Amazon ECS: Another Feather in AWS’ Cap

Amazon ECS Another Feather in AWS’ Cap
Amazon Elastic Container Service (ECS) is a newly developed, highly scalable and high-performance container orchestration service that supports Docker and allows users to effortlessly run and scale containerized applications on the Amazon Web Services (AWS) platform. ECS removes the need for users to install and operate container orchestration software, manage and scale clusters of virtual machines, or schedule containers on said virtual machines.

ECS is a service that introduces simplicity while running application containers in an accessible manner across multiple availability zones within a region. Users can create Amazon ECS clusters within new or existing virtual PCs. After building a cluster, users can define task definitions and services that specify running Docker container images have to across selected clusters. Container images are stored in and pulled from container registries, which exist within or outside the existing AWS infrastructure.

For vaster control, users can host tasks on a cluster of Amazon Elastic Compute Cloud (EC2) instances; this enables users to schedule the placement of containers across clusters based on resource needs, isolation policies, and availability requirements. ECS is a useful option when creating consistent deployment and build experiences, along with managing Extract-Transform-Load (ETL) workloads. Users can also develop sophisticated application architectures on a micro-services model if desired.

ECS allows users to launch and stop Docker-enabled applications with simple API calls. Perform a query about the state of an application or access additional features such as Identity and Access Management (IAM) roles, security groups, load balancers, CloudWatch Events, CloudFormation templates, and CloudTrail logs.

Recent IT developments have signaled an increasing dependency over smart cloud containers, and that is where Amazon ECS has become an essential pick. Firms are seeking more efficient and ready-to-go solutions that do not add any additional obstacle to an organizational pace. Amazon ECS offers various advantages and customization options including:

Containers Without Infrastructure Management
Amazon ECS features AWS Fargate, which enables users to deploy and manage containers without having to maintain any of the embedded underlying infrastructures. Utilizing AWS Fargate technology, users no longer need to select Amazon EC2 instance types, provision, or scale clusters of virtual machines to run containers. Fargate gives ample time for users to focus on building and running applications without having to worry about the underlying infrastructure.

Containerize Everything
Amazon ECS lets users quickly build various types of containerized applications, from long-running applications and micro-services to batch jobs and machine learning applications. ECS can migrate legacy Linux or Windows applications from on-premise solutions to the cloud and run them as containerized applications.

Secure Infrastructure
Amazon ECS provides the option of launching containers in one’s own Amazon VPC, allowing them to use the VPC security groups and network ACLs. None of the available resources expose themselves to other customers, which makes data all the more secure; it also enables users to assign granular access permissions for each of the containers using IAM to exhibit restriction on access to each service and accessible resources that a container has. This intricate level of isolation permits users to use Amazon ECS to build highly secure and reliable applications.

Performance at Scale
Amazon ECS is a product of gradually developed engineering over a period of years. Built on technology developed from many years of experience, ECS can run highly scalable services. Users can launch various Docker containers in seconds using Amazon ECS with no further introduction of complexity.

Compliment Other AWS Services
Amazon ECS is a product that works well with other AWS services and renders a complete solution for running a wide range of containerized applications. ECS can seamlessly integrate with services such as Elastic Load Balancing, Amazon VPC, AWS RDS, AWS IAM, Amazon ECR, AWS Batch, Amazon CloudWatch, AWS CloudFormation, AWS CodeStar, and AWS CloudTrail, among others.

It is important to highlight that Amazon ECS, when integrated with other AWS Services, will provide the best solution for running a wide range of containerized applications or services instead. Other popular container services such as Kubernetes and Mesos can also be efficiently run on AWS EC2.

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DEVELOPERWEEK 2018

DEVELOPERWEEK 2018
Date : February 3-7, 2018
Location : San Francisco Bay
Venue: Oakland Convention Center

Event Details
DeveloperWeek 2018 is San Francisco’s largest developer conference & event series with dozens of week-long events including the DeveloperWeek 2018 Conference & Expo, 1,000+ attendee hackathon, 1,000+ attendee tech hiring mixer, and a series of workshops, open houses, drink-ups, and city-wide events across San Francisco!

DeveloperWeek puts the spotlight on new technologies. Companies that participated in last year’s DeveloperWeek include Google, Facebook, Yelp, Rackspace, IBM, Cloudera, Red Hat, Optimizely, SendGrid, Blackberry, Microsoft, Neo Technology, Eventbrite, Klout, Built.io, Ripple, GNIP, Tagged, HackReactor, and 30+ more here!

Why Attend
Because DeveloperWeek covers all new technologies, our conference and workshops invite you to get intro lessons (or advanced tips and tricks) on technologies like HTML 5, WebRTC, Full-Stack Javascript Development, Mobile Web Design, Node.js, Data Science, and Distributed Computing to name a few.

