Accelerated Architecture for Manufacturing Intelligence (AMI)

| June 10, 2018

article image
Accelerated Architecture for Manufacturing Intelligence (AMI): Cyber Physical IIoT, Data Management, and Analytics Platform. Business Services. Application Development. Reusable Digital Catalog of assets -Datasets -Visual Analytics -Algorithms & Models -Applications & Custom Tools -Codes & Standards. Cloud Solution / On Premise Deployment. API for private cloud. Deployment on premise option.

Spotlight

Hadoop-Bootcamp

Hadoop Boot Camp offers four different levels of training packages for Big Data related technologies. These package offers you practical foundation level training that enables you to participate in one of the fastest growth area in technology. All the packages offer introduction to big data, strong foundation in distributed file system (DFS/HDFS), Map Reduce programming framework which are building blocks of any big data system. We also offer advanced techniques and patterns in Map Reduce framework. We also cover introduction to Hive and Pig, which are two popular higher level abstraction in Hadoop platform that facilitates the development of ad-hoc queries and data flows respectively without the complexities of map reduce programs directly.

OTHER ARTICLES

A Tale of Two Data-Centric Services

Article | April 13, 2020

The acronym DMaaS can refer to two related but separate things: data center management-as-a-service referred to here by its other acronym, DCMaaS and data management-as-a-service. The former looks at infrastructure-level questions such as optimization of data flows in a cloud service, the latter refers to master data management and data preparation as applied to federated cloud services.DCMaaS has been under development for some years; DMaaS is slightly younger and is a product of the growing interest in machine learning and big data analytics, along with increasing concern over privacy, security, and compliance in a cloud environment.DMaaS responds to a developing concern over data quality in machine learning due to the large amount of data that must be used for training and the inherent dangers posed by divergence in data structure from multiple sources. To use the rapidly growing array of cloud data, including public cloud information and corporate internal information from hybrid clouds, you must aggregate data in a normalized way so it can be available for model training and processing with ML algorithms. As data volumes and data diversity increase, this becomes increasingly difficult.

