Connecting Technology and Business.

A free ticket to kickstart your Digital Transformation journey with Amazon

If your enterprise is preparing for a digital transformation journey and is looking for a simple strategy to test waters (or road testing, if you want), here is what none can refuse to accept – a free ticket to kick start your journey and that with the pioneer that offered infrastructure as a service – Amazon.

Let us first look at what services are offered for free for 12 months by AWS in its Free Tier

(Only available to new AWS customers, and are available for 12 months following an AWS sign-up date).

Elastic Compute Cloud (EC2)

Use this to create Virtual machines for your workloads.

  • 750 hours of Amazon EC2 Linux t2.micro instance usage (1 GiB of memory and 32-bit and 64-bit platform support) – enough hours to run continuously each month
  • 750 hours of Amazon EC2 Microsoft Windows Server† t2.micro instance usage (1 GiB of memory and 32-bit and 64-bit platform support) – enough hours to run continuously each month

Elastic Load Balancer

Automatically distributes incoming application traffic across multiple targets – Available as Application load balancer, Network load balancer and Classic load balancer

  • 750 hours of an Elastic Load Balancer shared between Classic and Application load balancers, 15 GB data processing for Classic load balancers, and 15 LCUs for Application load balancers

Elastic Block Storage

Persistent block storage volumes for EC2 instances / Virtual machines

  • 30 GB of Amazon Elastic Block Storage in any combination of General Purpose (SSD) or Magnetic, plus 2 million I/Os (with EBS Magnetic) and 1 GB of snapshot storage

Elastic Container Registry

A fully-managed Docker container registry that makes it easy for developers to store, manage, and deploy Docker container images.

  • 500 MB-month of Amazon Elastic Container Registry storage for new customers

Amazon Simple Storage Service (S3)

Object storage built to store and retrieve any amount of data from anywhere

  • 5 GB of Amazon S3 standard storage, 20,000 Get Requests, and 2,000 Put Requests

Amazon Elastic File System (EFS)

A simple, scalable file storage for use with Amazon EC2 instances

  • 5 GB per month of Amazon EFS capacity free

Amazon Relational Database Service (RDS)

Set up, operate, and scale a relational database in the cloud.

  • 750 hours of Amazon RDS Single-AZ db.t2.micro Instances, for running MySQL, PostgreSQL, MariaDB, Oracle BYOL or SQL Server (running SQL Server Express Edition) – enough hours to run a DB Instance continuously each month
  • 20 GB of database storage, in any combination of RDS General Purpose (SSD) or Magnetic storage
  • 10 million I/Os (for use with RDS Magnetic storage; I/Os on RDS General Purpose (SSD) storage are not separately billed)
  • 20 GB of backup storage for your automated database backups and any user-initiated DB Snapshots

Amazon Cloud Directory

Enables you to build flexible cloud-native directories for organizing hierarchies of data along multiple dimensions. With Cloud Directory, you can create directories for a variety of use cases, such as organizational charts, course catalogs, and device registries including AD LDS

  • 1GB of storage per month; 10,000 write requests per month; 100,000 read requests per month;

Amazon Connect

A self-service cloud-based contact center service to deliver better customer service

  • 90 minutes per month of Amazon Connect usage; A local direct inward dial (DID) number for the AWS region; 30 minutes per month of local (to the AWS region) inbound DID calls; 30 minutes per month of local (to the AWS region) outbound calls; For US regions, a US toll-free number for use per month and 30 minutes per month of US inbound toll-free calls

Amazon GameLift

A managed service for deploying, operating, and scaling dedicated game servers for session-based multiplayer games

  • 125 hours per month of Amazon GameLift c4.large.gamelift On-Demand instance usage; 50 GB EBS General Purpose (SSD) storage

Data Transfer

  • 15 GB of data transfer out and 1GB of regional data transfer aggregated across all AWS services

Amazon Data Pipeline

A web service to reliably process and move data between different AWS compute and storage services, as well as on-premises data sources, at specified intervals

  • 3 low frequency preconditions running on AWS per month; 5 low frequency activities running on AWS per month

Amazon ElastiCache

Fully managed Redis and Memcached to seamlessly deploy, operate, and scale popular open source compatible in-memory data stores

  • 750 hours of Amazon ElastiCache cache.t2micro Node usage - enough hours to run continuously each month.

