Why pipenv > venv20181016154501

Why pipenv > venv

Pete GarcinOctober 16, 2018, 0 comments
Pipenv was first released as an experiment way back in January of 2017 by Kenneth Reitz. Even though pipenv is a package that attempts to marry the be...
Golang Module vs Dep: Pros & Cons20180928154812

Golang Module vs Dep: Pros & Cons

Pete GarcinSeptember 28, 2018, , 0 comments
The Golang (Go) community is a passionate one. That passion results in excellent discussions and lots of great ideas, especially when it comes to impr...
OSCON 2018: Distributed Apps & Composability20180809180358

OSCON 2018: Distributed Apps & Composability

Image source: www.flickr.com The 20th anniversary of the O’Reilly Open Source Convention (OSCON) brought a few surprises with it. The dominance ...
Python 3.7: Gains that Eliminate Pains20180802214501

Python 3.7: Gains that Eliminate Pains

In development for 10 months, Python 3.7 has just been released. For long time Python users, it’s a welcome addition, providing many quality of life...
Data is the foundation of your ML strategy20180412020008

Data is the foundation of your ML strategy

Pete GarcinApril 12, 2018, , 0 comments
When getting started with machine learning (ML) you need data — and lots of it. Data really forms the foundation of your machine learning strate...
Reproducible Builds: Introducing predictability into your pipeline20180301160604

Reproducible Builds: Introducing predictability into your pipeline

*This blog has been republished from ActiveState’s Medium Blog. Can your build pipeline perfectly reproduce a build including its critical ...
Poodle, Pug or Weiner Dog? Deploying a Dog Identification TensorFlow Model Using Python and Flask20180110210017

Poodle, Pug or Weiner Dog? Deploying a Dog Identification TensorFlow Model Using Python and Flask

Pete GarcinJanuary 10, 2018, , , 0 comments
In my last post I surveyed the growing array of options for deploying your ML models into production. In this post, we’ll create a demo to see how s...
Operationalizing Machine Learning20171129140000

Operationalizing Machine Learning

Pete GarcinNovember 29, 2017, , , , , 0 comments
Machine Learning (ML) powers an increasing number of the applications and services that we use daily. For organizations who are beginning to leverage ...
Optimizing Machine Learning with TensorFlow20171122140120

Optimizing Machine Learning with TensorFlow

In our webinar “Optimizing Machine Learning with TensorFlow” we gave an overview of some of the impressive optimizations Intel has made to TensorF...
Dealing with Ruby Dependency Conflicts20170914130241

Dealing with Ruby Dependency Conflicts

Dependency hell: If you’ve done any significant amount of programming or system administration, no matter which framework you used, you’ve been th...
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