Read More

Understanding Big Data and Artificial Intelligence

Article | April 13, 2020

Data is an important asset. Data leads to innovation and organizations tend to compete for leading these innovations on a global scale. Today, every business requires data and insights to stay relevant in the market. Big Data has a huge impact on the way organizations conduct their businesses. Big Data is used in different enterprises like travel, healthcare, manufacturing, governments, and more. If they need to determine their audience, understand what clients want, forecast the needs of the customers and the clients, AI and big data analysis is vital to every decision-making scenario. When companies process the collected data accurately, they get the desired results, which leads them to their desired goals. The term Big Data has been around since the 1990s. By the time we could fully comprehend it, Big Data had already amassed a huge amount of stored data. If this data is analyzed properly, it would reveal valuable industry insights into the industry to which the data belonged. IT professionals and computer scientists realized that going through all of the data and analyzing it for the purpose was too big of a task for humans to undertake. When artificial intelligence (AI) algorithm came into the picture, it accomplished analyzing the accumulated data and deriving insights. The use of AI in Big Data is fundamental to get desired results for organizations. According to Northeastern University, the amount of data in the world was 4.4 zettabytes in 2013. By of 2020, the data rose to 44 zettabytes. When there is this amount of data produced globally, this information is invaluable to the enterprises and now can leverage AI algorithms to process it. Because of this, the companies can understand and influence customer behavior. By 2018, over 50% of countries had adopted Big Data. Let us understand what Big Data, convergence of big data and AI, and impact of AI on big data analytics. Understanding Big Data In simple words, Big Data is a term that comprises every tool and process that helps people use and manage vast sets of data. According to Gartner, Big Data is a “high-volume and/or high-variety information assets that demand cost-effective, innovative forms of information processing to enable enhanced insight, decision-making, and process automation.” The concept of Big Data was created to capture trends, preferences, and user behavior in one place called the data lake. Big Data in enterprises can help them analyze and configure their customers’ motivations and come up with new ideas for the creation of new offerings. Big Data studies different methods of extracting, analyzing, or dealing with data sets that are too complicated for traditional data processing systems. To analyze a large amount of data requires a system designed to stretch its extraction and analysis capability. Data is everywhere. This stockpile of data can give us insights and business analytics to the industry belonging to the data set. Therefore, the AI algorithms are written to benefit from large and complex data. Importance of Big Data Data is an integral part of understanding customer demographics and their motivations. When customers interact with technology in active or passive manner, these actions create a new set of data. What contributes to this data creation is what they carry with them every day - their smartphones. Their cameras, credit cards, purchased products all contribute to their growing data profile. A correctly done analysis can tell a lot about their behavior patterns, personality, and events in the customer’s life. Companies can use this information to rethink their strategies, improve on their product, and create targeted marketing campaigns, which would ultimately lead them to their target customer. Industry experts, for years and years, have discussed Big Data and its impact on businesses. Only in recent years, however, has it become possible to calculate that impact. Algorithms and software can now analyze large datasets quickly and efficiently.The forty-four zettabyte of data will only quadruple in the coming years. This collection and analysis of the data will help companies get the AI insights that will aid them in generating profits and be future-ready. Organizations have been using Big Data for a long time. Here’s how those organizations are using Big Data to drive success: Answering customer questions Using big data and analytics, companies can learn the following things: • What do customers want? • Where are they missing out on? • Who are their best and loyal customers? • Why people choose different products? Every day, as organizations gather more information, they can get more insights into sales and marketing. Once they get this data, they can optimize their campaigns to suit the customer’s needs. Learning from their online habits and with correct analysis, companies can send personalized promotional emails. These emails may prompt this target audience to convert into full-time customers. Making confident decisions As companies grow, they all need to make complex decisions. With in-depth analysis of marketplace knowledge, industry, and customers, Big Data can help you make confident choices. Big Data gives you a complete overview of everything you need to know. With the help of this, you can launch your marketing campaign or launch a new product in the market, or make a focused decision to generate the highest ROI. Once you add machine learning and AI to the mix, your Big Data collections can form a neural network to help your AI suggest useful company changes. Optimizing and Understanding Business Processes Cloud computing and machine learning help you to stay ahead by identifying opportunities in your company’s practices. Big Data analytics can tell you if your email strategy is working even when your social media marketing isn’t gaining you any following. You can also check which parts of your company culture have the right impact and result in the desired turnover. The existing evidence can help you make quick decisions and ensure you spend more of your budget on things that help your business grow. Convergence of Big Data and AI Big Data and Artificial Intelligence have a synergistic relationship. Data powers AI. The constantly evolving data sets or Big Data makes it possible for machine learning applications to learn and acquire new skills. This is what they were built to do. Big Data’s role in AI is supplying algorithms with all the essential information for developing and improving features, pattern recognition capabilities. AI and machine learning use data that has been cleansed of duplicate and unnecessary data. This clean and high-quality big data is then utilized to create and train intelligent AI algorithms, neural networks, and predictive models. AI applications rarely stop working and learning. Once the “initial training” is done (initial training is preparing already collected data), they adjust their work as and when the data changes. This makes it necessary for data to be constantly collected. When it comes to businesses using this technology, AI helps them use Big Data for analytics by making advanced tools accessible and obtainable to help users gain insights that would otherwise have been hidden in the huge amount of data. Once firms and businesses gain a hold on using AI and Big Data, they can provide decision-makers with a clear understanding of factors that affect their businesses. Impact of AI on Big Data Analytics AI supports users in the Big Data cycle, including aggregation, storage, and retrieval of diverse data types from different data sources. This includes data management, context management, decision management, action management, and risk management. Big Data can help alert problems and help find new solutions and get ideas about any new prospects. With the amount of information stream that comes in, it can be difficult to determine what is important and what isn’t. This is where AI and machine learning come in. It can help identify unusual patterns in the processes, help in the analysis, and suggest further steps to be taken. It can also learn how users interact with analytics and learn subtle differences in meanings or context-specific nuances to understand numeric data sources. AI can also caution users about anomalies, unforeseen data patterns, monitoring events, and threats from system logs or social networking data. Application of Big Data and Artificial Intelligence After establishing how AI and Big Data work together, let us look at how some applications are benefitting from their synergy: Banking and financial sectors The banking and financial sectors apply these to monitor financial marketing activities. These institutions also use AI to keep an eye on any illegal trading activities. Trading data analytics are obtained for high-frequency trading, and decision making based on trading, risk analysis, and predictive analysis. It is also used for fraud warning and detection, archival and analysis of audit trails, reporting enterprise credit, customer data transformation, etc. Healthcare AI has simplified health data prescriptions and health analysis, thus benefitting healthcare providers from the large data pool. Hospitals are using millions of collected data that allow doctors to use evidence-based medicine. Chronic diseases can be tracked faster by AI. Manufacturing and supply chain AI and Big Data in manufacturing, production management, supply chain management and analysis, and customer satisfaction techniques are flawless. The quality of products is thus much better with higher energy efficiency, reliable increase in levels, and profit increase. Governments Governments worldwide use AI applications like facial recognition, vehicle recognition for traffic management, population demographics, financial classifications, energy explorations, environmental conservation, criminal investigations, and more. Other sectors that use AI are mainly retail, entertainment, education, and more. Conclusion According to Gartner’s predictions, artificial intelligence will replace one in five workers by 2022. Firms and businesses can no longer afford to avoid using artificial intelligence and Big Data in their day-to-day. Investments in AI and Big Data analysis will be beneficial for everyone. Data sets will increase in the future, and with it, its application and investment will grow over time. Human relevance will continue to decrease as time goes by. AI enables machine learning to be the future of the development of business technologies. It will automate data analysis and find new insights that were previously impossible to imagine by processing data manually. With machine learning, AI, and Big Data, we can redraw the way we approach everything else. Frequently Asked Questions Why does big data affect artificial intelligence? Big Data and AI customize business processes and make better-suited decisions for individual needs and expectations. This improves its efficiency of processes and decisions. Data has the potential to give insights into a variety of predicted behaviors and incidents. Is AI or big data better? AI becomes better as it is fed more and more information. This information is gathered from Big Data which helps companies understand their customers better. On the other hand, Big Data is useless if there is no AI to analyze it. Humans are not capable of analyzing the data on a large scale. Is AI used in big data? When the gathered Big Data is to be analyzed, AI steps in to do the job. Big Data makes use of AI. What is the future of AI in big data? AI’s ability to work so well with data analytics is the primary reason why AI and Big Data now seem inseparable. AI machine learning and deep learning are learning from every data input and using those inputs to generate new rules for future business analytics. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "Why does big data affect artificial intelligence?", "acceptedAnswer": { "@type": "Answer", "text": "Big Data and AI customize business processes and make better-suited decisions for individual needs and expectations. This improves its efficiency of processes and decisions. Data has the potential to give insights into a variety of predicted behaviors and incidents." } },{ "@type": "Question", "name": "Is AI or big data better?", "acceptedAnswer": { "@type": "Answer", "text": "AI becomes better as it is fed more and more information. This information is gathered from Big Data which helps companies understand their customers better. On the other hand, Big Data is useless if there is no AI to analyze it. Humans are not capable of analyzing the data on a large scale." } },{ "@type": "Question", "name": "Is AI used in big data?", "acceptedAnswer": { "@type": "Answer", "text": "When the gathered Big Data is to be analyzed, AI steps in to do the job. Big Data makes use of AI." } },{ "@type": "Question", "name": "What is the future of AI in big data?", "acceptedAnswer": { "@type": "Answer", "text": "AI’s ability to work so well with data analytics is the primary reason why AI and Big Data now seem inseparable. AI machine learning and deep learning are learning from every data input and using those inputs to generate new rules for future business analytics." } }] }

