The MachineLearning EngineerIssue #17

This week in Issue #17:

The SpaCy universe of resources, introducing Plotly Express, preprocessing images with Keras, calling out statistical significance, time series with tensorflow probability, massive multi-task learning, data streaming libraries, upcoming AI conferences, new Machine Learning jobs and more 🚀.

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The SpaCy universe of resources

The industrial-strength NLP framework SpaCy is known not only for its great features, but also for the awesome learning/documentation resources available. ExplosionAI cofounder Ines Montani shares two incredibly useful resources, 1) a two page SpaCy cheatsheet, and 2) a fully fledged SpaCy hands on online course. If that’s not cool enough, SpaCy is hosting their first IRL 2-day conference on the 4th of July in Berlin which we have featured in our upcoming ML conferences list below.

Preprocessing images with Keras

Check out this great tutorial on pre-processing for image data using Keras. Great resource which provides hands on examples on how to use some of the most common pre-processing approaches to image data, including normalising, centering and standardising images. This article contains details on: 1) How to configure and a use the ImageDataGenerator class (in keras) for train, validation, and test datasets of images. 2) How to use the ImageDataGenerator to normalize pixel values when fitting and evaluating a convolutional neural network model. 3) How to use the ImageDataGenerator to center and standardize pixel values when fitting and evaluating a convolutional neural network model.

Calling out statistical significance

The international journal of science “Nature” has released a very interesting article which brings attention to a key challenge in the scientific community, and it is “calling to retire statistical significance and use confidence intervals” instead. The article argues that significant p-values may not always be fully representative - an issue which has led to overhyped claims and even the dismissal of possibly crucial effects. There are some really great initiatives in the machine learning community which help raise the bar for quality through reproducibility of results to ensure they can be evaluated properly: one very exciting initiative which we mentioned a few weeks ago is Papers With Code, which just released a new feature to provide GitHub badges that show SotA performance.

✨ Introducing Plotly Express ✨

Great announcement of a simple and high level wrapper for Plotly.py - Plotly Express. It exposes a simple syntax for complex charts. Inspired by Seaborn and ggplot2, it was specifically designed to have a terse, consistent and easy-to-learn API. with This library has been added to the visualisation frameworks section in our Awesome Machine Learning list. Plotly express includes faceting, maps, animations, and trendlines. Check Plotly Express, as well as the fully fledged Plotly documentation.

Time series with TF Probability

Forecasting can be an incredibly valuable analytical skill to have in your toolset. This short article provides a great introduction to the family of probability models for time series called “structural time series models” - this family of models encompass autoregressive processes, moving averages, local linear trends, seasonality and regression. The article also provides a hands on example using the Tensorflow Probability library, forcasting CO2 Concentration using data from the Mauna Loa observatory in Hawaii.

Massive Multi-Task Learning

A group of Stanford researchers release an update on their work with Snorkel MeTal to tackle massive multi-task learning in natural language understanding. In this post, they talk about how they use Snorkel MeTaL to construct a simple model (pretrained BERT + linear task heads) and incorporate a variety of supervision signals (traditional supervision, transfer learning, multi-task learning, weak supervision, and ensembling) in a Massive Multi-Task Learning (MMTL) setting, achieving a new state-of-the-art score on the GLUE Benchmark and four of its nine component tasks (CoLA, SST-2, MRPC, STS-B).

MLOps = Featured OS Libraries

We are excited to add a new section to the MLOps library on data stream processing! Data stream processing falls on our Responsible ML Principle #4. The four featured libraries on data stream processing this week are:

• Apache Flink - Open source stream processing framework with powerful stream and batch processing capabilities.
• Faust - Streaming library built on top of Python’s Asyncio library using the async kafka client inspired by the kafka streaming library.
• Kafka Streams - Kafka client library for buliding applications and microservices where the input and output are stored in kafka clusters
• Spark Streaming - Micro-batch processing for streams using the apache spark framework as a backend supporting stateful exactly-once semantics

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.

• Spacy IRL [05/07/2019] - SpaCy NLP’s First F2F Conference in Berlin, Germany.

Business Conferences

• World Summit AI Americas [10/04/2019] - Large scale AI summit in Montreal, Canada.

• Come join our panel on AI Ethics and Tools.

• AI Expo Global [19/04/2019] - Global conference on artificial intelligence in London, UK.

• Come join us at our talk on AI orchestration at scale.

• Predictive Analytics World [18/11/2019] - Conference for Business AI in Berlin, Germany.

• Big Data LDN 2019 [13/11/2019] - Conference for strategy and tech on big data in London, UK.

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

• Seldon is hiring for a Machine Learning / Data Engineer in London
• Migacore is hiring for a Machine Learning Engineer in London
• CloudNC is hiring for a Machine Learning Engineer in London
• Babylon Health is hiring for a Machine Learning Engineer in London
• Chattermill is hiring for a Machine Learning Engineer in London
• PWC is hiring for a Data Scientist in London

Mid-level Opportunities

• Proportunity is hiring for a Senior Machine Learning Engineer in London
• Twitter is hiring for a Senior Machine Learning Engineer in London
• Atlas ML is hiring for a Lead NLP Engineer in London
• StreetBees is hiring for a Senior Data Scientist in London
• Expedia is hiring for a Principal Data Scientist in London
• QuantumBlack is hiring for a Senior Machine Learning Engineer in London
• Tractable is hiring for a Senior Deep Learning Engineer

Leadership Opportunities

• Fractal Labs is hiring for a VP of Engineering in London
• Distributed is hiring for a VP of Engineering in London
• FactMata is hiring for a Head of Machine Learning in London
• Brainpool.ai is hiring for a Head of Machine Learning in London, UK
• Cytora is hiring for a Data Science Director in London