Databases can be deployed within seconds. The database appears to be lightning fast. A query was done on several terabytes of data within less than a second. Google provides large vendor free datasets that companies can use. Google also provides machine learning training to allow customers to come up to speed rapidly. Google provides pre-trained models or allows customers to train their own models. Google has provided the open source TensorFlow machine learning platform. Digital Intelligence API — can analyze a video and provide context and relevance data on the video.
The API can also scan a video catalog and retrieve relevant video content. The platform that Google provides allows developers to focus on just designing and developing code rather than getting distracted by provisioning and managing infrastructure.
They have a large streaming analytics platform as well as a large data platform. The platform is enabled by Google Cloud Platform. The Google Cloud Platform allowed the organization to scale their business. Their analysts can now write queries against Big Query and has brought data science to the masses. Google spends significant resources on the partnerships it has with its customers. There should be a shared success model.
The partnership should be a commitment, not a mandatory sentence. There should be flexible deployment models and flexible support models. Google does not dictate VM configurations. Google has per second billing. Kohls is a brick and mortar retailer with a growing digital presence. They have two data centers with rack-and-stack servers.
They are moving to the cloud in order to scale for peak periods such as the holiday season. Scott reiterated the primary themes that were highlighted in the keynote. Overall this was a solid keynote for a technical crowd. Jerry M Reghumadh of Capiot did a talk on building micro-services from the ground up. The legacy system that his group replaced was monolithic and rigid. The solution the Capiot team proposed to the client placed each component of the ERP system into its own atomic component.
Everything on the platform that was built was an API. The engineering decisions that were made included the choice of NodeJS and MongoDB as the base technologies for this platform. NodeJS was selected in part, because of its small footprint. This lowered the barrier to entry for the application. Java was considered, but it was too heavy for the needs of the project. MongoDB was selected for the data persistence layer because it saves data as documents and it did not require the marshaling and unmarshaling of data.
MongoDB also allowed the implementation team to use a flexible schema. MongoDB offered greater ease of clustering and sharding versus other available options for this project. This allowed the developers to implement this without relying on a dedicated database administrator.
The team implemented a governance model that forced any exposed API to be exposed in Swagger. Any API not exposed in swagger would not work properly in the system. Mongoose allowed the team to enforce a schema. She began her talk by discussing analytics versus data insight. R has become a standard for analyzing data due to its open source nature and easy licensing requirements versus some legacy tools, such as SAS or SPSS.
The human genome consists of billions of gene pairs. In doing genomic analysis, schema design becomes important in making the analysis easier and more effective. One issue that must be addressed is scalability. Since R is a single-threaded application, data scientists come up against data volume constraints. One solution to this is to use Spark to parallelize and scale R. This operational component consists of an application cube and a MongoDB driver. The data management component consists of the MongoDB cluster.
Atlas allows administrators, developers, and managers to deploy a complete MongoDB cluster in a matter of minutes. Some basic requirements for using Atlas include:. Atlas supports the live migration of data from an EC2 instance. There is something that is energizing about gameplay. A scan of the brain has shown that the opposite of play may be depression.
Gameplay seems to increase activity in the hippocampus portion of the brain. In spite of this, gamers tend to activate the ability to learn. Jane does game research and analyzed the psychology of gameplay. Pokemon Go was fastest downloaded app in the history of apps. There were million downloads in 30 days.
Why was this game so popular? Pokemon go elicits a sense of opportunity for the players and engages users.
When million people start walking around playing this mobile game, a lot of data is generated. There have been analyses done around the statistics around the usage of this game. Augmented Reality may be the most compelling experience and platform for gaming vs.
The lessons learned from gaming data are:. Emotiv is a sensing device that can detect emotions. Mooditood is a social network to share how people feel.
Back to top Top. A Brief History Health technology in general dates back to ancient times and is as old as civilization. Early Medical Technology Advancements While it is probably impossible to know definitively when the first artifact of medical technology was created, we have examples of early medical technology dating back years. Medical Technology in the 20th Century The twentieth century saw an explosion in medical advances and in the use of technology in medicine.
Healthcare Supply Chain Breakdown and Failures Early in the pandemic, there were shortages of PPE Personal Protective Equipment which greatly increased the risk to medical professionals and impeded care to the infected. Accelerated Research and Development and Path to Production The urgency of the pandemic necessitated an acceleration in the development of diagnostic and treatment tools to address the healthcare crisis.
The Road Ahead Technology offers great potential benefits to the healthcare industry and enormous challenges. Telemedicine The remote delivery of medical care via telemedicine took off in in a way that was not expected for at least another 5 years. Machine Learning in Healthcare Machine learning tools are already used in the healthcare space and this is likely to expand greatly over the next 10 years.
The Open Source Model in Healthcare The use of open platforms and tools has the potential to increase innovation and reduce costs in the healthcare space. End to End Preparedness There is a general consensus that the world was ill-prepared for the impact of COVID and there is a recognition that there is a need for better preparedness.
Cloud Computing in Healthcare Cloud technology, as in other areas, will be one of the foundation technologies that underpins healthcare in the coming years and perhaps decades. Dick, Elaine B. Steen, and Don E. Bollyky and Stewart M. The 3. Some critical questions to consider before hiring a freelancer include the following: What is the goal of the project? Cloudsearch is a tool which allows users to search for content across all of their G Suite content.
G Suite uses simple controls to allow for easy administration of the tools. There has been an acceleration of businesses trying to use the cloud to increase productivity.
Google has worked to democratize data within organizations to increase collaboration and innovation. Google develops tools and platforms to facilitate this and open sources much of this technology.
This is the first horizontally scalable relational database. Cost is calculated in real time as the database architecture is specified. Databases can be deployed within seconds. The database appears to be lightning fast.
