Tag: Industries

Feed: Redis Labs. Author: Jane Paek. A little snow couldn’t stop the Redis community from coming out in force to our first Redis Day of the year in Seattle. Held on January 13–14 at the Hyatt Regency, Redis Day Seattle brought together some 300 developers, engineers, software architects, programmers, and business professionals for intensive training, thought leadership, and networking. On day one, Redis users from beginners to experts packed the conference room for a full schedule of hands-on training covering everything from Redis basics to Redis Streaming Architectures, Probabilistic Data Structures, and RediSearch. The following day, attendees heard from representatives of ... Read More

Feed: Microsoft Azure Blog. Author: John 'JG' Chirapurath. Enterprises today are transforming their businesses using Machine Learning (ML) to develop a lasting competitive advantage. From healthcare to transportation, supply chain to risk management, machine learning is becoming pervasive across industries, disrupting markets and reshaping business models. Organizations need the technology and tools required to build and deploy successful Machine Learning models and operate in an agile way. MLOps is the key to making machine learning projects successful at scale. What is MLOps ? It is the practice of collaboration between data science and IT teams designed to accelerate the entire ... Read More

Feed: Teradata Blog. The hybrid cloud is not just a consideration, but for our customers, especially our larger customers, already a reality. To understand what we mean when we say hybrid cloud, we first need to define two terms: Hybrid Cloud: Uses mix of on-premises and one or more clouds. Multi-cloud: Uses two or more clouds. Typically, companies are using both hybrid and multi-cloud architectures. When we talk to our customers about hybrid cloud and or multi-clouds, we often hear the following: We want to use the best-of-breed cloud for a workload. We want to be cloud agnostic. One cloud provider does ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Roman Chuprina. Machine Learning is a term heard around the world these days. Industries like Retail, Healthcare, and Manufacturing are taking the best out of it. So does the leaders in Banking and Finance. There is enough time and room before the technology will truly explode. However, we can still talk about some real-world use cases and ways your business can benefit. AI and ML in financial services Machine learning in finance is rapidly developing – there are already dozens of options for its use in the financial sector. So why ... Read More

Feed: AWS Government, Education, & Nonprofits Blog. Author: Teresa Carlson. The ubiquity of always-on, high performance infrastructure is fueling the rapid pace of innovation. Now more than ever, government and private companies can take advantage of affordable and scalable systems to test new ideas, evolve more quickly to meet the needs of their citizens and customers, and operate more securely. And yet, many institutions have not started on their digital journey because they lack a workforce with the advanced skills they need to drive transformational change. In recent months, I have had the opportunity to meet with a diverse collection ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Fahad Zaidi. Having spent the better part of the last decade helping organisations in the U.S. and the U.K. use data to drive profit, efficiency, and performance improvements, I thought it would be helpful to jot down best practices for organizations that are looking to get the most value out of their enterprise-level financial and operational data sets. Every organisation entering the roaring 20s knows that they must use their data as a strategic differentiator to gain a competitive advantage and this article lists five things all companies must do to ... Read More

Feed: Actian. Author: Jack Mardack. It feels like only yesterday that “Machine Learning” and “Artificial Intelligence” were equated with science fiction by most regular folks outside of tech. As a movie-going culture, we have a long history of associating these concepts with self-aware robots or rogue computer programs that could escape the laboratories where they were created and threaten all humanity. But in 2020, ML and AI have been considerably demystified. And yet, even if it seems less likely now that the Singularity will be triggered by the reckless ambition of some tech startup, consumers have new reasons to be concerned. Yes, ML and AI are going mainstream, and it’s not just disruption-minded startups that are leveraging these technologies. Enterprises in a wide range of established industries are finding solid business reasons ... Read More

Feed: Big Data Made Simple. Author: Nikhil Bansal. Where the last few decades experienced the wave of mobile-first economy, the present and the upcoming decade denounce something that we call AI-first. Ok, this is nothing but introducing the concept of your very much popular AI in different aspects for all industries. From manufacturing to automotive, healthcare and finance, AI has its wings spread all over. With this, a new experiment done by the AI experts is in the field of SEO. For years, we have known that Google ranks websites based on the relevancy of the content and the one ... Read More

Feed: Microsoft Azure Blog. Author: James Ding. Anomaly detection plays a vital role in many industries across the globe, such as fraud detection for the financial industry, health monitoring in hospitals, fault detection and operating environment monitoring in the manufacturing, oil and gas, utility, transportation, aviation, and automotive industries. Anomaly detection is about finding patterns in data that do not conform to expected behavior. It is important for decision-makers to be able to detect them and take proactive actions if needed. Using the oil and gas industry as one example, deep-water rigs with various equipment are intensively monitored by hundreds ... Read More

Feed: Planet PostgreSQL. It's every developer's nightmare: SQL queries that get large and unwieldy. This can happen fairly quickly with the addition of multiple joins, a subquery and some complicated filtering logic. I have personally seen queries grow to nearly one hundred lines long in both the financial services and health industries.Luckily Postgres provides two ways to encapsulate large queries: Views and Materialized Views. In this article, we will cover in detail how to utilize both views and materialized views within Ruby on Rails, and we can even take a look at creating and modifying them with database migrations. Our ... Read More