Testing deep learning neural networks on public datasets is fun, but it's usually on unseen data that you can really see how the published techniques really perform. Recently, I was trying to detect human faces on Game of Thrones footage. I was surprised to see that the most widely used techniques didn't fare really well. … Continue reading State of the art (2019) face detection with RetinaFace and MXNet
I've been lately regularly working with strategy consultancies, on data and AI matters. While corporate ambitions everywhere around data/AI are rising, when it comes to day-to-day operations, most of the data is still fragmented among many databases, and people use Excel sheets as exchange format. This in itself is interesting, because it means a corporate … Continue reading Excel to Pandas to PostgreSQL: Data Science for Strategy Consulting
AWS recently unveiled Outposts, a way to deploy on-premises servers that blend seamlessly in AWS cloud, and Snowball, a rugged server with a local EC2 and S3, that can be deployed anywhere on the planet even without connectivity. Apart from the engineering achievement, what’s interesting with those 2 announcements is that AWS seems to be … Continue reading AWS Outposts, Snowball: Precursors of AWS Region-as-a-service?
Every once in a while, you see an impressive tech arrive on the market. Usually the engineer in me is blown away by its elegance and/or the freshness of the technical approach to a problem. But my inner businessman wonders whether the company founders will manage to propose a compelling business case to their customer … Continue reading What Happened to Cloud Paging Technology?
A few years ago, I ran a cloud rendering startup that worked with VFX studios. We worked with a few mid-size studios, that used Windows on all their machines. We were faced with the challenge of deploying Windows server farms, and find practical ways to install programmatically heavy software (3dsMax, Maya, V-ray...) on all the … Continue reading Spoon: Containerization Before it was Cool, on Windows
After years of working with industry on large-scale numerical simulations, VFX studios on massive rendering, and other compute-intensive stuff such as AI-powered satellite image analysis, I have now 1 habit: I'm always looking for the biggest bang for the buck, preferably cloud-based since I hate operating hardware. When it comes to HPC (high-performance computing), Xeon … Continue reading Hijacking the Blade Shadow PC for Deep Learning
Once you're past the R&D phase, you have working deep neural nets, and you need to run them somewhere. If you want to avoid headaches with dependencies, you probably chose to make docker images out of your neural nets, with Tensorflow or equivalent + your code + your neural net weights. Problem is, if you … Continue reading Docker Registry Distribution in a Deep Learning Pipeline