Cloudera Fast Forward is an applied machine learning research and advisory group at Cloudera. We explore newly possible machine learning and artificial intelligence technologies, and help enterprises understand and adopt them, empowering organizations to continually grow and differentiate by leveraging their available data. Enabling ethical and responsible ML outcomes for our customers is one of our highest priorities.
In this quarterly update, we provide an overview of what we’ve been up to this quarter and introduce our latest research endeavors. (If you’d like to receive more frequent updates, please sign up for our monthly newsletter.)
Towards the beginning of April, we hosted a webinar on interpretability entitled Opening the ML Black Box: Deploying Interpretable Models to Business Users. You can catch the replay!
Causality for Machine Learning
Machine learning allows us to detect subtle correlations in large data sets, and use those correlations to make accurate predictions. However, these subtle correlations are often spurious – they exist only in a particular dataset – and the resultant model performs poorly, or gives unexpected results in the real world.
Business decisions should be based on things that are true, not things that are true only in a limited dataset – but the trouble is identifying what is spurious and what is not.
Join us on May 28th at 10:00am PT / 1:00pm ET for a webinar on Causality for Machine Learning. During the webinar, we’ll explain how combining causal inference with machine learning can help us address these problems. We’ll cover:
- when you should think about causality and lessons to apply in your data science practice,
- the latest research at the intersection of machine learning and causality,
- how causal thinking helps us write models that generalize to new circumstances (including an example of the causal approach applied to a computer vision problem), and
- the ethical implications of causality.
NLP for Question Answering
Typically, our applied research culminates in a series of quarterly reports, along with a live webinar demonstrating the prototypes we build in conjunction with that research. This time, instead of waiting until the prototype is finished and the report is polished, we thought it would be fun to invite you to join us while we build.
Experiments and Articles
- Interpretability: LIME and SHAP in prose and code (a CoLab notebook by Victor Dibia)
- Enterprise-Grade ML (an article by Shioulin Sam)
- Machine learning in production: Human error is inevitable, here’s how to prepare. (an article by Ade Adewunmi)
Learn more about Cloudera’s machine learning platform and Cloudera Fast Forward’s service offerings by visiting the links below, and follow us on Twitter for announcements about more new content and upcoming virtual events!