source{d}

Data Platform for the Software Development Life Cycle. #EngineeringEffectiveness #MLonCode⠀| GitHub: website:

[source{d} @sourcedtech]

In case you missed today exciting news from @sourcedtech 🎉📢🗞️

- Private beta of @sourcedtech Community Edition is now open - more info in this blog https://t.co/Vjm7T9EEdy

- @sourcedtech Enterprise Edition is now GA
https://t.co/LVM0PBuIDU

#CodeAsData #EngineeringOps #SDLC

[source{d} @sourcedtech]

source{d} analysis of the @cloudfoundry codebase: The results show a mature and complex architecture yet extraordinarily active and agile. https://t.co/LT8ofsgmtZ #MLonCode

[source{d} @sourcedtech]

Data Retrieval pipeline at source{d}
"Data collection and processing might be less sexy than #MachineLearning but nevertheless is crucial for any progress" https://t.co/QyHDWJNduK #MLonCode

[source{d} @sourcedtech]

Managing with Context, Not Control by @aspyker, #Engineering Manager, @netflix Container Platform https://t.co/AgEHuNPT2n

[source{d} @sourcedtech]

Software Delivery Performance, Multi-GPU #DeepLearning and Overton https://t.co/wEAc6kBcI4

[source{d} @sourcedtech]

How do you know if your software is good enough? @rich_archbold, VP of #Engineering @intercom shares his engineering standards that will help protect you from easily avoidable mistakes https://t.co/5ItSuOhM8p #DevOps

[source{d} @sourcedtech]

We analyzed all the @TensorFlow git repositories with source{d} EE to extract interesting insights for the Tensorflow community. Check out our summary here: https://t.co/mYcleqKDa1

View the dashboard of the entire analysis here: https://t.co/Pvtg2YCnma #MLonCode

[P.-E. Fraisse @p_e_fraisse]

👏 Not only for architects but for a whole organization This is what I am used to call #CodeAsData from a few years.
Hope to be able to spend time on it very soon! #MLOnCode @eisokant @natfriedman @sourcedtech @Semmle

[source{d} @sourcedtech]

"Many of the threats facing #DevOps and #Agile transformation fall into four main camps."
1. No sense of urgency and an unclear vision
2. Lack of senior stakeholder sponsorship
3. Cultural resistance to change
4. Not understanding the change maturity curve https://t.co/3CKv973ROw

[source{d} @sourcedtech]

💥 "...Vendors such as source{d} are enabling developers to build increasingly complex software faster, with high quality, and better user experience.” 💥@marksdriver , @Gartner_inc https://t.co/JA3gyEkQkT #MLonCode #GartnerCoolVendor

[source{d} @sourcedtech]

Yesterday we shared Part 1. Here is Part 2 of "Clean code... Why bother?" https://t.co/064QzKEmdz by @pauxdsantamaria, co-founder of @uppernauts

1. Clean code leads to better practices
2. Reviewing PRs is hard work
3. Reusability
4. Faster bug fixing

[source{d} @sourcedtech]

Check out source{d} Lead #MachineLearning Engineer, @vadimlearning's article on @kdnuggets "What is #MLonCode?" https://t.co/ugzw8JL6OD 🤖🎓 💻 📄

[source{d} @sourcedtech]

@sourcedtech CEO, @Eisokant had his article, "How #MLonCode Could Disrupt #SoftwareDevelopment," published on @EnterpriseAI_ https://t.co/FXJxe6ESKC

[source{d} @sourcedtech]

"Clean Code... Why bother?" https://t.co/mUbh2vduYE via @pauxdsantamaria, Co-Founder of @uppernauts
1. Your teammates will thank you
2. Think about your future self
3. Messy code tends to get messier
4. Faster decision making
5. Reduce repeated code
6. It feels great!

[source{d} @sourcedtech]

Paper Review: "Import2vec - Learning Embeddings for Software Libraries" by Hugo Mougard, Senior Machine Learning Engineer at source{d}
https://t.co/q5gmbMLMOM #MLonCode

[source{d} @sourcedtech]

Learn how to "get the product feeling great, the #Engineering team feeling productive and proud of delivering a high-quality product, pumping out features while keeping the bugs down" by former @facebook and @YouTube, @radoshi https://t.co/FRB0xF5ZsX 💻 💡 🎓🐜

[source{d} @sourcedtech]

We analyzed all the @TensorFlow git repositories with source{d} EE to extract interesting insights for the Tensorflow community. Check out our summary here: https://t.co/WAkY8hwM0F

View the dashboard of the entire analysis here: https://t.co/pb8W4Z5fBc #MLonCode

[source{d} @sourcedtech]

Paper review: "code2seq - generating sequences from structured representations of code" by source{d} Engineer, @seoul_engineer https://t.co/utN4uHguaP #MLonCode

[source{d} @sourcedtech]

Multi-GPU #DeepLearning at source{d}
Learn how we solved several problems in order to train neural networks with @Tensorflow 2.0 on several local GPUs in our ML cluster. https://t.co/i89kRAzKWp #MLonCode

[source{d} @sourcedtech]

Top 4 metrics to measure your #Software Delivery Performance
1. Change Lead Time
2. Deployment Frequency
3. Change Failure Rate
4. Mean Time to Restore (MTTR)
https://t.co/Ni6W41ThqB #EngineeringObservability

[source{d} @sourcedtech]

🎓 #DeepLearning on #Dataframes with @PyTorch

"The goal of this post is to lay out a framework that could get you up and running with deep learning predictions on any dataframe using PyTorch and Pandas." via Mike Chaykowsky https://t.co/KgvW5jrjCZ