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.

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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5 exciting new database services from aws reinvent 2017

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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Infographics: AWS re:Invent 2017 – Product Announcements