Amazon CloudFront

A global content delivery network (CDN) service that securely delivers data, videos, applications, and APIs to viewers with low latency and high transfer speeds.

  • 50 GB Data Transfer Out, 2,000,000 HTTP and HTTPS Requests of Amazon CloudFront

Amazon API Gateway

A fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale

  • 1 Million API Calls per month

Amazon Cognito

Add user sign-up, sign-in and access control of web and mobile application users

  • The Your User Pool feature has a free tier of 50,000 MAUs each month; 10 GB of cloud sync storage; 1,000,000 sync operations per month.

Amazon Sumerian

Create and run virtual reality (VR), augmented reality (AR), and 3D applications quickly and easily without requiring any specialized programming or 3D graphics expertise.

  • 50MB published scene that receives 100 views per month for free in the first year

Amazon Elasticsearch Service

A fully managed service to deploy, secure, operate, and scale Elasticsearch for log analytics, full text search, application monitoring etc.

  • 750 hours per month of a single-AZ t2.micro.elasticsearch instance or t2.small.elasticsearch instance; 10GB per month of optional EBS storage (Magnetic or General Purpose)

Amazon Pinpoint

Engage your customers by tracking the ways in which they interact with your applications

  • 5,000 free targeted users per month; 1,000,000 free push notifications per month; 100,000,000 events per month

AWS OpsWorks for Chef Automate

A fully-managed configuration management service that hosts Chef Automate, a suite of automation tools from Chef for configuration management, compliance and security, and continuous deployment.

  • 7500 node hours (which equals 10 nodes) per month

AWS OpsWorks for Puppet Enterprise

A fully-managed configuration management service that hosts Puppet Enterprise, a set of automation tools from Puppet for infrastructure and application management.

  • 7500 node hours (which equals 10 nodes) per month

Amazon Polly

A Text-to-speech service that turns text into lifelike speech, allowing to create applications that talk, and build entirely new categories of speech-enabled products

  • 5 million characters per month


A managed cloud platform that lets connected devices easily and securely interact with cloud applications and other devices.

  • 250,000 messages (published or delivered) per month

Amazon Lex

An automatic speech recognition / speech-to-text service for building conversational interfaces into any application using voice and text

  • 10,000 text requests per month; 5,000 speech requests per month

Here below is the list of services that are always free (non-expiring)

These free tier offers do not automatically expire at the end of your 12 month AWS Free Tier term and are available to all AWS customers. 

Amazon DynamoDB

A fully managed, fast and flexible NoSQL database service for all applications that need consistent, single-digit millisecond latency at any scale

  • 25 GB of Storage, 25 Units of Read Capacity and 25 Units of Write Capacity – enough to handle up to 200M requests per month with Amazon DynamoDB.

Amazon Cognito

Add user sign-up, sign-in and access control of web and mobile application users

  • The Your User Pool feature has a free tier of 50,000 MAUs each month; The Federated Identities feature for authenticating users and generating unique identifiers is always free with Amazon Cognito.

(The Your User Pool feature is currently in Beta and you will not be charged for sending SMS messages for Multi-Factor Authentication (MFA) and phone verification. However, separate pricing for sending SMS messages will apply after the conclusion of Beta period.)

AWS CodeCommit

A fully-managed source control service that makes it easy for companies to host secure and highly scalable private Git repositories

  • 5 active users per month; 50 GB-month of storage per month; 10,000 Git requests per month

Amazon CloudWatch

A monitoring service for AWS cloud resources and the applications you run on AWS

  • 10 Amazon Cloudwatch custom metrics, 10 alarms, and 1,000,000 API requests; 5 GB of Log Data Ingestion; 5 GB of Log Data Archive; 3 Dashboards with up to 50 metrics each per month


Analyze and debug production, distributed applications, such as those built using a microservices architecture

  • 100,000 traces recorded per month; 1,000,000 traces scanned or retrieved per month

Amazon Mobile Analytics – Now called Amazon Pinpoint

Engage customers by tracking the ways in which they interact with your applications.

