I have been at Lucidworks for about eight years. My background is in software engineering with an emphasis on analytics and distributed computing. I was brought in to Lucidworks by the board to lead the company through a transition from an open source support and services company to delivering proprietary search and AI solutions. We have a team of around 250 employees that are distributed across the country and the world. Back in 2005, pre-Lucidworks, I was a part of the founding team at Splunk.

I had a chance to review the manuscript. It is a very good book. For the supply chain managers out there, you should read at least the first few chapters, and then have others on your team read the rest of it and act on it ... you can have close to state-of-the-art forecasts with a minimum of effort.... This book closes the coffin on vendors who are selling only a handful of forecasting models. The objective of Data Science for Supply Chain Forecasting is to show practitioners how to apply the statistical and ML models described in the book in simple and actionable "do-it-yourself" ways by showing, first, how powerful the ML methods are, and second, how to implement them with minimal outside help, beyond the "do-it-yourself" descriptions provided in the book. In an age where analytics and machine learning are taking on larger roles in business forecasting, Nicolas' book is perfect for professionals who want to understand how they can use technology to predict the future more reliably.

In Part 1 of this series of posts, we offered step-by-step guidance for using Amazon SageMaker, SageMaker projects and Amazon SageMaker Pipelines, and AWS services such as Amazon Virtual Private Cloud (Amazon VPC), AWS CloudFormation, AWS Key Management Service (AWS KMS), and AWS Identity and Access Management (IAM) to implement secure architectures for multi-account enterprise machine learning (ML) environments. In this second and final part, we provide instructions for deploying the solution from the source code GitHub repository to your account or accounts and experimenting with the delivered SageMaker notebooks. The provided CloudFormation templates provision all the necessary infrastructure and security controls in your account. An Amazon SageMaker Studio domain is also created by the CloudFormation deployment process. The following diagram shows the resources and components that are created in your account.

Millennials and Gen Z buyers – the dominant market segment – are increasingly voicing a preference for sustainably produced, longer-lasting clothing. Second-hand and rental fashion retail is also gaining popularity as buyers recognize the negative impact of over-shopping and then discarding. In 2019 alone, 52% of millennials in Britain bought secondhand clothes – a significant number. And brands are responding to this shift by pivoting their practices with the help of data insights from AI solutions.

Launched at AWS re:Invent 2020, Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With Pipelines, you can create, automate, and manage end-to-end ML workflows at scale. You can integrate Pipelines with existing CI/CD tooling. This includes integration with existing source control systems such as GitHub, GitHub Enterprise, and Bitbucket. This new capability also allows you to utilize existing installations of Jenkins for orchestrating your ML pipelines.

Computer monitors that support HDMI 2.1, the latest HDMI standard, are beginning to trickle into online retailers. They sell at extremely high prices (when they're available at all). Even the most affordable HDMI 2.1 monitors, like the Gigabyte Aorus FI32U and Acer Nitro XV282K KV, are priced near $1,000. The high price of HDMI 2.1 implies it's important, but the truth is more nuanced. HDMI 2.1 brings new features to the table, but they're relevant only to people with specific needs.

Reducing the number of failures in a production system is one of the most challenging problems in technology driven industries, such as, the online retail industry. To address this challenge, change management has emerged as a promising sub-field in operations that manages and reviews the changes to be deployed in production in a systematic manner. However, it is practically impossible to manually review a large number of changes on a daily basis and assess the risk associated with them. This warrants the development of an automated system to assess the risk associated with a large number of changes. There are a few commercial solutions available to address this problem but those solutions lack the ability to incorporate domain knowledge and continuous feedback from domain experts into the risk assessment process. As part of this work, we aim to bridge the gap between model-driven risk assessment of change requests and the assessment of domain experts by building a continuous feedback loop into the risk assessment process. Here we present our work to build an end-to-end machine learning system along with the discussion of some of practical challenges we faced related to extreme skewness in class distribution, concept drift, estimation of the uncertainty associated with the model's prediction and the overall scalability of the system.

As customers continue to come up with new use-cases for machine learning, data gravity is as important as ever. Where latency and network connectivity is not an issue, generating data in one location (such as a manufacturing facility) and sending it to the cloud for inference is acceptable for some use-cases. With other critical use-cases, such as fraud detection for financial transactions, product quality in manufacturing, or analyzing video surveillance in real-time, customers are faced with the challenges that come with having to move that data to the cloud first. One of the challenges customers are facing with performing inference in the cloud is the lack of real-time inference and/or security requirements preventing user data to be sent or stored in the cloud. Tens of thousands of customers use Amazon SageMaker to accelerate their Machine Learning (ML) journey by helping data scientists and developers to prepare, build, train, and deploy machine learning models quickly.

Reviewers played an important role in helping people make purchases in the past, and still play an important role in today's world. There is, however, a growing skeptical element in the population. In the wake of last year's controversies over fake content, customers have changed the way they look at the information they find online, even if it looks like it's. It is inconceivable that the media have ever been as aggressive in their pursuit of truth as they are. A large volume of user generated content can now be analyzed by artificial intelligence. An analytical algorithm was used to analyze 25,000 reviews of hotels that were found across the web and analyzed with machine learning.

We all could use a little help keeping our homes clean and a robot vacuum can do just that. Some robots, like iRobot's Roomba i7 and S9, go one step further by automatically emptying their bins into their clean bases after each job -- so you rarely have to take out its trash. These gadgets come with high price tags, but you can grab either of them for less right now at Wellbots. The online retailer has the Roomba i7 for $699, or $100 off, and the S9 for $949, or $150 off, when you use the codes 100ENGADGET and 150ENGADGET, respectively, at checkout. While not all-time-low prices, they're the best prices we've seen since April.