Scaling Machine Learning at Uber with Michelangelo
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Re: Scaling Machine Learning at Uber with Michelangelo
#2I love what Uber does with machines (ML), hate what it (currently) does to people.
We recently potted some models from Stan to Pyro (SVI on PyTorch), and it’s been reallly exciting (except for the dark corner of poutines), it really has the performance of something being used in production, except the occasional nan explosion.
edit we are lazy and use our GitLab CI/CD to drive model development iteration. It’s not as fully featured as what’s in the article but it’s a zero effort start.
Re: Scaling Machine Learning at Uber with Michelangelo
#3It's kinda funny they tout their usage of GPS. I use Uber on a near daily basis and drivers by an large use Google maps. They have out right said "Uber sucks for directions"
And if you use express pools it will always say to go the wrong side of an intersection. I like uber because of the drivers, but their fancy technology is flawed.
Re: Scaling Machine Learning at Uber with Michelangelo
#4It's kinda funny they tout their usage of GPS. I use Uber on a near daily basis and drivers by an large use Google maps. They have out right said "Uber sucks for directions" And if you use express pools it will always say to go the wrong side of an intersection. I like uber because of the drivers, but their fancy technology is flawed.
Indeed. What is even more strange about the use of google maps is that Uber bought Bing maps, I am sure for a hefty sum.
Re: Scaling Machine Learning at Uber with Michelangelo
#5I love what Uber does with machines (ML), hate what it (currently) does to people. We recently potted some models from Stan to Pyro (SVI on PyTorch), and it’s been reallly exciting (except for the dark corner of poutines), it really has the performance of something being used in production, except the occasional nan explosion. edit we are lazy and use our GitLab CI/CD to drive model development iteration. It’s not as…
Can you elaborate a bit more about your usage of GitLab CI/CD for model management/development. I am currently working on a platform [1] that tries to solve some of the issues mentioned in the article, i.e. improving data scientists' productivity and velocity, compare models, solve reproducibility issues...
Re: Scaling Machine Learning at Uber with Michelangelo
#6It's kinda funny they tout their usage of GPS. I use Uber on a near daily basis and drivers by an large use Google maps. They have out right said "Uber sucks for directions" And if you use express pools it will always say to go the wrong side of an intersection. I like uber because of the drivers, but their fancy technology is flawed.
I've never seen an Uber driver not use Waze in London.
Re: Scaling Machine Learning at Uber with Michelangelo
#7It's kinda funny they tout their usage of GPS. I use Uber on a near daily basis and drivers by an large use Google maps. They have out right said "Uber sucks for directions" And if you use express pools it will always say to go the wrong side of an intersection. I like uber because of the drivers, but their fancy technology is flawed.
I've never seen an Uber driver not use Waze in London.
I was in Colombia and South Africa last year. Those Uber drivers also used Waze.
Re: Scaling Machine Learning at Uber with Michelangelo
#8I love what Uber does with machines (ML), hate what it (currently) does to people. We recently potted some models from Stan to Pyro (SVI on PyTorch), and it’s been reallly exciting (except for the dark corner of poutines), it really has the performance of something being used in production, except the occasional nan explosion. edit we are lazy and use our GitLab CI/CD to drive model development iteration. It’s not as…
would love to know what is your model development iteration. especially how you do testing, etc
Re: Scaling Machine Learning at Uber with Michelangelo
#9It's kinda funny they tout their usage of GPS. I use Uber on a near daily basis and drivers by an large use Google maps. They have out right said "Uber sucks for directions" And if you use express pools it will always say to go the wrong side of an intersection. I like uber because of the drivers, but their fancy technology is flawed.
I've never seen an Uber driver not use Waze in London.
In Mexico City I always see them use Waze.
Re: Scaling Machine Learning at Uber with Michelangelo
#10I love what Uber does with machines (ML), hate what it (currently) does to people. We recently potted some models from Stan to Pyro (SVI on PyTorch), and it’s been reallly exciting (except for the dark corner of poutines), it really has the performance of something being used in production, except the occasional nan explosion. edit we are lazy and use our GitLab CI/CD to drive model development iteration. It’s not as…
Can you elaborate a bit more about your usage of GitLab CI/CD for model management/development. I am currently working on a platform [1] that tries to solve some of the issues mentioned in the article, i.e. improving data scientists' productivity and velocity, compare models, solve reproducibility issues... [1] https://github.com/polyaxon/polyaxon
We uh treat models as code, but also have NFS shares setup for the storage and GitLab runner talking to a Slurm cluster to run the models. Results and cross validation upload to GitLab. Main thing we haven’t built out yet are performance dashboards for showing improvement across commits, but with the GitLab APIs that’s a script away (currently we do it by hand)