Read More

Machine Learning vs. Deep Learning. Which Does Your Business Need?

Article | April 13, 2020

In recent years, artificial intelligence research and applications have accelerated at a rapid speed. Simply saying your organization will incorporate AI isn’t as specific as it once was. There are diverse implementation options for AI, Machine Learning, and Deep Learning, and within each of them, a series of different algorithms you can leverage to improve operations and establish a competitive edge. Algorithms are utilized across almost every industry. For example, to power the recommendation engines in all media platforms, the chatbots that support customer service efforts at scale, and the self-driving vehicles being tested by the world’s largest automotive and technology companies. Because of how diverse AI has become and the many ways in which it works with data, companies must carefully evaluate what will work best for them.

Read More

NEW TECHNOLOGY CAN IMPROVE STORAGE CONGESTION OF AI’S MEMORY

Article | April 13, 2020

The upsurge in data generation and its computing has raised the need for more power, storage and speed. What we call as big data is extremely memory-hungry and power-sapping and to fetch this requirement, engineers have put forward an innovative method. Recently, electrical engineers at Northwestern University and the University of Messina in Italy have developed a new magnetic memory device that could potentially support the surge of data-centric computing, which requires ever-increasing power, storage, and speed. Based on antiferromagnetic (AFM) materials, the device is the smallest of its kind ever demonstrated and operates with record-low electrical current to write data.

Read More

Spotlight

Hadoop-Bootcamp

Hadoop Boot Camp offers four different levels of training packages for Big Data related technologies. These package offers you practical foundation level training that enables you to participate in one of the fastest growth area in technology. All the packages offer introduction to big data, strong foundation in distributed file system (DFS/HDFS), Map Reduce programming framework which are building blocks of any big data system. We also offer advanced techniques and patterns in Map Reduce framework. We also cover introduction to Hive and Pig, which are two popular higher level abstraction in Hadoop platform that facilitates the development of ad-hoc queries and data flows respectively without the complexities of map reduce programs directly.

Events