  • 100 million free events per month

AWS Database Migration Service

Migrate databases to AWS quickly and securely

  • 750 Hours of Amazon DMS Single-AZ dms.t2.micro instance usage; 50 GB of included General Purpose (SSD) storage

AWS Storage Gateway

A hybrid storage service that enables your on-premises applications to seamlessly use AWS cloud storage for backup and archiving, disaster recovery, cloud bursting, storage tiering, and migration

  • Up to 100GB a month free; up to $125 a month maximum charges

Amazon Chime

A communications service for online meetings, video conferencing, calls, chat, and to share content, both inside and outside your organization.

  • Unlimited usage of Amazon Chime Basic

Amazon Simple Workflow Service (SWF)

A task-based API that makes it easy to coordinate work across distributed application components by providing a programming model and infrastructure for coordinating distributed components and maintaining their execution state in a reliable way.

  • 1,000 Amazon SWF workflow executions and a total of 10,000 activity tasks, signals, timers and markers, and 30,000 workflow-days.

Amazon Simple Queue Service (SQS) and Amazon Simple Notification Service (SNS)

SQS is a fully managed message queuing service to decouple and scale microservices, distributed systems, and serverless applications. SNS is a flexible, fully managed pub/sub messaging and mobile notifications service for coordinating the delivery of messages to subscribing endpoints and clients.

  • 1,000,000 Requests of Amazon Simple Queue Service; 1,000,000 Requests, 100,000 HTTP notifications and 1,000 email notifications for Amazon Simple Notification Service

Amazon Elastic Transcoder

A media transcoding service for developers and businesses to convert (or “transcode”) media files from their source format into versions that will playback on devices like smartphones, tablets and PCs.

  • 20 minutes of SD transcoding or 10 minutes of HD transcoding

AWS Key Management Service

A managed service to create and control the encryption keys used to encrypt your data, and uses Hardware Security Modules (HSMs) to protect the security of your keys

  • 20,000 free requests per month

AWS Lambda

A platform service to run code without provisioning or managing servers

  • 1,000,000 free requests per month; Up to 3.2 million seconds of compute time per month

AWS CodePipeline

A continuous integration and continuous delivery service for application and infrastructure updates. 

  • 1 active pipeline per month

AWS Device Farm

An app testing service that lets you test and interact with your Android, iOS, and web apps on many devices at once, or reproduce issues on a device in real time.

  • Free one-time trial of 1,000 device minutes

AWS Step Functions

A serverless platform service to orchestrate AWS Lambda functions for serverless applications.

  • 4,000 state transitions per month

Amazon SES

A cloud-based email sending service designed to help digital marketers and application developers send marketing, notification, and transactional emails to customers.

  • 62,000 Outbound Messages per month to any recipient when you call Amazon SES from an Amazon EC2 instance directly or through AWS Elastic Beanstalk.; 1,000 Inbound Messages per month.

Amazon QuickSight

A business analytics service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data

  • 1 user, 1 GB of SPICE (Super-fast, Parallel, In-memory, Calculation Engine)

Amazon Glacier

A secure, durable cloud storage service for data archiving and long-term backup

  • 10 GB of Amazon Glacier data retrievals per month for free. The free tier allowance can be used at any time during the month and applies to Standard retrievals.

Amazon Macie

A security service that uses machine learning to automatically discover, classify, and protect sensitive data in AWS.

  • 1 GB processed by the content classification engine; 100,000 events

AWS Glue

A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics

  • 1 Million objects stored in the AWS Glue Data Catalog; 1 Million requests made per month to the AWS Glue Data Catalog

AWS CodeBuild

A fully managed build service that compiles source code, runs tests, and produces software packages that are ready to deploy

  • 100 build minutes per month of build.general1.small compute type usage

 † The following Windows variants are not eligible for the free tier: Microsoft Windows Server 2008 R2 with SQL Server Web, Microsoft Windows Server 2008 R2 with SQL Server Standard, Microsoft Windows 2008 R2 64-bit for Cluster Instances and Microsoft Windows 2008 R2 SQL Server 64-bit for Cluster Instances.