A query was done on several terabytes of data within less than a second. Google provides large vendor free datasets that companies can use. Google also provides machine learning training to allow customers to come up to speed rapidly. Google provides pre-trained models or allows customers to train their own models. Google has provided the open source TensorFlow machine learning platform.
Digital Intelligence API — can analyze a video and provide context and relevance data on the video. The API can also scan a video catalog and retrieve relevant video content. The platform that Google provides allows developers to focus on just designing and developing code rather than getting distracted by provisioning and managing infrastructure.
They have a large streaming analytics platform as well as a large data platform. The platform is enabled by Google Cloud Platform. The Google Cloud Platform allowed the organization to scale their business. Their analysts can now write queries against Big Query and has brought data science to the masses. Google spends significant resources on the partnerships it has with its customers. There should be a shared success model. The partnership should be a commitment, not a mandatory sentence.
There should be flexible deployment models and flexible support models. Google does not dictate VM configurations. Google has per second billing. Kohls is a brick and mortar retailer with a growing digital presence.
They have two data centers with rack-and-stack servers. They are moving to the cloud in order to scale for peak periods such as the holiday season. Scott reiterated the primary themes that were highlighted in the keynote. Overall this was a solid keynote for a technical crowd. Jerry M Reghumadh of Capiot did a talk on building micro-services from the ground up. The legacy system that his group replaced was monolithic and rigid. The solution the Capiot team proposed to the client placed each component of the ERP system into its own atomic component.
Everything on the platform that was built was an API. The engineering decisions that were made included the choice of NodeJS and MongoDB as the base technologies for this platform. NodeJS was selected in part, because of its small footprint. This lowered the barrier to entry for the application. Java was considered, but it was too heavy for the needs of the project. MongoDB was selected for the data persistence layer because it saves data as documents and it did not require the marshaling and unmarshaling of data.
MongoDB also allowed the implementation team to use a flexible schema. MongoDB offered greater ease of clustering and sharding versus other available options for this project. This allowed the developers to implement this without relying on a dedicated database administrator. The team implemented a governance model that forced any exposed API to be exposed in Swagger. Any API not exposed in swagger would not work properly in the system.
Mongoose allowed the team to enforce a schema. She began her talk by discussing analytics versus data insight. R has become a standard for analyzing data due to its open source nature and easy licensing requirements versus some legacy tools, such as SAS or SPSS. The human genome consists of billions of gene pairs. In doing genomic analysis, schema design becomes important in making the analysis easier and more effective.
One issue that must be addressed is scalability. Since R is a single-threaded application, data scientists come up against data volume constraints. One solution to this is to use Spark to parallelize and scale R. This operational component consists of an application cube and a MongoDB driver. The data management component consists of the MongoDB cluster. Atlas allows administrators, developers, and managers to deploy a complete MongoDB cluster in a matter of minutes. Some basic requirements for using Atlas include:.
Atlas supports the live migration of data from an EC2 instance. There is something that is energizing about gameplay. A scan of the brain has shown that the opposite of play may be depression. Gameplay seems to increase activity in the hippocampus portion of the brain. In spite of this, gamers tend to activate the ability to learn. Jane does game research and analyzed the psychology of gameplay. Pokemon Go was fastest downloaded app in the history of apps.
There were million downloads in 30 days. Why was this game so popular? Pokemon go elicits a sense of opportunity for the players and engages users. When million people start walking around playing this mobile game, a lot of data is generated. There have been analyses done around the statistics around the usage of this game. Augmented Reality may be the most compelling experience and platform for gaming vs.
The lessons learned from gaming data are:. Emotiv is a sensing device that can detect emotions. Mooditood is a social network to share how people feel. Back to top Top. A Brief History Health technology in general dates back to ancient times and is as old as civilization. Early Medical Technology Advancements While it is probably impossible to know definitively when the first artifact of medical technology was created, we have examples of early medical technology dating back years.
Medical Technology in the 20th Century The twentieth century saw an explosion in medical advances and in the use of technology in medicine. Healthcare Supply Chain Breakdown and Failures Early in the pandemic, there were shortages of PPE Personal Protective Equipment which greatly increased the risk to medical professionals and impeded care to the infected.
Accelerated Research and Development and Path to Production The urgency of the pandemic necessitated an acceleration in the development of diagnostic and treatment tools to address the healthcare crisis. The Road Ahead Technology offers great potential benefits to the healthcare industry and enormous challenges. Telemedicine The remote delivery of medical care via telemedicine took off in in a way that was not expected for at least another 5 years. Machine Learning in Healthcare Machine learning tools are already used in the healthcare space and this is likely to expand greatly over the next 10 years.
The Open Source Model in Healthcare The use of open platforms and tools has the potential to increase innovation and reduce costs in the healthcare space. Resides in Mastic Beach, NY. Includes Address 13 Phone 7 Email 2. Resides in Easley, SC. Includes Address 7. Resides in Wautoma, WI. Includes Address 9 Phone 7 Email 5. Resides in Chicago, IL. Includes Address 13 Phone 26 Email 4. Includes Address 6 Phone 2. Resides in Mosinee, WI. Also known as Jr Bray.
Includes Address 6 Phone 4 Email 2. Resides in Piqua, OH. Lived In Sidney OH. Includes Address 10 Phone 13 Email 7. Includes Address 9 Phone 7. Resides in Sidney, OH. Lived In Arlington TX. Includes Address 8 Phone 11 Email 6. Resides in Santa Maria, CA. Includes Address 5 Phone 2 Email 1. Resides in Akron, OH. Includes Address 8 Phone 2. Also known as James Vbray. Includes Address 11 Phone 9 Email 3.
Resides in Saint Marys, GA.
0コメント