Natural Language Automation
How to quickly and easily experiment with advanced NLP in the cloud with Google Cloud and Machine Learning
In the past week (and a few days) a team and I ventured down the path of legal document analysis as part of a research project. The idea was to explore and prove an approach to analysing documents using a machine instead of a subject matter expert. This started off as a simple natural language processing (NLP) task but lead us down a path into more and more advanced text analysis. It pushed our NLP skills to the limit as we entered into the wonderful world of transformers.
Mental health in the workplace
How the workplace can affect the mentality of its employees
I’d like to talk to you about mental illness, its relationship to work, and the benefits of creating a safer, healthier workplace for everyone. This can be a sensitive topic to talk about, but it is entirely worth discussing in order to bring attention to the stigma that still lingers around mental illness.
This is the first part of a multi-part blog series about the problem mental health in workplaces. I will be discussing how we can begin to change how we work in order to improve the quality of life of everyone, and even increase businesses’ bottom line.
Let’s…
The Secrets of Orchestration in the Cloud
Mistakes made and lessons learnt in a year of using Airflow
Airflow, Airflow, Airflow… how I love and hate thee. The siren calls of scale and flexibility tempt me, even as I have been scalded by my trust in you. As Airflow projects of the future loom, I am reminded constantly of the past. I hear the bellows of my projects booming in the wind. They call to me. Bound in irons. Doomed for eternity. They clasp and beg at me, asking:
“Why did you think a dynamic mega-DAG was a good idea?”
I dismiss them as the nags of the past. I cannot give them the attention that they demand…
A better approach to devops
Google’s answer to reliability
In late 2018, I took part in a workshop run by Google to teach their solution to reliability in large, distributed systems — Site Reliability Engineering. This structure provided a different perspective on how a product should be planned, developed, and operated. Through defining clear internal benchmarks for reliability (SLOs), a product’s performance can be tracked (SLIs) and problems can be intercepted before an outage occurs. Their vision went further than performance metrics though. Google would have the traditional project team structure completely revolutionised with greater transparency and communication between the product, development, and operations teams. …
Matthew Grey
Technology engineer keen on big data, automation, streaming, and natural language processing. Currently focused on solutions in Google Cloud.
