Marc Peter Deisenroth is DeepMind Chair in Artificial Intelligence at the Department of Computer Science, University College London. Prior to this, he was a faculty member in the Department of Computing, Imperial College London. His research areas include data-efficient learning, probabilistic modeling, and autonomous decision making. Deisenroth was Program Chair of the European Workshop on Reinforcement Learning (EWRL) 2012 and Workshops Chair of Robotics Science and Systems (RSS) 2013. His research received Best Paper Awards at the International Conference on Robotics and Automation (ICRA) 2014 and the International Conference on Control, Automation and Systems (ICCAS) 2016.

Artificial intelligence (AI) is transforming how enterprises analyze and process information. It is also shifting from theoretical to real-world technology. Companies are deploying AI technologies to boost efficiency, reduce costs, and grow sales and profitability. The technology can also reduce marketing waste by predicting what works. It is the most impactful innovation of our lifetime, and it will create new winners and losers across entire industries.

Amazon SageMaker Neo was launched at AWS re:Invent 2018. It made notable performance improvement on models with statically known input and output data shapes, typically image classification models. These models are usually composed of a stack of blocks that contain compute-intensive operators, such as convolution and matrix multiplication. Neo applies a series of optimizations to boost the model's performance and reduce memory usage. The static feature significantly simplifies the compilation, and you can decide on runtime inference tasks such as memory sizes ahead of time using a dedicated analysis pass.

Core ML is a machine learning (ML) model format created and supported by Apple that compiles, deploys, and runs on Apple devices. Developers who train their models in popular frameworks such as TensorFlow and PyTorch convert models to Core ML format to deploy them on Apple devices. Neo is an ML model compilation service on AWS that enables you to automatically convert models trained in TensorFlow, PyTorch, MXNet, and other popular frameworks, and optimize them for the target of your choice. With the new automated model conversion to Core ML, Neo now makes it easier to build apps on Apple's platform to convert models from popular libraries like TensorFlow and PyTorch to Core ML format. In this post, we show how to set up automatic model conversion, add a model to your app, and deploy and test your new model.

Various machine learning (ML) optimizations are possible at every stage of the flow during or after training. Model compiling is one optimization that creates a more efficient implementation of a trained model. In 2018, we launched Amazon SageMaker Neo to compile machine learning models for many frameworks and many platforms. We created the ML compiler service so that you don't need to set up compiler software, such as TVM, XLA, Glow, TensorRT, or OpenVINO, or be concerned with tuning the compiler for best model performance. Since then, we have updated Neo to support more operators and expand model coverage for TensorFlow, PyTorch, and Apache MXNet (incubating).

Today, I'm extremely happy to announce the availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that accelerates your machine learning workflows with one-click access to popular model collections (also known as "model zoos"), and to end-to-end solutions that solve common use cases. In recent years, machine learning (ML) has proven to be a valuable technique in improving and automating business processes. Indeed, models trained on historical data can accurately predict outcomes across a wide range of industry segments: financial services, retail, manufacturing, telecom, life sciences, and so on. Yet, working with these models requires skills and experience that only a subset of scientists and developers have: preparing a dataset, selecting an algorithm, training a model, optimizing its accuracy, deploying it in production, and monitoring its performance over time. In order to simplify the model building process, the ML community has created model zoos, that is to say, collections of models built with popular open source libraries, and often pretrained on reference datasets.

Lucidworks, a leading provider of machine learning-based ecommerce search and personalization technology, announced Cyber Five results from its collection of enterprise retail and direct-to-consumer brands. Over the five days spanning Thanksgiving through Cyber Monday, many Lucidworks Fusion customers saw high double-digit to low triple-digit growth in ecommerce revenue tied to performance in search and browse as well as landing page sorts, typeahead, and personalized recommendations. "We're proud to be a part of our customer's holiday shopping season success," said Peter Curran, General Manager for Digital Commerce, Lucidworks. "This time of year is mission critical for enterprise retail and direct-to-consumer brands and we're elated to see this year's traffic and revenue over Cyber Five double from last year." Another insight from this year's Cyber Five was the role that Lucidworks Fusion vector search capabilities played in search processing.

Today, I'm extremely happy to announce that Amazon SageMaker Debugger can now profile machine learning models, making it much easier to identify and fix training issues caused by hardware resource usage. Despite its impressive performance on a wide range of business problems, machine learning (ML) remains a bit of a mysterious topic. Getting things right is an alchemy of science, craftsmanship (some would say wizardry), and sometimes luck. In particular, model training is a complex process whose outcome depends on the quality of your dataset, your algorithm, its parameters, and the infrastructure you're training on. As ML models become ever larger and more complex (I'm looking at you, deep learning), one growing issue is the amount of infrastructure required to train them.

Walmart has transformed an ordinary grocery store into a 50,000-square-foot AI lab that tests new retail technologies in a real-world setting. The Intelligent Retail Lab is located in Levittown, New York, and is equipped with AI-powered sensors that keep track of the inventory and the freshness of the produce.

Retail is a powerful data-driven industry. Retailers use traditional analysis over the years. However, the advent of artificial intelligence (AI) and machine learning (ML) has opened many new opportunities to gain a deeper understanding of data processing. The artificial intelligence retail market is expected to grow at a CAGR of 35.9% from 2019 to 2025 to reach $ 15.3 billion by 2025. The growth of artificial intelligence services on the retail market is driven by several factors such as the growing number of Internet users, the increasing adoption of smart devices, the rapid adoption of technological advances across the retail chain, and the increasing adoption of multi-channel or Omni retail strategy.