Artificial intelligence (AI) and machine learning (ML) are powering a whole new generation of business intelligence (BI) solutions. And these mission-critical software packages are in turn one of the primary drivers behind the migration of enterprise big data to the cloud. BI tools are designed to collect and analyze current and actionable data – delivering insights into processes and workflows that can impact business operations in the near term. But what if you need those insights immediately, and you need them in the hands of employees and experts who are working simultaneously across the globe? IT stakeholders are turning to the cloud for faster, more accurate and timelier BI insights – especially in the face of Covid-19 where companies are looking to operate as economically possible and millions are forced into remote working locations.
Over the past several years, organizations have had to move quickly to deploy new data technologies alongside legacy infrastructure to drive market-driven innovations such as personalized offers, real-time alerts, and predictive maintenance. However, these technical additions--from data lakes to customer analytics platforms to stream processing--have increased the complexity of data architectures enormously, often significantly hampering an organization's ongoing ability to deliver new capabilities, maintain existing infrastructures, and ensure the integrity of artificial intelligence (AI) models. Current market dynamics don't allow for such slowdowns. Leaders such as Amazon and Google have been making use of technological innovations in AI to upend traditional business models, requiring laggards to reimagine aspects of their own business to keep up. Cloud providers have launched cutting-edge offerings, such as serverless data platforms that can be deployed instantly, enabling adopters to enjoy a faster time to market and greater agility.
Today, global innovation company Hitachi has announced its next-generation digital transformation solutions will run on Microsoft. The two companies have signed a strategic agreement to advance AI, Robotics, and IoT capabilities across logistics and manufacturing industries based in South Asia and Japan. The digital solutions would also be made available to the North American market. Each industry is unique in the way it adopts digital tools to transform its core operations. Logistics, manufacturing and supply industries are the most potent markets for digitalization.
In reviewing this year's batch of announcements for MongoDB's online user conference, there's a lot that fills the blanks opened last year as reported by Stephanie Condon. But the sleeper is unifying a platform that has expanded over the past few years with mobile and edge processing capabilities, not to mention a search engine, and the reality that Atlas, its cloud database-as-a-service (DBaaS) service, is now comprising the majority of new installs. Last year, MongoDB announced previews of Atlas Data Lake, the service of MongoDB's cloud service that lets you target data stored in Amazon S3 cloud storage; full text search, plans to integrate the then recently-acquired mobile Realm database platform with the Stretch serverless development environment; and autoscaling of MongoDB's Atlas cloud service. This year, all those previews announced last year are now going GA. Rounding it out is the announcement of the next release of MongoDB, version 4.4, that includes some modest enhancements with querying and sharding. The cloud is clearly MongoDB's future.
Deploying big-data Machine Learning (ML) services in a cloud environment presents a challenge to the cloud vendor with respect to the cloud container configuration sizing for any given customer use case. OracleLabs has developed an automated framework that uses nested-loop Monte Carlo simulation to autonomously scale any size customer ML use cases across the range of cloud CPU-GPU "Shapes" (configurations of CPUs and/or GPUs in Cloud containers available to end customers). Moreover, the OracleLabs and NVIDIA authors have collaborated on a ML benchmark study which analyzes the compute cost and GPU acceleration of any ML prognostic algorithm and assesses the reduction of compute cost in a cloud container comprising conventional CPUs and NVIDIA GPUs.
This course is all about learning various cloud Analytics and Machine Learning options available on Microsoft AZURE cloud platform. We would be creating resources for Stream Analytics, Spark, HDInsight exploring options. We would be learning all the Analytics services with some use cases. Machine learning and cloud computing are trending domains and also have lot of job opportunities, if you have interest in machine learning as well as cloud computing then this course for you. This course will let you use your machine learning skills deploy in cloud.
I am leading a super talented group of Engineers building all Kenshoo products. Our Engineering organization is organized in independent full-stack development teams that continuously deliver business value to our clients. Women make up nearly 50% of our R&D team leaders--which I am told is unusual--and this inclusive-by-design approach delivers continuous innovation in the form of unique, intuitive products and capabilities. My personal role is to maintain an engineering culture to define, change and adapt our processes and to look for the right technology to build or buy so that we can be both efficient and innovative in meeting the growing business needs of our clients. At Kenshoo we have been handling data for more than a decade.
In the current age of cloud computing, there is now a multitude of mature services available -- offering security, scalability, and reliability for many business computing needs. What was once a colossal undertaking to build a data center, install server racks, and design storage arrays has given way to an entire marketplace of services that are always just a click away. One leader in that marketplace is Amazon Web Services, which consists of 175 products and services in a vast catalog that provides cloud storage, compute power, app deployment, user account management, data warehousing, tools for managing and controlling Internet of Things devices, and just about anything you can think of that a business needs. AWS really grew in popularity and capability over the last decade. One reason is that AWS is so reliable and secure.
It wasn't so long ago that business analytics operated on a months-long cycle. For most of the twentieth century, the main interaction between a company and its data was a regular review of its most easily quantifiable measures, in the form of annual or quarterly financial assessments. Today, interacting with data this infrequently would be unimaginable in even a small business. As data availability and transfer speeds have grown at exponential rates, the time lag between intake and analysis of data has shortened to the point that, today, real-time data analytics is often part of an organisation's standard operating procedure. There are few industries which have not been lifted up by this rising tide of data.
The below excerpt showcases the distinctiveness and acumen of a holistic AI company – TransOrg Analytics that is consistently striving to roll out intelligent and scalable solutions for the betterment of its customers. TransOrg Analytics is an award-winning player in'Analytics and Advisory' space. Founded in 2009, TransOrg is headquartered in Gurugram, India with a global presence in the US, UK, Singapore, India and the Middle East. Its global clientele includes Fortune 500 companies and industry leaders in sectors like Banking, Financial Services, Insurance, Telecom, Hospitality, CPG, Retail, E-commerce, Travel & Aviation. TransOrg has a strong team of over 80 high-performing Data Scientists, Data Engineers, Visualization experts from top schools and leadership with strong academic credentials and collective work experience of over 100 years with reputed organizations.