TensorFlow is a Python-friendly open-source library for numerical computation and large-scale machine learning, which has been created by the team at Google Brain. This new framework will allow researchers to push state-of-the-art machine learning and give developers the ability to easily build and deploy machine learning-powered applications.

TensorFlow and Machine Learning

Machine learning (ML) is a complex discipline, but implementing machine learning models is a lot easier than it used to be thanks to ML frameworks such as TensorFlow!

Working by bundling together deep learning models and algorithms and making them useful by cooperating with each other, one of the major benefits of TensorFlow is that it provides machine learning with abstraction

Some features of Tensorflow include: 

  • Easy model building- Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging.

  • Robust ML production anywhere- Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use.

  • Powerful experimentation for research- A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster.

Developers will no longer have to deal with the intricate details behind implementing algorithms or figuring out outputs/inputs of different functions and will be able to solely focus on the overall logic of an application instead. 

TensorFlow takes care of those details behind the scenes plus has the advantage of its backing from Google, which has fuelled the rapid pace of development for the project and has also made it easier to deploy and use.

Machine Learning and Customer Experience 

Developers can use Machine Learning to create recommended products tailored to each customer, using machine learning and data from previous orders to recommend the best products for the customer and increasing the conversion rate of the product recommendations.

Ecommerce businesses have found much success by implementing dynamic pricing. Machine learning can change and readjust prices by taking into account various factors all at once, without the merchant needing to evaluate this.

What this means for the future of development

Merchants can expect more services that offer easy to use AI-based features. More and more time-consuming processes will become automated allowing merchants to save time and focus on their sales. AI will provide a more personalised experience for customers as well, assisting them with choosing what to buy.

More and more ecommerce retailers are embracing machine learning and deriving much value from it. For businesses looking to automate tedious, labour-intensive and costly manual processes, machine learning can be a huge asset. It can empower online retailers with meaningful insights about their customers.

They can help online businesses generate more clicks, convert prospects into customers, retain them and even build strong customer relations.

How do you think you could implement AI and Machine Learning into your online strategy? 

Read more of our predictions for this year by downloading our free innovations ebook: http://bit.ly/innovations-ebook