Everything You Need to Know About TensorFlow 2.0: A Guide
Recently, Google announced Tensorflow 2.0, an open-source framework for machine learning (ML) to help both small and big businesses out there use AI and ML.
According to a study conducted by Forbes, it would be safe to assume that AI and machine learning applications will be used more commonly across enterprises and businesses, especially in predictive analysis.
Not just this, machine learning and deep learning aided technologies are being actively integrated into companies to boost customer loyalty, increase profit opportunities, and simplify processes and distribution.
Let’s understand the concept in detail.
TensorFlow is an open source platform that enables the use of artificial intelligence and machine learning in day-to-day life.
We can use TensorFlow to access powerful machine learning models that can recognize billions of physical objects and their connections with space.
TensorFlow takes this technology to the frontend, which ensures the real-time representation of the real world can now be achieved using the device's audio-visual receptors from inside a web browser or smartphone app GUI.
TensorFlow for AI and ML
There are many machine learning solutions and libraries available out there. But TensorFlow provides an opportunity to newbie developers in AI and machine learning by providing access to an entire powerful library.
Using TensorFlow, developers don’t have to start from scratch. They can easily use the platform to create apps by combining different modules.
Developers are free to concentrate on the general logic of the program rather than the nitty-gritty of applying algorithms or working out appropriate ways to hitch the output of one element to the input of another. Behind the scenes, TensorFlow takes care of the data.
TensorFlow technology is used to link the dots in an image, converting it into a particular figure. It's used by many companies to perform sophisticated AI-based tasks. One example is the UK company Ocado, which used libraries to execute routing algorithms that made moving through warehouses with machines much more efficient. They also used it to develop better business forecasting.
This is just one example, there are a plethora of businesses out there who rely on artificial intelligence services to bring the best out of their service offerings.
TensorFlow For Predictive Analysis: Aiding Real-Time Business Applications
Predictive analysis is an important aspect of TensorFlow. TensorFlow uses computational simulation, data processing, and artificial intelligence principles to provide observations that assist in predicting future trends. That opens up a world of opportunities for businesses across a range of industries.
Agriculture and Food
Agricultural and food industries are the pillar element of any country's economic. With machine learning, artificial intelligence services, and the Internet of Things (IoT), companies are doing everything from identifying and eliminating plant diseases to making key decisions with AI prediction about where to plant for the best result.
By computing weather and agriculture databases, predictive analysis allows for the estimation of quarterly and annual crop yields. AI can even help predict potential prices of based goods by forecasting demand and supply for crops in advance.
Banking and Finance
TensorFlow can be used to observe the past behavior of stock prices and then predict future pricing. The detailed process involves analyzing previous market results, taking current news about the stock into account, and analyzing opinion correlated with it, as well as many other metrics.
It can be used to detect fraudulent activities, too. It keeps an eye on circular trading, which is the practice of buying and selling stock shares in unusual ways within a group of people who are somehow related. Furthermore, you can use the platform to detect tax evasion by observing past activities and data sets.
Pharma and Healthcare
TensorFlow is opening new opportunities in the pharma and healthcare sector.
The technology can help identify patients with identical symptoms using machine learning algorithms on medical databases such as the CLAIMS database.
For example, an ML model can be applied using classification and regression algorithms on data of patients diagnosed with AIDS or cancer to confirm whether a prospective patient is afflicted with the same diseases.
Furthermore, it offers personalized treatment to patients. Customized medication recommendations based on historical evidence and deterministic outcomes could increase treatment success rates. It's not only beneficial to patients but also to hospitals in identifying their resources.
Companies That Use TensorFlow
Plenty of companies use TensorFlow technology to get results at scale.
- Airbnb. Airbnb uses TensorFlow technology to categorize home photos across millions of rental properties on its platform.
- Airbus. Airbus, the defense and space service, uses TensorFlow to provide insights to its clients, including analyzing the variation in surfaces across global locations to aid urban planning.
- Intel. Intel is using TensorFlow to boost backpropagation efficiency. It entails a careful combination of cache blocking, data formats, and prefetching that encourage better spatial and temporal localization across the company's machine learning applications.
In the future, we look forward to Sequence-to-Sequence (Seq2Seq) models, which will be useful for designing human language-related applications of software with TensorFlow.
But, in the meantime, there's no question the technology is having an impact—and it's worth a look if you're interested in understanding, piloting, or scaling AI.