Valentin Deleplace | Devoxx

Devoxx Belgium 2018
from Monday 12 November to Friday 16 November 2018.

Valentin is a senior developer for backends, databases, cloud architecture, performance, algorithms, and UX. He currently works as a DA (developer advocate) for Google Cloud Platform.

See also https://cloudplatform.googleblog.com/

Computer vision: British clay

Tools-in-Action

The artificial vision spectrum ranges from fully automated generic Image Content Analysis (just give a picture and get answers, no configuration needed!) to fine-tuned neural network frameworks.

In-between, AutoML techniques let you build high-accuracy prediction systems focused on your use case, discovering and learning the topology of your data of interest, without writing any line of code at all. For image classification, this typically involves providing hundreds of known photos to a platform to train a model, and then use this model to infer characteristics of new pictures. Supervised learning at its simplest: just show labeled samples, and you get a model.

I will demo several recent tools that help build a model from a collection of pictures and a list of labels, and compare the quality of the results. Each may have its own answer to this fundamental question: Is it easier to detect Wallace or to detect Gromit?

MADPIG: Measure and deepen performance in Go

Deep Dive

Do you care about speed? Do your users care about latency? When performance matters, theory and intuition are initially useful guides, but soon the rubber meets the road. Write benchmarks, try several approaches, optimize at different levels of abstraction, find the bottleneck, repeat! The Go tooling makes performance exploration approachable and exciting. Use Pprof to discover where the time is actually spent. It's often not where you think! Consider writing concurrent code, when the benefits exceed the costs. Use Trace to peek at your CPU cores: Why are they sometimes idle? Where do all these context switches come from? Is the GC responsible for my slowdown? Write good tests, to preserve semantics across incremental refactorings. Learn about the benchmarking idioms of the testing package. Run the Race detector, understand what it does and why it matters. Examine test code coverage to discover dead code and hot paths. What the hell is a Flame Graph? Factor in the trade-offs of memory allocation, regexps, maps, random numbers, I/O, stdlib. Also, consider when it is wise not to optimize!

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