Next location prediction is a discipline that involves predicting a users next location. Its applications include resource allocation, quality of service, energy efficiency, and traffic management. This paper proposes an energy-efficient, small, and low parameter machine learning (ML) architecture for accurate next location prediction, deployable on modest base stations and edge devices. To accomplish this we ran a hundred hyperparameter experiments on the full human mobility patterns of an entire city, to determine an exact ML architecture that reached a plateau of accuracy with the least amount of model parameters. We successfully achieved a reduction in the number of model parameters within published ML architectures from 202 million down to 2 million. This reduced the total size of the model parameters from 791 MB down to 8 MB. Additionally, this decreased the training time by a factor of four, the amount of graphics processing unit (GPU) memory needed for training by a factor of twenty, and the overall accuracy was increased from 80.16% to 82.54%. This improvement allows for modest base stations and edge devices which do not have a large amount of memory or storage, to deploy and utilize the proposed ML architecture for next location prediction.

Machine learning on images is revolutionizing healthcare, manufacturing, retail, and many other sectors. Many previously difficult problems can now be solved by training machine learning (ML) models to identify objects in images. Our aim in this book is to provide intuitive explanations of the ML architectures that underpin this fast-advancing field, and to provide practical code to employ these ML models to solve problems involving classification, measurement, detection, segmentation, representation, generation, counting, and more. Image classification is the "hello world" of deep learning. Therefore, this book also provides a practical end-to-end introduction to deep learning. It can serve as a stepping stone to other deep learning domains, such as natural language processing.

Edge AI offers lots of improvement over conventional ML architectures. First of all the latency involved with any network transfer is removed, which can be critical in some use cases. The battery drain involved with streaming data is no longer an issue, allowing for better battery life, and associated costs for data communication are significantly reduced. This is highly beneficial for a number of use cases. Sensors in remote locations like offshore wind farms can come pre-loaded with the algorithms that enable them to make decisions without the complex infrastructure of getting them internet-connected.

ITU is conducting a global ITU AI/ML 5G Challenge on the theme "How to apply ITU's ML architecture in 5G networks". If you don't know the difference between AI & ML, this picture from the old blog post may help. The ITU website says: Artificial Intelligence (AI) will be the dominant technology of the future and will impact every corner of society. In particular, AI / ML (machine learning) will shape how communication networks, a lifeline of our society, will be run. Many companies in the ICT sector are exploring how to make best use of AI/ML.

The last blog in the machine learning series delved into the machine learning capabilities of Microsoft Azure. In this blog, let's look at the intent behind Google's AI platform. Google has brought about a true paradigm shift in the way ML models are deployed by completely abstracting the complexity of the underlying algorithmic mechanics. In my opinion, this will work in 80% of the cases with minor tweaks, if any. This is a blessing for organizations struggling with productionizing ML models for their businesses in a meaningful and rapid manner.