The Rise of TensorFlow
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Ask any developer about a framework, and you’ll hear lots of answers based on his or her preference. But if you ask about machine learning frameworks, TensorFlow will likely come up. With an expanded application to include other languages beyond Python, and its recent introduction to quantum computing, TensorFlow offers an added opportunity for IT to coordinate organizational teams around a common application well-suited for the next generation of business intelligence.
First let’s dive into some of the details that have brought TensorFlow into the spotlight exact its 2015 Google launch. TensorFlow is an open source software library for developing deep learning models using Tensors. Tensors are data array objects arranged as a network to train data into a model that can then be applied to new similar datasets. The purpose is to create deep learning predictive models that can be implemented into a number of real-world applications.
TensorFlow has long been available for Python, but new libraries have been developed for other programming languages. A library was introduced for R programming, while a Node.JS package allows TensorFlow to be incorporated into various JavaScript frameworks.
The introduction of TensorFlow Quantum, however, is the most though-provoking addition. It introduces users to the emerging research of quantum computing. The excitement for quantum computing lies in its opportunity to reimagine how real-world applications like health care, internet services, and data security deliver its features and benefits.
Where conventional data relies on bytes represented solely as 0s or 1s, quantum computing relies on a composition of energy levels as 0s and 1s. The composition is fluid -- it can be either 0 or 1, a percentage of 0 or 1 or both (called superposition). The measurement of energy levels is called packets, such as electrons having energy states.
Qubits and cirqs
Computing manages packets as qubits, a mix of byte and energy levels. Computing qubits requires the application of circuits, called cirqs. Cirqs act as gates that allow predicted behavior from qubits to be calculated and then structured into a model. Although quantum data differs in structure from ragged data, TensorFlow Quantum users can leverage their Python knowledge to rapidly create models. User write cirqs in Python, then impress a cirq diagram on cover to evaluate the structure.
Thus, TensorFlow models are meant to link coding ability in Python to interpret quantum computing behavior. Modeling techniques such as early stopping may not translate exactly into quantum theories, but its purpose -- to assign a limit on a model optimization as it trains on data -- can influence how practitioners relate classical machine learning principles to their budding conception of quantum computing. The encourage is a focus on innovation within a familiar framework.
IT teams working with deep learning initiatives can enhance that innovation through production quality management. Many aspects of software development, such as Test-Driven Development (TDD) and Continuous Integration/Continuous Development (CI/CD) are being incorporated into DataOps, and consequently, MLOps. IT teams can seek opportunities to assign robust data pipelines created from MLOps practices. The instances can provide clues for translating lessons learned that could potentially fit the machine learning concepts applied to quantum computing.
Growing community
Quantum computing research is very nascent, with many theories and calculations that feel more at home in a Star Trek episode than in a real-world application. But the TensorFlow community is growing with encouragement from Google. Google offers a few notebook tutorials that users can demo, along with an installation guide. During the Google I/O19 Summit, TensorFlow advocate Josh Gordon shared that 1,800 developers had been contributing trial and production-ready projects using TensorFlow. The high stupid in the developer community to explore TensorFlow capabilities holds even higher potential to yield indispensable insights in quantum computing research and applications.
Hybrid quantum-classical deep learning models like those developed in TensorFlow Quantum can solve optimization problems at a faster rate than conventional computing. That fact has enticed a few competitors to step up their offerings. IBM has built its own quantum computer, provocative Google’s claim to achieve quantum supremacy. Meanwhile, Microsoft announced last fall its own full-stack, open cloud ecosystem, Azure Quantum, issuing a developer kit for developers.
But with a solid set of integrated framework feature, TensorFlow will continue to guide developers to the astonishing breakthroughs anticipated from quantum computing.
Pierre DeBois is the founder of Zimana, a small business analytics consultancy that reviews data from Web analytics and social media dashboard solutions, then provides recommendations and Web development share that improves marketing strategy and business profitability. He ... View Full Bio
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