Tag Archives: Framework

Harness AI with the Top Machine Learning Frameworks of 2021

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According to Gartner, machine learning and AI will create $2.29 trillion of business value by 2021. Artificial intelligence is the way of the future, but many businesses do not have the resources to create and employ AI from scratch. Luckily, machine learning frameworks make the implementation of AI more accessible, enabling businesses to take their enterprises to the next level.

What Are Machine Learning Frameworks?

Machine learning frameworks are open source interfaces, libraries, and tools that exist to lay the foundation for using AI. They ease the process of acquiring data, training models, serving predictions, and refining future results. Machine learning frameworks enable enterprises to build machine learning models without requiring an in-depth understanding of the underlying algorithms. They enable businesses that lack the resources to build AI from scratch to wield it to enhance their operations.

For example, AirBNB uses TensorFlow, the most popular machine learning framework, to classify images and detect objects at scale, enhancing guests ability to see their destination. Twitter uses it to create algorithms which rank tweets.

Here is a rundown of today’s top ML Frameworks:

TensorFlow

TensorFlow

TensorFlow is an end-to-end open source platform for machine learning built by the Google Brain team. TensorFlow offers a comprehensive, flexible ecosystem of tools, libraries, and community resources, all built toward equipping researchers and developers with the tools necessary to build and deploy ML powered applications.

TensorFlow employs Python to provide a front-end API while executing applications in C++. Developers can create dataflow graphs which describe how data moves through a graph, or a series of processing nodes. Each node in the graph is a mathematical operation; the connection between nodes is a multidimensional data array, or tensor.

While TensorFlow is the ML Framework of choice in the industry, increasingly researchers are leaving the platform to develop for PyTorch.

PyTorch

PyTorch

PyTorch is a library for Python programs that facilitates deep learning. Like TensorFlow, PyTorch is Python-based. Think of it as Facebook’s answer to Google’s TensorFlow—it was developed primarily by Facebook’s AI Research lab. It’s flexible, lightweight, and built for high-end efficiency.

PyTorch features outstanding community documentation and quick, easy editing capabilities. PyTorch facilitates deep learning projects with an emphasis on flexibility.

Studies show that it’s gaining traction, particularly in the ML research space due to its simplicity, comparable speed, and superior API. PyTorch integrates easily with the rest of the Python ecosystem, whereas in TensorFlow, debugging the model is much trickier.

Microsoft Cognitive Toolkit (CNTK)

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Microsoft’s ML framework is designed to handle deep learning, but can also be used to process large amounts of unstructured data for machine learning models. It’s particularly useful for recurrent neural networks. For developers inching toward deep learning, CNTK functions as a solid bridge.

CNTK is customizable and supports multi-machine back ends, but ultimately it’s a deep learning framework that’s backwards compatible with machine learning. It is neither as easy to learn nor deploy as TensorFlow and PyTorch, but may be the right choice for more ambitious businesses looking to leverage deep learning.

IBM Watson

IBM-Watson

IBM Watson began as a follow-up project to IBM DeepBlue, an AI program that defeated world chess champion Garry Kasparov. It is a machine learning system trained primarily by data rather than rules. IBM Watson’s structure can be compared to a system of organs. It consists of many small, functional parts that specialize in solving specific sub-problems.

The natural language processing engine analyzes input by parsing it into words, isolating the subject, and determining an interpretation. From there it sifts through a myriad of structured and unstructured data for potential answers. It analyzes them to elevate strong options and eliminate weaker ones, then computes a confidence score for each answer based on the supporting evidence. Research shows it’s correct 71% of the time.

IBM Watson is one of the more powerful ML systems on the market and finds usage in large enterprises, whereas TensorFlow and PyTorch are more frequently used by small and medium-sized businesses.

What’s Right for Your Business?

Businesses looking to capitalize on artificial intelligence do not have to start from scratch. Each of the above ML Frameworks offer their own pros and cons, but all of them have the capacity to enhance workflow and inform beneficial business decisions. Selecting the right ML framework enables businesses to put their time into what’s most important: innovation.

Making the Most of Your Tablet Design Part 2: Custom Device Design

In Part One of our two part series on tablets, we explored the top tablets on the market in 2014 and what they offer to both consumers and developers. This article, explores the perks & advantages of custom device application design and optimization.

Companies design applications to connect with their consumers–to attract eyes to their company or product. As developers, we don’t focus on what device the consumer uses, the goal is to hold the user’s attention and potentially engrain our client’s brand in the consumer’s day. When developing an application, one must remember that each device has its advantages and disadvantages, and one cross-device layout doesn’t always get the job done. By optimizing a mobile application separately for phones and tablets, it portrays your company in the best light and engages the end user with the best possible user experience.

As discussed in the previous article, tablets vary in numerous factors, most notably operating system, processing power and screen size. When optimizing a mobile application, there are a few options. Developing an application optimized for smartphones creates an app which can be used to its fullest potential on any smartphone, but the app’s functionality could suffer on a tablet. Developing a tablet-only application optimizes the app for tablets, but again, it will not work nearly as well on a smartphone.

The third and best option is to optimize the app separately for both phones and tablets. Although creating a phone or tablet-only optimized application is cheaper, when the design is optimized for each screen size and device type, your app always looks great, sacrificing neither functionality nor usability on any given platform and ultimately providing the best possible user experience. At Mystic Media, we recommend investing in both phone and tablet versions of your application to maximize the quality of the app, and vicariously the perception of your company.

Phone only applications can be fixed to have multiple viewing options. We all have seen and experienced the 1x & 2x buttons on iPads, which allow you to adjust the size of the application based on what device you are using. While this seems a reasonable solution in theory, in practice, it appears shoddy and cheap. When one application attempts to optimize only for a phone and utilizes the same general framework for tablets, it often ends up mediocre on the tablet. For big companies, it’s not up for debate–they recognize the importance of appearing on the cutting edge so they invest in multiple device applications

Take a look at the Youtube mobile app. Their iPhone & Android apps limit the app to display vertical orientation on phones, but on tablets they optimize the design to display both vertical and horizontal orientations based on the angle at which one holds the device. By optimizing the design of the app to change based upon the screen size and device orientation, Youtube allows for a customized feel and content placement on all devices, ensuring the end user will spend more time on their app increasing the quality of the user experience.

Device optimization is worth the time and money because it allows the mobile application to live up to its fullest potential functionally and is aesthetically pleasing on every screen. In addition, marketing your app in both the phone and tablet categories within the app store gives your app a major boost in visibility.

When developing an application, the number one goal is to avoid looking amateur. If the application looks amateur, it turns off the user, consequently causing less downloads, uses, and of course,money. In the spirit of app store optimization and attracting downloads, it is critical to maximize the exposure to your mobile application. Having a bad application is worse than having no application—it can degrade the business in the eyes of your customers and potential users. Rather than squander your time and money on a cheap app, satisfy and impress your customers by developing a multiple device optimized application.

At Mystic Media, our team is equipped with all the tools to develop your app, optimize it to devices, and even develop market strategies. We have the knowledge, the work force, and the work ethic to design your mobile app to its fullest potential. Contact us today by clicking here or give us a call at 801.994.6815