在美国新冠疫情爆发之前，我偶尔也在家工作。因为我平时通勤的时间很长（单程90分钟），所以每次在家工作的时候，都觉得偷得浮生半日闲，可以用省下的时间读读书，做做瑜伽，或者睡个懒觉。我从来没想过在家工作会成为新常态，也没有做好在家打持久战的准备。同样没有做好准备的是我的猫，Maru，它估计在想“铲屎官怎么还不出门给我赚钱？她居然敢坐在我睡觉的御座上？能不能不要在我打盹的时候骚扰我啊！！！”
Author: Ju
Adapt and Thrive: thoughts after working from home for 3 months
In the pre-COVID19 era, I used to work from home occasionally and treated it as a break from my long daily commute (90 min one-way). I spent the saved time reading, exercising, or sleeping. I never thought that working from home would be the new norm and I was not prepared for a seamless work & life, nor was my cat Maru – he was probably thinking “why is this hooman being not hunting outside? why is she still sitting on my sleeping chair? and stop disturbing my day-time nap!!!”
Distributed model training II: Parameter Server and AllReduce
In the previous post, I talked about using MapReduce and Spark for distributed model training. In this post, I will talk about parameter server and how it is used in distributed model training.
Distributed model training I: MapReduce and Spark
In the previous post, I introduced challenges in machine learning systems with big data and complex models. In this post, I will discuss distributed systems in the era of big data.
Think big: ML systems in the era of big data
Let’s start with linear regression. Using established libraries such as scikit-learn, it is almost trivial to train a linear regression model. We can easily run the model training with a few hundred Megabytes of data on our laptop with a build-in CPU.
Now let’s think big.
Machine Learning Lifecycle
In business management, product lifecycle is broken into 4 stages with the distinct pattern of sales over time: introduction, growth, mature, and decline. In the diagram below, I adapt the classic product lifecycle curve to show the engineering load over time in machine learning (ML): from model development to maintenance. Managing and coordinating different stages in ML lifecycle presents pressing challenges for ML practitioners.
5 things you need to know about Machine Learning Systems
The more I work on building end-to-end machine learning (ML) pipelines, the more I realize the importance of system design and infrastructure. ML shares many concerns with traditional software development, and poses new challenges to system design.
Onboarding as a Machine Learning Engineer
It’s been 3 months since I started my new position as a Machine Learning Engineer (MLE) at Spotify. What I like most about this position is that I get to participate in building an end-to-end pipeline, including ideation and experiment, data engineering, machine learning modeling, model serving, online A/B test, monitoring, and many more.