With the rise of digital transformation and advancements in machine learning (ML), an increasing number of businesses start incorporating artificial intelligence (AI) into their workflow. In research by Deloitte, a considerable number of responders said that cognitive technologies are either "important" or "very important" to both service offerings and internal business processes. Apparently, these companies feel that using AI is central to their ability to develop their businesses and get ahead of their competition. And because the demand for integrative AI is only growing, platforms suggesting no-code or low-code AI development grow in their number as well. This article will define the terms "low-code" and "no-code", will discuss the advantages and disadvantages of this technology, and introduce a list of low-code/no-code platforms for AI and computer vision.

The new Create ML app just announced at WWDC 2019, is an incredibly easy way to train your own personalized machine learning models. All that's required is dragging a folder containing your training data into the tool and Create ML does the rest of the heavy lifting. So how do we prepare our data? When doing image or sound classification we just need to organize the data into folders, but if we want to do object detection the task becomes a bit more complicated. With object detection, we need to specify some additional information.

Create a new Text Classifier Model Project in Create ML and add the training folder. You can choose any of the techniques to train your model and go have a cup of coffee while the training and validation happens. It took me 4 hours to get a Transfer learning model trained. Here's an illustration that compares the model metrics across the two techniques in Text Classification: The transfer learning based model extrapolates better. Though you can try achieving better accuracy by increasing the dataset size(TL model training took four hours on a dataset of 15000 texts for me).

The first thing I compared was what each framework was telling me about their own level of confidence. Models giving themselves their own confidence level is really interesting and yet curious. Stats are always welcomed but my skepticism lies in the fact that I wonder if test data is truly trustworthy and how vulnerable machine learning assumptions are to human error. At first glance, the numbers suggest it's a tight race with Apple having a slight lead. There are a few variables to this, though.

Apple's artificial intelligence strategy continues to be focused on running workloads locally on devices, rather than relying heavily on cloud-based resources, as competitors Google, Amazon, and Microsoft do. While this fits in with Apple's core business -- selling devices -- and the company's emphasis on user privacy, it could put it at a competitive disadvantage with many app makers, who prefer the more flexible approach from competitors as they look at add AI features. Between announcements about iOS, Mac and Siri, Apple made two big announcements about AI at its recent Worldwide Developer Conference in San Jose. First, it introduced a new framework called CreateML that app makers can use to train AI models on Macs. Apple developers can try out Create ML inside the app that many of them are already very familiar with: Xcode, Apple's own app for coding programs for its devices.

If you've been following Apple's announcements from the past year, you know that they are heavily invested in machine learning. Ever since they introduced Core ML last year at WWDC 2017, there are tons of apps which have sprung up which harness the power of machine learning. However, one challenge developers always faced was how to create the models? Luckily, Apple solved our question last winter when they announced the acquisition on Turi Create from GraphLab. Turi Create is Apple's tool which can help developers simplify the creation of their own custom models. With Turi Create, you can build your own custom machine learning models.

Create ML is proof that Apple is committed to making it easier for you to use machine learning models in your apps. In this Create ML tutorial, you'll learn how Create ML speeds up the workflow for improving your model by improving your data while also flattening the learning curve by doing it all in the comfort of Xcode and Swift. You don't need to know how to write a compiler to use Swift, and you don't need to be able to write a new ML algorithm to use a classifier. With Create ML, you have no excuse not to get started! You'll start this Create ML tutorial with the spectacular Create ML party trick: You'll build an image classifier in a GUI, using images from the Kaggle Cats and Dogs Dataset. Then you'll compare this with the Turi Create example that uses the same dataset. As you'll see, Turi Create is more manual, but it's also more flexible and not at all mysterious!

At WWDC Apple released Core ML 2: a new version of their machine learning SDK for iOS devices. The new release of Core ML, whose first version was released in June 2017, should create an inference time speedup of 30% for apps developed using Core ML 2. They achieve this using two techniques call "batch prediction" and "quantization". Batch prediction refers to the practice of predicting for multiple inputs at the same time (e.g. Quantization is the practice of representing weights and activation in fewer bits during inference than during training. During training, you can use floating-point numbers used for weights and activations, but they slow down computation a lot during inference on non-GPU devices.

Apple describes its mobile devices as designed in California and assembled in China. You could also say they were made by the App Store, launched a decade ago next month, a year after the first iPhone. Inviting outsiders to craft useful, entertaining, or even puerile extensions to the iPhone's capabilities transformed the device into the era-defining franchise that enabled Uber and Snapchat. Craig Federighi, Apple's head of software, is tasked with keeping that wellspring of new ideas flowing. One of his main strategies is to get more app developers to use artificial intelligence tools such as recognizing objects in front of an iPhone's camera. The hope is that will spawn a new generation of ideas from Apple's ecosystem of outsourced innovation.

Machine learning (ML) is rapidly becoming a key framework for a new digital future. It seems like every tech firm wants to utilize this subset of artificial intelligence (A.I.), which uses a variety of techniques to "train" programs to become better at particular tasks. At this year's WWDC, Apple announced a new way to train machine learning models – entirely in Swift – with a new service dubbed Create ML. As a more direct method for training machine learning models, Create ML is meant to supplant TensorFlow and other machine learning models in the'creation' phase of machine learning. As Apple sees it, there are three steps to machine learning: create, train, implement.