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

Advantages of Cloud Analytics over On-Premise Analytics

Advantages of Cloud Analytics over On-Premise Analytics
Majority of the organizations now agree that data science is a great tool to scale-up, build and streamline their businesses. But, with this huge amount of data they are collecting, are the organizations really coping up to analyze and implement the decisions in time? Most of them, in-spite of having on-premise analytics teams are in disconnection with their operations part.

Having the in-house analytics teams linked to your Enterprise Resource Planning(ERP) systems can be sometimes be irresponsive due to data loads, might cause your sales teams to lose the real-time data, also can cause delay in response to the queries. Collection of data from various internal applications, devices, online media networks, consumer data and converting them into actionable insights can be a cost consuming (both time and capital costs) process for the organizations.

Is there any better way of utilizing your Company’s data towards reaping benefits?
Yes, most of your valuable data from modes of communication to collecting track-able data of consumer behavior lies in the cloud. Cloud computing allows you to easily consolidate information from all your communication channels and resources, and helps you to do it in a wider scale.

Cloud, basically helps the business’ data teams to re-establish the connection with their operations. And hence the business will be able to minimize the time and capital costs incurred, from the research and development of the product, marketing and sales to increasing the efficiency of your consumer support teams.

How does Cloud Analytics serve as a better and real-time mode of efficient data management?

Agile Computing Resources
Instead of handling speed and delivery time related hassles from your on-premise servers, cloud computing resources are high-powered and can deliver your queries and reports in no-time.

Ad hoc Deployment of Resources for Better Performance
If you are having an in-house analytics team, you should be concerned about an efficient warehouse, latency of your data over poor public internet, being up-to date with advanced tools and experience in handling the high demands for real-time BI or emergency queries. Employing Cloud services in data science and analytics can help your business scale-up by establishing a direct connection between them, reducing the latency and response issues to less than a millisecond.

Match, Consolidate and Clean Data Effortlessly
Real time Cloud analytics with real-time access to your online data keeps your data up-to date and organized, helping your Operations and Analytics teams function under the same roof. This makes sure of no mismatches and delays, helping you to also predict and implement finer decisions.

Accessibility
Cloud services are capable in sharing data and visualization and performing cross-organizational analysis, making the raw data more accessible and perceivable by a broader user base.

High Returns on Time Investments
Cloud services provide readily-available data models, uploads, application servers, advanced tools and analytics. You need not spend any time in building up a separate infrastructure, unlike employing on-premise analytics teams.

Your marketing teams can forecast and segment your campaign plans, the campaign reports and leads generated are readily available to your sales teams to follow-up, insights from sales and marketing and more real time consumer data can help your strategy teams in predicting crucial decisions or your support teams being immediately notified with consumer queries. Better the collaboration, higher are your returns, and an ideal cloud service can make this possible.

Flexible and Faster Adoption
Cloud-based applications are built with self-learning models and have a consumer friendly user experience unlike the on-premise applications. Cloud technologies learn to adopt as your business grows and can expand or adjust as your data storage and applications needs increase or decrease.

Affordability
There are no upgrade costs or issues, and enabling new tools or applications require minimal IT maintenance. This keeps the business in a continuous flow without any interventions like the need for upgrading the on-premise infrastructure, and having to redo your integrations and other time consuming efforts.

Security
Robustly built, Cloud analytics are reportedly more reliable than on-premise systems in times of a data breach. Detecting a breach or a security issue can be within hours or minutes with Cloud security whereas with an in-house team, it takes weeks or even months in detecting a breach. Your data is more trusted and secure with cloud computing.

Implementing cloud services in data science can be the best and most-effective infrastructure you can give to your business. They are agile, secure and flexible and help you to streamline each of your business process as Cloud services enable all your teams function under the same data foundation.

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5 Exciting New Database Services from AWS re:Invent 2017

New Database Services from AWS re:Invent 2017

AWS cloud division has geared up for revolutionizing the cloud infrastructure with unveiling of its much anticipated AWS event re:Invent 2017 cloud user conference which had a distinct focus on data and so-called serverless computing. It was the sixth annual re:Invent of the cloud market leader AWS which additionally laid emphasis on competitive prices along with modern suit. Five most exciting data services of the event are as follows:

1. Amazon Neptune
A new, faster, more reliable and fully-managed graph database service that will make it easy to build and run applications that work with highly connected datasets. Besides being a high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency, Amazon Neptune supports popular graph models Apache TinkerPop and W3C’s RDF, and their associated query languages TinkerPop Gremlin and RDF SPARQL for easy query navigation. It also powers graph use cases such as recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security. It is secured with support for encryption at rest and in transit; can be fully managed, to ease out hardware provisioning, software patching, setup, configuration, or backups.

Currently available in preview with sign-up only in US East (N. Virginia) only on the R4 instance family and supports Apache TinkerPop Version 3.3 and the RDF/SPARQL 1.1 API

2. Amazon Aurora Multi-Master
Amazon Aurora Multi-Master allows the user to create multiple read/write master instances across multiple Availability Zones. This empowers applications to read and write data to multiple database instances in a cluster. Multi-Master clusters improve Aurora’s already high availability. If the user’s master instances fail, the other instances in the cluster will take over immediately for smart and flawless procession, maintaining read and write availability through instance failures or even complete AZ failures, with zero application downtime. It is a fully managed relational database that combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases.

The preview of the product will be available for the Aurora MySQL-compatible edition, and people can participate by filling out the signup form on AWS’ official website.

3. Amazon DynamoDB On-Demand Backup
On-Demand Backup allows one to create full backups of DynamoDB tables data for data archival, helping them meet corporate and governmental regulatory requirements. People can also backup tables from a few megabytes to hundreds of terabytes of data, with no impact on performance and availability to your production applications. It processes back up requests in no time regardless of the size of tables, which makes the operators carefree of the backup schedules or long-running processes. All backups are automatically encrypted, cataloged, easily discoverable, and retained until manually deleted. It allows the facility of single-click backup and restore operations in the AWS Management Console or a single API call.

Initially it is being rolled out only to US East (N. Virginia), US East (Ohio), US West (Oregon), EU (Ireland) regions. In early 2018, users will be able to opt-in to DynamoDB Point-in-Time Restore (PITR) which will allow to restore your data up to the minute for the past 35 days, further protecting your data from loss due to application errors.

4. Amazon Aurora Serverless
An on-demand auto-scaling configuration for Amazon Aurora, Serverless will enable database’s automatic start up, shut down, and scale up or down capacity based on application’s needs. It enables the user to run relational database in the cloud without managing any database instances or clusters. It is built for applications with infrequent, intermittent or unpredictable workloads of likes as online games, low-volume blogs, new applications where demand is unknown, and dev/test environments that don’t need to run all the time. Current database solutions require a significant provisioning and management effort to adjust capacity, leading to worries about over- or under-provisioning of resources.We can also optionally specify the minimum and maximum capacity that an application needs, and only pay for the resources are consumed. The serverless computing is going to hugely benefit the world of relational databases.

5. Amazon DynamoDB Global Tables

The advanced Global Tables builds upon DynamoDB’s global footprint to provide a fully managed multi-region, multi-master global database that renders fast local read and write performance for massively scaled applications across the globe. It replicates data between regions and resolves update conflicts, enabling developers to focus on the application logic when building globally distributed applications. In addition, it enables various applications to stay highly available even in the unlikely event of isolation or degradation of an entire region.

Global Tables is available at the time only in five regions: US East (Ohio), US East (N. Virginia), US West (Oregon), EU (Ireland, and EU (Frankfurt).

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