In this paper we present an end-to-end framework for addressing the problem of dynamic pricing on E-commerce platform using methods based on deep reinforcement learning (DRL). By using four groups of different business data to represent the states of each time period, we model the dynamic pricing problem as a Markov Decision Process (MDP). Compared with the state-of-the-art DRL-based dynamic pricing algorithms, our approaches make the following three contributions. First, we extend the discrete set problem to the continuous price set. Second, instead of using revenue as the reward function directly, we define a new function named difference of revenue conversion rates (DRCR). Third, the cold-start problem of MDP is tackled by pre-training and evaluation using some carefully chosen historical sales data. Our approaches are evaluated by both offline evaluation method using real dataset of Alibaba Inc., and online field experiments on Tmall.com, a major online shopping website owned by Alibaba Inc.. In particular, experiment results suggest that DRCR is a more appropriate reward function than revenue, which is widely used by current literature. In the end, field experiments, which last for months on 1000 stock keeping units (SKUs) of products demonstrate that continuous price sets have better performance than discrete sets and show that our approaches significantly outperformed the manual pricing by operation experts.

Behind every incredible play are thousands of data points you might otherwise miss, such as player's speed, field location, and movement patterns. The NFL uses AWS to track the scale, speed, and complexity of that data. It's called Next Gen Stats (NGS) and with AWS Machine Learning and Artificial Intelligence technology, the NFL has developed ways to visualize the action on the field, uncover deeper insights, and expand the fan experience by offering a broader range of advanced stats.

A report by research firm IDC in September said global spending for AI systems will reach $97.9 billion in 2023, a staggering increase from the projected $37.5 billion that will be spent this year. That means the annual growth rate will be 28.4 percent over the next several years. That's not surprising as BigTech is primed to increase their monopoly status in the 2020s with AI leadership that will boost GDP via machine learning with the emergence of an automation economy. Over the last few years, we have seen an exponential upthrust in the number of platforms, applications, and tools based on machine learning and AI technologies. We are seeing greater mainstream impact of algorithms, and machine learning in regular jobs across a variety of industries.

Technology is changing the way every industry does business by helping to create efficiencies, save money, and provide better products and services. Retail businesses are also adopting technology to their advantage. Here are 10 of the top tech trends that will transform shopping. Virtual and augmented reality offer retailers several ways to enhance the customer experience. From browsing products to virtually "trying them on," extended reality is already in use by many retailers today.

The AWS Deep Learning AMIs are available on Ubuntu 18.04, Ubuntu 16.04, Amazon Linux 2, and Amazon Linux with TensorFlow 1.15, Tensorflow 2.0, PyTorch 1.3.1, Also new in this version is support for AWS Neuron, a SDK for running inference using AWS Inferentia chips. It consists of a compiler, run-time, and profiling tools that enable developers to run high-performance and low latency inference using Inferentia-based EC2 Inf1 instances. Neuron is pre-integrated into popular machine learning frameworks including TensorFlow, Pytorch, and MXNet to deliver optimal performance of EC2 Inf1 instances. Customers using Amazon EC2 Inf1 instances will receive the highest performance and lowest cost for machine learning inference in the cloud, and no longer need to make the sub-optimal tradeoff between optimizing for latency or throughput when running large machine learning models in production.

You can easily login to Amazon SageMaker Studio using the single sign-on enabled by AWS SSO. You can then use the Amazon SageMaker Autopilot to automatically generate models from your data, or spin-up the new SageMaker Notebooks (currently in preview) in seconds to start building your ML models and algorithms. Collaborating on notebooks with your peers is easy in SageMaker Studio. With a single click, you can share a link to a snapshot of your notebook that is captured with all its dependencies and configurations to reproduce your analysis and results. As you start experimenting with various model parameters and inputs, you can use the SageMaker Studio's visual interface for Amazon SageMaker Experiments to easily browse, track and compare your machine learning experiments, helping you keep track of incremental improvements and best models.

We are just about at the end of the year and readying ourselves to make predictions for the new decade, but before I start to do that, I thought we should look at what's been happening in procurement, supply chain, and how the IoT (Internet of Things) is making an impact on business processes. This past weekend was pretty remarkable for the retail industry. Black Friday sales topped a whopping, $7.4 billion. It's not surprising almost $3 billion of those sales were conducted digitally using a computers, tablets, and smartphones. So just how important is technology in this race to capture consumers and businesses?

As is a global holiday sales event, apparently. Global Cyber Monday sales hit $30 billion, according to Salesforce. In the U.S., sales hit $8 billion, up 11% from last year. The deals started at an average of 23% off on November 25th, growing to almost one-third off by Cyber Monday. That, of course, is if retailers still have stock left after $4.4 billion in sales on U.S. Thanksgiving Day and a record $7.2 billion in sales on Black Friday in the the U.S. alone.

The biggest cloud giant of all is Amazon.com Amazon Web Services (AWS) is also growing quite strongly for a company of its size, up 35% last quarter, and with operating margins in the mid-20s. Not only that, but Amazon has also been investing heavily in research and sales for AWS, so the unit's ultimate operating margin is probably higher than today's figures. That 35% growth also came on the heels of several significant price cuts, which shows how much companies large and small are increasingly using the public cloud. According to IDC, worldwide GDP from digitally transformed businesses, or those that have moved to the cloud, will increase from 17% to 52% of world GDP between 2018 and 2023.

Implementing AI solutions in brick-and-mortar retail is naturally more challenging than online retail, yet taking a step back, both are still surprisingly only in early stages. According to a 2018 study by Capgemini, over a quarter of the top 250 global retailers are integrating AI into their organizations (a sharp increase from 2016, when it was only a small minority of 4 percent). However, the study also found that only 1 percent of AI initiatives reach full-scale deployment. This is about to change. According to McKinsey Global Institute, investments by retail and CPG in artificial intelligence are expected to exceed $8 billion by 2024.