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Machine Learning Systems Pt. 1: Overview and Challenges
In 2015, a Machine Learning research paper made massive waves discussing the “Hidden Technical Debt in Machine Learning Systems”. In this paper, Sculley et. al. highlighted how the code to build a machine learning model is a really small piece of the entirety of the project.... Read more
Four Problems and Solutions Responsive MT Will Address
Editor’s note: Dr. Arle Lommel is a speaker for ODSC East 2022. Be sure to check out his talk, “How Can We Make Machine Translation Responsive and Responsible?” to learn more about responsive MT! Machine translation (MT) has become ubiquitous as a technology that enables individuals... Read more
From Clipboard to DataFrame with Pandas
When I write about a library or a new concept, I typically like to showcase its working via examples. The source of datasets that I use in my articles varies widely. Sometimes I create simple toy datasets, while on other occasions, I go with the established... Read more
Multi-step Time Series Forecasting with ARIMA, LightGBM, and Prophet
Time series forecasting is a quite common topic in the data science field. Companies use forecasting models to get a clearer view of their future business. Choosing the right algorithm might be one of the hard decisions when you develop time series forecasting model. In this... Read more
Azure for Machine Learning Engineers
As more and more companies decide to move their on-premises datacenters to the cloud, cloud skills are now becoming increasingly important. In 2020, Microsoft Azure was declared the fastest growing cloud provider and therefore I decided to challenge myself to learn more about their Data... Read more
Why is Adversarial Robustness Critical for Machine Learning? 
As machine learning (ML) gets adopted in every field and every possible use case, a threat lurks underneath. In technology, security doesn’t become a primary concern until adoption reaches a tipping point like it did with consumer software three decades ago and the internet over two.... Read more
Creating Managed Online Endpoints in Azure ML
Suppose you’ve trained a machine learning model to accomplish some task, and you’d now like to provide that model’s inference capabilities as a service. Maybe you’re writing an application of your own that will rely on this service, or perhaps you want to make the service... Read more
Optimizing Your Model for Inference with PyTorch Quantization
Editor’s Note: Jerry is a speaker for ODSC East 2022. Be sure to check out his talk, “Quantization in PyTorch,” to learn more about PyTorch quantization! Quantization is a common technique that people use to make their model run faster, with lower memory footprint and lower... Read more
Master Machine Learning: Multiple Linear Regression From Scratch With Python
Linear regression is the simplest algorithm you’ll encounter while studying machine learning. Multiple linear regression is similar to the simple linear regression covered last week — the only difference being multiple slope parameters. How many? Well, that depends on how many input features there are — but more on that... Read more
Creating Spectrograms and Scaleograms for Signal Classification
In this post, I’ll explain how to convert time-series signals into spectrograms and scaleograms, which are image representations of those signals that contain both frequency and time information. In a future post, we’ll use the images created here to classify the signals. I’ll explain the intuition... Read more