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THE ML ENGINEER — WEEKLY NEWSLETTER

The MachineLearning EngineerIssue #13

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Issue #1310/03/19ml-educationml-research
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Issue #13 🤖 - A deep dive on ML versioning, How to become an ML engineer, Developing competence in DL, 🚀 beyond Jupyter with Jupytext, Tensorflow 2.0 Alpha is out, Adversarial drawing with GANs + more 🚀

This week in Issue #13:

A deep dive on machine learning versioning, how to become an ML engineer, developing competence in deep learning, 🚀 beyond Jupyter with Jupytext, launch of Tensorflow 2.0 Alpha, adversarial drawings with GANs, upcoming AI conferences, ML jobs and more!

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A deep dive on ML versioning

“Productionizing machine learning/AI/data science is a challenge. Not only are the outputs of machine-learning algorithms often compiled artifacts that need to be incorporated into existing production services, the languages and techniques used to develop these models are usually very different than those used in building the actual service.” Great blog post that provides an overview of the challenges and approaches to our #EthicalML Principle #3 model and data versioning.

How to become an ML engineer

A very comprehensible guide that introduces the key skills required in machine learning engineering roles based on what is outlined in job descriptions and day to day work routines. This post dives into the skills that are often sought in MLE roles such as theory knowledge, data wrangling, good software practices, etc. It also covers several resources to explore your path further and help you train your skills in these areas.

Developing competence in DL

Supercharge your deep learning skills and portfolio with this great post by machine learning mastery. The post includes an overview of how you are able to build a portfolio of projects to demonstrate your skills, approaching multiple smaller projects to maximise real-world problem solving skills, and a three-level competence framework that can be followed for mastery in data, technique and application.

🚀 beyond Jupyter with Jupytext

Mark Wouts brings us a completely awesome library to represent, save and extend your good old Jupyter notebooks in plain markdown. The potential of this project is outlined in his Towards Data Science post, where he shows how this library shines in the area of collaboration. With Jupytext, committing local changes may no longer look like abstract merges, and instead they will be more intuitive additions.

Tensorflow 2.0 Alpha is out

An exciting milestone for one of the most widely adopted libraries in deep learning. In this great post released by the core team, they cover some of the high level objectives. The main point they mention is that Tensorflow 2.0 will see all the components that have been build separately brought in together as a comprehensible unified platform. This includes all its flavours, such as Tensorflow Serving, Tensorflow Lite, Keras, tensorflow JS and beyond. In this blog post, Iorni showcases key Tensorflow 2.0 (experimental) features by building a deep reinforcement model.

Adversarial drawing with GANs

A researcher from the ETRI Research Institute in Korea brings us an exciting piece of research, where he presents a “novel image editing system that generates images as the user provides free-form mask, sketch and color as an input.” This basically allows you to scribble on top of someone’s portrait photo, such as a pair of glasses or a simple grin, and the trained GAN will modify the picture with the addition of the most probable interpretation of the scribble - in this example, making the subject smile, or adding realistic glasses. The codebase is also open source and available online, big shoutout for the push on reproducibility.

MLOps = Featured OS Libraries

We are excited to see the Awesome MLOps list growing to almost 300 stars now! Thanks to everyone for your support! This week’s edition is focused on new libraries on Reproducibility which fall on our Responsible ML Principle #4. The four featured libraries this week are:

  • MLFlow - Open source platform to manage the ML lifecycle, including experimentation, reproducibility and deployment.
  • Sacred - Tool to help you configure, organize, log and reproduce machine learning experiments.
  • FGLab -Machine learning dashboard, designed to make prototyping experiments easier.
  • StudioML - Model management framework which minimizes the overhead involved with scheduling, running, monitoring and managing artifacts of your machine learning experiments.

If you know of any libraries that are not in the “Awesome MLOps” list, please do give us a heads up or feel free to add a pull request!

MLConf = Conferences & Events

We feature conferences that have core ML tracks (primarily in Europe for now) to help our community stay up to date with great events coming up.

Technical Conferences

  • DataFest19 [11/03/2019] - Two week festival of Data Innovation hosted across Scotland, UK.

  • PyCon + PyData Florence [02/05/2019] - Python X comes this year with a PyData focus in Florence, Italy.

  • AI Conference Beijing [18/06/2019] - O’Reilly’s signature applied AI conference in Asia in Beijing, China.

  • RAAIS 2019 [28/06/2019] - The Research and Applied AI Summit in London, UK

  • Data Natives [21/11/2019] - Data conference in Berlin, Germany.

  • ODSC Europe [19/11/2019] - The Open Data Science Conference in London, UK.

Business Conferences

MLJobs = Jobs & Careers

We showcase Machine Learning Engineering jobs (primarily in London for now) to help our community stay up to date with great opportunities that come up. It seems that the demand for data scientists continues to rise!

Junior Opportunities

Mid-level Opportunities

Leadership Opportunities