AWS Marketplace offers free and paid software products that run on the AWS Free Tier. If you qualify for the AWS Free Tier, you can use these products on an Amazon EC2 t2.micro instance for up to 750 hours per month and pay no additional charges for the Amazon EC2 instance (during the 12 months).

Refer this page for more details

Digital Transformation helps Microsoft weed out fake marketing leads

Microsoft has showcased how it solved the Fake leads problem as a Leader in Digital Transformation

“Fake leads” is the problem to tackle

When people sign up via online forms, they sometimes give a fake name, company name, email, or phone number. They may submit randomly typed characters (keyboard gibberish) or use profanity. Or, they may accidentally make a small typographical error, but otherwise the name is real—so we don’t want to classify the lead as junk.

The abundance of fake lead names across Microsoft subsidiaries results in:

·         Lost productivity for our global marketers and sellers. Fake names waste an enormous amount of time since sellers rely on accurate information to follow up with leads.

·         Lost revenue opportunities. Among thousands of fake lead names, there could be one legitimate opportunity.

Each day, thousands of people sign up using thousands of web forms. But, in any month, many of the lead names—whether a company or a person—are fake.

The solution to tackle “Fake leads”

Improving data quality is critical. To do that, and to determine if names are real or fake, Microsoft built a machine learning solution that uses:

·         Microsoft Machine Learning Server (previously Microsoft R Server).

·         A data quality service that integrates machine learning models. When a company name enters the marketing system, the system calls their data quality service, which immediately checks if it’s a fake name.

So far, machine learning has reduced the number of fake company names that enter Microsoft’s marketing system, at scale. Their solution has prevented thousands of names from being routed to marketers and sellers. Filtering out junk leads has made their marketing and sales teams more efficient, allowing them to focus on real leads and help customers better.

Microsoft Machine Learning Server

Microsoft needed a scalable way to eliminate fake names across millions of records and to build and operationalize their machine learning model—in other words, they wanted a systematic, automated approach with measurable gains. They chose Machine Learning Server, in part, because:

·         It can handle their large datasets—which enables them to train and score their model.

·         It has the computing power that they need.

·         They can control how they scale their model and operationalize for high-volume business requests.

·         Access is based on user name and password, which are securely stored in Azure Key Vault.

·         It helps expose the model as a secure API that can be integrated with other systems and improved separately.

The difference between rule-based model to Machine Learning


Experts create static rules to cover common scenarios. As new scenarios occur, new rules are written. A static, rules-based model can make it hard to capture varying types of keyboard gibberish (like akljfalkdjg). With static rules, Microsoft’s marketers must waste time sorting through the fake leads and deciphering misleading or confusing information.

Machine Learning

Algorithms are used to train the model and make intelligent predictions. Algorithms help build and train the model by labeling and classifying data at the beginning of the process. Then, as data enters the model, the algorithm categorizes the data correctly—saving valuable time. Microsoft used the Naive Bayes classifier algorithm to categorize names as real/fake. This algorithm is influenced by how LinkedIn detects spam names in their social networks.

Scenarios where the model is used

Microsoft’s business team identified their subsidiaries worldwide that are most affected by fake names. Now, they are weeding out fake names so that marketers and sellers don’t have to. Going forward, they plan to:

·         Create a lead data quality metric with more lead-related signals and other machine learning models that allow them to stack-rank their leads. The goal is to give a list to their sellers and marketers that suggests which leads to call first and which to call next.

·         Make contact information visible to their sellers and marketers when they’re talking on the phone with leads. For example, if the phone number that someone gave in an online form is real, but the company name isn’t, their seller can ask the lead to confirm the company name.

