First of all, let me get this straight: I've never been a big fan of Azure. I'm rather an AWS hardcore fan, love the way it's built, its reliability, and the design focus on infrastructure. But every once in a while, it's good to open your chakras and try new things. I recently worked with … Continue reading Scaling a Software Team: Feedback on Azure DevOps →

Crypto trading has been all the rage over the past few years, and people tend to forget that there are many many more trading opportunities in the real world than in the crypto world. Unfortunately, the real world is often using outdated tech and it can be a real pain to finally manage to perform … Continue reading Trading Automation with Interactive Broker API, Python and Docker →

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 →

Let's face it: nowadays big cloud providers have freed many companies from hardware. With cloud instances and object storage, CTOs don't have to worry anymore about hardware failures and redundancies. But the likes of Amazon, Azure etc did not stop there. Most providers also provide plenty of extra services (container orchestration, hosted database, "serverless" features … Continue reading Tech Independence: the Case for Excellence →

Given a set of specs (specifications), an engineer usually tries to come up with the optimal architecture for those specs. As we all know, specs tend to change. The true understanding of a problem usually comes after you've started working on it. Like Mike Tyson used to say, everyone has a plan, until they get … Continue reading Good vs Bad Legacy Code →