Explore >> Select a destination
|
You are here 
|
stribny.name | ||
| | | | |
www.altexsoft.com
|
|
| | | | | A dive into the machine learning pipeline on the production stage: the description of architecture, tools, and general flow of the model deployment. | |
| | | | |
your-docusaurus-site.example.com
|
|
| | | | | Creative Applications of MLflow Pyfunc in Machine Learning Projects | |
| | | | |
tomaugspurger.net
|
|
| | | | | This work is supported by Anaconda Inc. and the Data Driven Discovery Initiative from the Moore Foundation. Anaconda is interested in scaling the scientific python ecosystem. My current focus is on out-of-core, parallel, and distributed machine learning. This series of posts will introduce those concepts, explore what we have available today, and track the community's efforts to push the boundaries. You can download a Jupyter notebook demonstrating the analysis here. Constraints I am (or was, anyway) an economist, and economists like to think in terms of constraints. How are we constrained by scale? The two main ones I can think of are | |
| | | | |
tomasvotruba.com
|
|
| | | Artificial intelligence is at boom the last couple of years. I wrote about AI and its relation to [jobs development](/blog/2017/12/04/life30-what-will-you-do-when-ai-takes-over-the-world/) in following years. Now we'll try to look a bit closer. Not in time, but in space. Take a look around you, what do you see? Do you see *your own first* AI already? And how do you treat it? | ||