[Know more about the Conference]

About Idexcel:idexcel is a global IT professional services and technology solutions provider specialized in AWS Cloud Services, DevOps, Cloud Application Modernization and Data Science. With keen focus on addressing immediate and strategic business challenges of customers, idexcel is centered at providing deep industry and business process expertise. The idexcel team thoroughly dedicates itself to the occupation of technology innovation and business improvisation. Aware that all businesses involve specific areas unique to their culture and environment, the Idexcel team encourages flexibility and transparency across all levels of interactions with clients. Our team of AWS certified experts ensure that clients benefit from the latest cutting-edge technology in AWS cloud.

Our Mission: Our mission is to provide effective, efficient and optimal IT professional services meeting our client’s needs. Our extensive and proven technical expertise enables us to provide the high quality of services and innovative solutions to our clients.

Allolankandy Anand Sr. Director Technical Sales & Delivery will be attending this event. For further queries, please write to anand@idexcel.com

Machine Learning’s Impact on Cloud Computing

Machine learnings impact on cloudcomputing
Increasing dependency on AI (Artificial Intelligence) and the (Internet of Things) have given new goals to cloud computing infrastructure administrators. The premises enfolding within this newly emerging subfield of Information and Technology are indeed very vast ranging from smartphones to robotics. Firms are developing new machinery requiring the least amount of dependency on human resources. Developments aimed at providing human-made mechanisms with levels of autonomy to become entirely independent.

To gain a level of autonomy over soft resources, developers have begun to depend on a mediator to assist ‘smart machines’ in increasing functional ability. As cloud computing is already taking over essential domains of human efforts such as data storage, this technological advancement will result in unprecedented impacts on the global economy.

Integrated cloud services can be even more beneficial than current offerings. The contemporary usage of cloud involves computing, storage, and networking; however, the intelligent cloud will multiply the capabilities of the cloud by rendering information from vast amounts of stored data. This will result in quick advancements within the IT field, where tasks are performed much efficiently.

Cognitive Computing
The large amounts of data stored in the cloud serve as a source of information for machines to gain their functional state. The millions of functions that are occurring daily in the cloud will provide vast sources of information for computers to learn. The entire process will equip the machine applications with sensory capabilities, and applications will be able to perform cognitive functions, making decisions best suited for them to achieve their desired goal.

Even though the intelligent cloud is in its infantile age, the propositions are predicted to increase in the coming years and revolutionize the world in the same way that the internet had. Expectations of those who would utilize cognitive computing including those in the healthcare, hospitality, and business fields

Changing Artificial Intelligence Infrastructure
With the aid of the intelligent cloud, AI as a platform service makes the process of smart automation more accessible for users by taking control of the complexities of a process; this will further increase the capabilities of cloud computing, in return growing the demand for the cloud. The interdependency of cloud computing and artificial intelligence will become the essence of new realities.

New Dimensions for the Internet of Things
Just as we are now aware how the IoT has overtaken our lives and created an undeniable dependency on gadgets, cloud-assisted machine learning is almost increasing rapidly. Smart sensors that allow cars to operate in cruise control will grasp their source of data from the cloud only. Cloud computing will become the long-term memory for the IoT where they can retrieve the data for solving in-time problems. The web’s massive of interconnectivity will generate and operate on an enormous amount of data saved in that very cloud; this will expand the horizons of cloud computing. In coming years, cloud-based machine learning will become as meaningful to machines as water is for humans.

Personal Assistance
We have already seen assistants such as Alexa, Siri, Cortana, and Google perform well in the consumer market; it is not absurd to think that an assistant will exist in every modern home by the next decade. These assistants make life easier for individuals through pre-coded voice recognition that also gives a feeling of human touch to machines.

Current assistant responses operate on a limited set of provided information. However, these assistants are very likely to be developed more finely so that their capabilities will not remain so confined. Through the increasing use of autonomous cognition, personal assistants will attain a state of reliability where they can replace human interaction. The role of cloud computing will be supremely vital in this regard, as it will become the heart and brain of these machines.

Business Intelligence
The tasks of a future intelligent cloud will be to make the tech world even smarter – autonomous learning coupled with the capabilities of understanding and rectifying real-time anomalies. In the same way, business intelligence will also become more intelligent wherein along with identifying faults, it will be able to predict future strategies in advance.

Armed with proactive analytics and real-time dashboards, businesses will operate upon predictive analytics that process previously collected data, making real-time suggestions and future predictions. These predictions from current trends and recommendations for actions would make things easier on leaders.

