpenShift is deployed on IBM Cloud
- How to deploy applications on and with OpenShift
- How to build a microservice with Java and how to deploy it to OpenShift on the IBM Cloud

[Watch the recording](https://www.crowdcast.io/e/kubernetes-hands-on-with) 📺

**📹 Deep Learning Master Class I – Introduction (January 30, 2020)**

This is the 1st online meetup in a 5-part series with [Lavanya Shukla](https://twitter.com/lavanyaai/) and [Upkar Lidder](https://twitter.com/lidderupk).

Neural networks are powerful beasts that give you a lot of levers to tweak! The sheer size of customizations that they offer can be overwhelming to even seasoned practitioners. In this talk, Lavanya will give you a framework for making smart decisions about your neural network architecture!

Developers will explore lots of different facets of neural networks in this talk, including how to setup a basic neural network (including choosing the number of hidden layers, hidden neurons, batch sizes etc.). Developers will learn about the role momentum and learning rates play in influencing model performance. And finally you will explore the problem of vanishing gradients and how to tackle it using non-saturating activation functions, BatchNorm, better weight initialization techniques and early stopping.

[Watch the recording](https://www.crowdcast.io/e/deep-learning-master) 📺