Choosing the technology

Microsoft incorporated the following technologies into their solution:

·         The programming language R and the Naive Bayes classifier algorithm for training and building the model are based, in part, on the approach that LinkedIn uses.

·         Machine Learning Server with machine learning, R, and artificial intelligence (AI) capabilities help them build and operationalize their model.

·         Their data quality service, which integrates with the machine learning models to determine if a name is fake – person or company.

Designing the approach

Microsoft designed their overall architecture and process to work as follows:

1.       Marketing leads enter their data quality and enrichment service, where their team does fake-name detection, data matching, validation, and enrichment. They combine these data activities using a 590-megabyte model. Their training data consists of about 1.5 million real company names and about 208,312 fake (profanity and gibberish) company names. Before they train the model, they remove commonly used company suffixes such as Private, Ltd., and Inc.

2.       They generate n-grams—combinations of contiguous letters—of three to seven characters and calculate probabilities that each n-gram belongs to the real/fake name dataset in the model. For example, an n-gram that shows three sequenced letters of the name “Microsoft” would look like “Mic,” “icr” “cro” and so on. The training process computes how often the n-grams occur in real/fake company names and stores the computation in the model.

3.       They have four virtual machines that run Machine Learning Server. One serves as a web node and three serve as compute nodes. They have more compute nodes so that they can scale to handle the volume of requests that they have. The architecture gives them the ability to scale up or down by adding/removing compute nodes as needed based on the volume of requests. The provider calls a web API hosted on the web node, with company name as input.

4.       The web API calls the scoring function on the compute node. This scoring function generates n-grams from the input company name and calculates the frequencies of these n-grams in the real/fake training dataset.

5.       To determine whether the input company name is real or fake, the predict function in R uses these calculated n gram frequencies stored in the model, along with the Naive Bayes rule.

To summarize, the scoring function that’s used during prediction generates the n-grams. It uses the frequencies of each n-gram in the real/fake name dataset that’s stored in the model to compute the probability of the company name belonging to the real/fake name dataset. Then, it uses these computed probabilities to determine if the company name is fake.

What Microsoft learned about Business, technical, and design considerations

·         Ideally, the business problem should be solved within your organization itself rather than outsourcing it. Your organization will have deeper historical knowledge of the business domain, which helps to design the most relevant solution.

·         Having good training and test data is crucial. Most of the work Microsoft did was labeling their test data, analyzing how Naive Bayes performed compared to rxLogisticRegression and rxFastTrees algorithms, determining how accurate their model was, and updating their model where needed.

·         When you design a machine learning model, it’s important to identify how to effectively label the raw data. Unlabeled data has no information to explain or categorize it. Microsoft labels the names as fake/real and apply the machine learning model. This model takes new, unlabeled data and predicts a likely label for it.

·         Even in machine learning, you risk having false positives and negatives, so you need to keep analyzing predictions and retraining the model. Crowdsourcing is an effective way to analyze whether the predictions from the model are correct; otherwise, these can be time-consuming tasks. In Microsoft’s case, due to certain constraints they faced, they didn’t use crowdsourcing, but they plan to do so in the future.

Operationalizing with Machine Learning Server vs. other Microsoft technologies

Some other technical and design considerations included deciding which Microsoft technologies to use for creating machine learning models. Microsoft offers great options such as Machine Learning Server, SQL Server 2017 Machine Learning Services (previously SQL Server 2016 R Services), and Azure Machine Learning Studio. Here are some tips to help you decide which to use for creating and operationalizing your model:

·         If you don’t depend on SQL Server for your model, Machine Learning Server is a great option. You can use the libraries in R and Python to build the model, and you can easily operationalize R and Python models. This option allows you to scale out as needed and lets you control the version of R packages that you want to use for modeling.

·         If you have training data in SQL Server and want to build a model that’s close to your training data, SQL Server 2017 Machine Learning Services works well—but there are dependencies on SQL Server and limits on model size.

·         If your model is simple, you could build it in SQL Server as a stored procedure without using libraries. This option works well for simpler models that aren’t hard to code. You can get good accuracy and use fewer resources, which saves money.