Revolutionizing the World
Fields like banking, education, and hospitality will be able to make use of the intelligent cloud, enhancing the precision and efficiency of the services they provide. Consider, for example, having an assistant in hospitals which diminishes doctors’ customary load of decision making by analyzing cases, making comparisons, and promoting new approaches to the treatment.

With the rapid development of both machine learning and the cloud, it seems in the future that cloud computing will become much easier to handle, scale, and protect with machine learning. Along with those mentioned above, more extensive businesses relying on the cloud will lead to the implementation of more machine learning. We will arrive at a point in which we will have no cloud service that operates as they do today.

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Amazon SageMaker in Machine Learning

Amazon SageMaker in machine Learning
Machine Learning (ML) has become the talk of the town, and its usage has grown inherent in virtually all spheres of the technology sector. As more applications are beginning to employ the use of ML in their functioning, there is a tremendous possible value for businesses. However, developers have still had to overcome many obstacles to harness the power of ML in their organizations.

Keeping the difficulty of deployment in mind many developers are turning to Amazon Web Services (AWS). Some of the challenges to processing include correctly collecting, cleaning, and formatting the available data. Once the dataset is available, it needs to be prepared, which is one of the most significant roadblocks. Post processing, there are many other procedures which need to be followed before the data can be utilized.

Why should developers use the AWS Sagemaker?
Developers need to visualize, transform, and prepare their data, before drawing insights from it. What’s incredible is that even simple models need a lot of power and time to train. From choosing the appropriate algorithm to tuning the parameters to measuring the accuracy of the model, everything requires plenty of resources and time in the long run.

With the use of AWS Sagemaker, data scientists provide easy to build, train and use Machine learning models, which don’t require extensive training knowledge for deployment. Being an end-to-end machine learning service, Amazon’s Sagemaker has enabled users to accelerate their machine learning efforts, thereby allowing them to set up and install production applications efficiently.

Bid farewell to heavy lifting along with guesswork, when it comes to using machine learning techniques. Amazon’s Sagemaker is trained to provide easy to handle pre-built development notebooks, while up-scaling popular machine learning algorithms aimed at handling petabyte-scale datasets. Sagemaker further simplifies the training process, which translates into shorter model tuning time. In the expressions of the AWS experts, the idea behind Sagemaker was to remove complexities, while allowing developers to use the concepts of Machine Learning more extensively and efficiently.

Visualize and Explore Stored Data
Being a fully managed environment, it’s easier for Sagemaker to help developers visualizer and explore stored data. The information can be modified with all of the available popular libraries, frameworks, and interfaces. Sagemaker has been designed to include the ten most commonly used algorithm structures, some of which include K-means clustering, linear regression, principal component analysis and factorization machines. All of these algorithms are designed to run ten times faster than their usual routines, allowing processing to reach more efficient speeds.

Increased Accessibility for Developers
Amazon SageMaker has been geared to make training all the more accessible. Developers can just select the quantity and the type of Amazon EC2 instances, along with the location of their data. Once the data processing process begins within Sagemaker, a distributed compute cluster is set up, along with the training, as the output is setup and directed towards Amazon S3. Amazon SageMaker is prepared to fine-tune models with a hyper-parameter optimization option, which helps adjust different combinations of algorithms, allowing the developers to arrive at the most precise predictions.

Faster One-Click Deployment
As mentioned before, Sagemaker takes care of all launching instances, which are used for setting up HTTPS end-points. This way, the application achieves high throughput with a combination of low latency predictions. At the same time, it auto-scales various Amazon EC2 instances across different availability zones (AZ) to accelerate the processing speeds and results. The main idea is to eliminate the need for heavy lifting within machine learning so that developers don’t have to indulge in elaborate coding and program development.

Conclusion
Amazon’s Sagemaker services are changing the way data is stored, processed, and trained. With a variety of algorithms in place, developers can wet their hands with the various concepts of Machine Learning, allowing themselves to understand what goes on behind the scenes. All this can be achieved without becoming too involved in algorithm preparations and logic creation. An ideal solution for companies looking forward to helping their developers focus on drawing more analytics from tons of data.

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IoT Announcements from AWS re:Invent 2017

IoT announcements
Amidst primitive turmoil in the IoT world, AWS unveiled its various solutions for IoT spreading over a large range of usage. The directionless forces of IoT will now meet the technologically advanced solutions through the hands of AWS which has offered a wide range of solutions in the arena.

AWS IoT Device Management
This product allows the user to securely onboard, organize, monitor, and remotely manage their IoT devices at scale throughout their lifecycle. The advanced features allow configuring, organizing the device inventory, monitoring the fleet of devices, and remotely managing devices deployed across many locations including updating device software over-the-air (OTA). This automatically results in reduction of the cost and effort of managing large IoT device infrastructure. It further lets the customer provision devices in bulk to register device information such as metadata, identity, and policies.

