The book includes twenty-seven chapters organized into eight parts. Part 1 presents the basics and foundations of ROS. In Part 2, four chapters deal with navigation, motion and planning. Part 3 provides four examples of service and experimental robots. Part 5 presents signal-processing tools for perception and sensing.

Leading home improvement retailer Lowe's Companies, Inc. recently concluded an exciting and engaging webinar as part of their TechSprint series, in collaboration with Analytics India Magazine. Curated by Isaac Mathew, senior director, technology (DACI) and Swaroop Shivaram, director, data science, at Lowe's, the webinar focused on the company's capabilities and latest advancements in AI/ML, besides touching upon some of the challenges, solutions, work culture, and career opportunities. Impact delivered by Lowe's using AI/ML solutions Mathew began the webinar focusing on data analytics across retail operations. With businesses trying to provide a personalised experience with an omnichannel approach, Lowe's enhances customer experience through data assessment. "AI and ML have become very integral in all decision making. We help businesses make decisions in a way they can have intelligence embedded in all their products," Mathew said.

With the recent global and regional socio-economic disruptions caused by the pandemic, industries such as retail, consumer products, manufacturing, pharmaceutical, and life sciences all struggle to align production and stocking with rapidly shifting purchasing demands. At the same time, some channels have surged ahead: online retailers, delivery services, and pharmacies are thriving. In this latest Data Science Central podcast, we discuss how injecting AI into existing business intelligence solutions can greatly enhance the ability of organizations to predict future demand for goods, even in uncertain and dynamic times.

As we inch closer to Black Friday and the start of the holiday buying extravaganza, retailers are putting the final touches on the demand forecasts they're using to predict the mix of goods they'll carry this winter. There are lot of variables to juggle, including COVID, the economy, and the weather. It seems like a perfect use case for the increasingly sophisticated machine learning models that are in vogue in the industry. But can they trust their predictions? Over the past decade, retailers and other companies in the consumer goods supply chain have started upgrading their demand forecasting systems in hopes of gaining ground in this super competitive industry. Forward-looking retailers, in particular, are replacing the largely deterministic approaches that were favored in the past–which used simple linear regression models based on historical data with relatively static assumptions about the state of the world–with probabilistic approaches that bring more data into the equation and rely on more sophisticated machine learning algorithms, like neural nets and XGBoost, to generate more detailed forecast ranges.

"Make everything as simple as possible, but not simpler." This quote often attributed to Albert Einstein surmises a conflict that business leaders are facing with the adoption of artificial intelligence (AI) and machine learning (ML). When it comes to automated forecasting and decision-making, many are now embracing the idea of these advanced predictive solutions--but only until they begin to delve beyond the norms and already-familiar data. In this respect, just like the scientific community, business leaders crave "simplicity." However, they often have different definitions of the word, focusing more on how data can affirm their own gut feelings, rather than how deeper insight and newly proved theories can override that personal experience.

I was a welder/metal worker for nearly 40 years, with only dreams of writing, having abandoned college literature and science classes for the trade to ward off starvation and frostbite.

Using artificial intelligence to generate accurate product recommendations is one of the most beneficial applications of artificial intelligence in eCommerce. An AI system can analyse data from previous purchases, page visits, wishlists, clicks, searches, and other parameters to recommend the right products at the right time. These insights assist online retailers in custom product recommendations and provide a uniform user experience across all devices and channels. Once confined to the realm of scientific research, artificial intelligence is now rapidly infiltrating the eCommerce industry as an indispensable tool for many businesses. When it comes to the eCommerce industry, artificial intelligence revolves around the concept of algorithms and learning technologies, which serve as the foundation for automation and so much more. AI enables today's online retailers to provide an exceptional customer and user experience in eCommerce while also making intelligent business decisions based on customer data.

The Predictive Retailer is a retail company that utilizes the latest technological developments to connect with its customers to deliver an exceptional personalized experience to each and every one of them. Today, technology such as AI, Machine Learning, Augmented Reality, IoT, Real-time stream processing, social media, and wearables are altering the Customer Experience (CX) landscape and retailers need to jump aboard this fast moving technology or run the risk of being left out in the cold. The Predictive Retailer reveals how these and other technologies can help shape the customer journey. The book details how the five types of analytics--descriptive, diagnostic, predictive, prescriptive, and edge analytics--affect not only the customer journey, but also just about every operating function of the retailer. An IoT connected retailer can make its operations smart.

Today, Amazon SageMaker Canvas introduces the ability to use the Quick build feature with time series forecasting use cases. This allows you to train models and generate the associated explainability scores in under 20 minutes, at which point you can generate predictions on new, unseen data. Quick build training enables faster experimentation to understand how well the model fits to the data and what columns are driving the prediction, and allows business analysts to run experiments with varied datasets so they can select the best-performing model. Canvas expands access to machine learning (ML) by providing business analysts with a visual point-and-click interface that allows you to generate accurate ML predictions on your own--without requiring any ML experience or having to write a single line of code. In this post, we showcase how to to train a time series forecasting model faster with quick build training in Canvas.

Model deployment in machine learning (ML) is becoming increasingly complex. You want to deploy not just one ML model but large groups of ML models represented as ensemble workflows. These workflows are comprised of multiple ML models. Productionizing these ML models is challenging because you need to adhere to various performance and latency requirements. Amazon SageMaker supports single-instance ensembles with Triton Inference Server.