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Successful machine learning models are developed to serve and bring value to an end user. Whether the end user is a customer or domain expert, the full value of data science is only realized by operationalizing the workflow and exposing model predictions and insights to the end user. End users consume and interact with models in different contexts—via a web application, mobile application, or a command line API request.

Continue reading Deploying a Text Classification Model Using Flask and Vue.js

Advancements in the power of machine learning have brought with them major data privacy concerns. This is especially true when it comes to training machine learning models with data obtained from the interaction of users with devices such as smartphones.

So the big question is, how do we train and improve these on-device machine learning models without sharing personally-identifiable data? That is the question that we’ll seek to answer in this look at a technique known as federated learning.

Continue reading Federated Learning: An Introduction

Slow build speed is a huge momentum buster. It’s kind of like driving down the road and constantly hitting speed bump after speed bump. Build speed is critical to your productivity, and over the past few years, the Android team has consistently focused on improving build speed performance.

After implementing the tips in this article, we’ll be able to speed up development build times by 3x, 4x—sometimes even up to 10x. All by simply applying a set of best practices.

Continue reading 10 Tips to Speed Up Android Gradle Build Times

Every data science task needs data. Specifically, data that’s clean and understandable by the system it’s being fed into. When it comes to images, a computer needs to see what human eyes see.

For example, humans have the ability to identify and classify objects. Similarly, we can use computer vision to interpret visual data that it receives. That’s where image annotation comes into the picture.

Continue reading Data Annotation Fundamentals (Part 1) — Image Annotation

NumPy supports different ways of generating arrays, and this tutorial is going to explore one way of do so, using the np.linspace() function. It returns evenly-spaced numbers and can generate arrays of any dimensionality.

The following sections are covered in the tutorial:

linspace() is a function that is not only available in NumPy but also available in other tools such as MATLAB. By searching about it, you can find the regular answer which defines linspace() as a function to generate evenly spaced numbers.

Continue reading Exploring NumPy’s linspace() function

Computer vision, at its core, is about understanding images. The field has seen rapid growth over the last few years, especially due to deep learning and the ability to detect obstacles, segment images, or extract relevant context from a given scene.

Using computer vision, we can build autonomous cars, smart buildings, fashion recommender systems, augmented reality tools…the possibilities are endless.

Continue reading Computer Vision and Deep Learning: From Image to Video Analysis

With augmented reality rocking every corner of the App Store and touching every category from gaming to healthcare, now is a better time than ever to jump in and start building your own AR experiences. In this tutorial, you’ll learn to set up and use Reality Composer to bring Mars into your home using the power of augmented reality. Later, you can apply this knowledge to build more advanced AR experiences.

Continue reading Apple’s Reality Composer: Bring Mars into Your Living Room with AR

The process of uploading iOS binary files to the App Store is time-consuming and (let’s face it) boring. And Apple hasn’t provided better options for this process. As of now, we’ve used various tools to upload .ipa files to App Store Connect. The most common method for solo iOS developers is to upload directly from Xcode or use the Application Loader tool, which is integrated in Xcode.

Continue reading Full Stack iOS Continuous Delivery with xcodebuild and ExportOptions Plist

Sales are the lifeblood of a business. Sales forecasting is, therefore, a crucial part of any business’s financial planning.

In this tutorial, we look at one specific sales forecasting example: public bus ticket sales. Accurately predicting the demand for bus ticket sales can enable commuter companies to make informed decisions regarding their operations. The companies are able to gauge customer demand for certain rides, to manage resources and vehicles more efficiently, to offer promotions and sell other services more effectively, such as micro-insurance, or even improve customer service by being able to efficiently communicate alerts and other useful information.

Continue reading Using Machine Learning to Predict Bus Ticket Sales