A new search capability has been added for querying against both the device attribute and device state for quickly finding devices in near real-time. Device logging levels for more granular control and remotely updating device software are also added in view of improving the device functionality.

AWS IoT Analytics
A new brain that will assist the IoT world in cleansing, processing, storing and analyzing IoT data at scale, IoT Analytics is also the easiest way to run analytics on IoT data and get insights that help project better resolutions for future acts.

IoT Analytics includes data preparation capabilities for common IoT use cases like predictive maintenance, asset usage patterns, and failure profiling etc. It also captures data from devices connected to AWS IoT Core, and filters, transforms, and enriches it before storing it in a time-series database for analysis.

The service can be set up to collect specific data for particular devices, apply mathematical transforms to process the data, and enrich the data with device-specific metadata such as device type and location before storing the processed data. IoT Analytics is used to run ad hoc queries using the built-in SQL query engine, or perform more complex processing and analytics like statistical inference and time series analysis.

AWS IoT Device Defender
The product is a fully managed service that allows the user to secure fleet of IoT devices on an ongoing basis. It audits your fleet to ensure it adheres to security best practices, detects abnormal device behavior, alerts you to security issues, and recommends mitigation actions for these security issues. AWS IoT Device Defender is currently not generally available.

Amazon FreeRTOS
Amazon FreeRTOS is an IoT operating system for microcontrollers that enables small, low-powered devices to be easily programed, deployed, secured, connected, and maintained. Amazon FreeRTOS provides the FreeRTOS kernel, a popular open source real-time operating system for microcontrollers, and includes various software libraries for security and connectivity. Amazon FreeRTOS enables the user to easily program connected microcontroller-based devices and collect data from them for IoT applications, along with scaling those applications across millions of devices. Amazon FreeRTOS is free of charge, open source, and available to all.

AWS Greengrass
AWS Greengrass Machine Learning (ML) Inference allows to perform ML inference locally on AWS Greengrass devices using models of machine learning. Formerly, building and training ML models and running ML inference was done almost exclusively in the cloud. Training ML models requires massive computing resources to naturally fit in the cloud. With AWS Greengrass ML Inference, AWS Greengrass devices can make smart decisions quickly as data is being generated, even when they are disconnected.

The product aims at simplifying each step of ML deployment. For example, with its help, the user can access a deep learning model built and trained in Amazon SageMaker directly from the AWS Greengrass console and then download it to the concerned device. AWS Greengrass ML Inference includes a prebuilt Apache MXNet framework to install on AWS Greengrass devices.

It also includes prebuilt AWS Lambda templates that is used to create an inference app. The Lambda blueprint shows common tasks such as loading models, importing Apache MXNet, and taking actions based on predictions.

AWS IoT Core
AWS IoT Core is providing new enhanced authentication mechanisms. Using the custom authentication feature, users will be able to utilize bearer token authentication strategies, such as OAuth, to connect to AWS without using a X.509 certificate on their devices. With this, they can reuse their existing authentication mechanism that they have already invested in.

AWS IoT Core also now makes it easier for devices to access other AWS services, such as to upload an image to S3. This feature removes the need for customers to store multiple credentials on their devices.

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Agile & DevOps Conference 2018

Agile & DevOps Conference
Date : 29 Jan, 2018
Location : Dallas-TX, United States
Venue: Homewood Suites by Hilton

Event Details
The conference targets to feature presentation and discussion sessions by recognized thought-leaders addressing the actual developments and trends in Agile & DevOps highlighting implementation challenges and their solutions. The conference presentations by expert speakers will make it easier to understand how Agile & DevOps can successfully bring cross-functional business units together for delivering business results speedily in the Agile environment.

Why Attend
A full day event for professionals to meet their industry peers, exchange knowledge and take away ideas for making best use of Agile & DevOps practice. Based on the conference theme ‘Let’s switch it on’, this conference provides an opportunity to learn from industry experts the concept of Agile & DevOps and how to implement it in your organizations. Get to know critical challenges faced during implementation, and their solutions. This is a great platform to meet top solution providers and industry players in this domain.

[Know more about the Conference]

About Idexcel: Idexcel is a global business that supports Commercial & Public Sector organizations as they Modernize their Information Technology using DevOps methodology and Cloud infrastructure. Idexcel provides Professional Services for the AWS Cloud that includes Program Management, Cloud Strategy, Training, Applications Development, Managed Service, Integration, Migration, DevOps, AWS Optimization and Analytics. As we help our customers modernize their IT, our clients should expect a positive return on their investment in Idexcel, increased IT agility, reduced risk on development projects and improved organizational efficiency.

Allolankandy Anand Sr. Director Technical Sales & Delivery will be attending this event. For further queries, please write to anand@idexcel.com