·         If you’re doing experiments and want quick learning, Azure Machine Learning Studio is a great choice. As your training dataset grows and you want to scale your models for high-volume requests, consider Machine Learning Server and SQL Server 2017 Machine Learning Services.

Challenges and roadblocks Microsoft faced

·         Having good training data. High-quality training data begins with a collection of company names that are clearly classified as real or fake—ideally, from companies around the world. Microsoft feeds that information into their model for it to start learning the patterns of real or fake company names. It takes a while to build and refine this data, and it’s an iterative process.

·         Identifying and manually labeling the training and test dataset. Microsoft manually labeled thousands of records as real or fake, which takes a lot of time and effort. Instead, one can take advantage of crowdsourcing services if possible, to avoid manual labeling. With these services, one can submit company names through a secure API and a human says if the company name is real or fake.

·         Deciding which product to use for operationalizing the model. Microsoft tried different technologies, but found computing limitations and versioning dependencies between the R Naive Bayes package they used and what was available in Azure Machine Learning Studio at the time. Microsoft chose Machine Learning Server because it addressed those issues, had the computing power they needed, and helped them easily scale out their model.

·         Configuring load balance. If Microsoft’s Machine Learning Server web node gets lots of requests, it randomly chooses which of the three compute nodes to send the request to. This can result in one node that’s overutilized while another is underutilized. They like to use a round-robin approach, where all nodes are used equally to better distribute the load. This can be achieved by using an Azure load balancer in between the web and compute node.

Measurable benefits Microsoft has seen so far

The gains Microsoft has made thus far are just the beginning. So far, Machine Learning Server has helped them in the following ways:

·         With the machine learning model, their system tags about 5 to 9 percent more fake records than the static model. This means the system prevented 5 to 9 percent more fake names from going to marketers and sellers. Over time, this represents a vast number of fake names that their sellers do not have to sort through. As a result, marketer and seller productivity is enhanced.

·         They have captured more gibberish data and most profanities, with fewer fake positives and fake negatives. They have a high degree of accuracy, with an error rate of +/– 0.2 percent.

·         Their time to respond to requests has improved. With 10,000 data classifications of real/fake in 16 minutes and 200,000 classifications in 3 hours 13 minutes, they have ensured that their data quality service meets service level agreements for performance and response time. They plan to improve response time by slightly modifying the algorithm in Python.

Next steps

Microsoft is excited about how their digital transformation journey has already enabled them to innovate and be more efficient. They will build on this momentum by learning more about business needs and delivering other machine learning solutions. Their roadmap includes:

·         Ensuring that their machine learning model delivers value end-to-end. Machine learning is just one link in the chain that reaches all the way to sellers and marketers around the world. The whole chain needs to work well.

·         Expanding their set of models and making business processes and lead quality more AI-driven vs. rule-driven.

·         Operationalizing other machine learning models, so that they get a holistic view of a lead.

·         Addressing issues created from sites that create fake registrations.

By improving data quality at scale, Microsoft is enabling marketers and sellers to focus on customers and to sell their products, services, and subscriptions more efficiently.

A free ticket to kickstart your Digital Transformation journey with Microsoft

Microsoft Azure

You can start your digital transformation journey today - your first mile is free.

Access a number of services available in Microsoft Azure without paying a penny (or rupee). Some are available for free for the first 12 months while many are always free. Added to this, you also get a pocket money of ₹13,300 to spend for the first month of your journey.

Let us first look at what services are always offered for free by Microsoft

1.       Do you want to quickly create powerful cloud apps using a fully-managed platform? Get 10 web, mobile or API apps with Azure App Service with 1 GB storage

2.       Wish to build apps faster with serverless architecture? You can now send 1 million requests and get4,00,000 GBs of resource consumption with Azure Functions Service

3.       Are you looking for simplifying the deployment, management and operations of Kubernetes - an open-source system for automating deployment, scaling, and management of containerized applications to groups containers that make up an application into logical units – for easy management and discovery? Use Azure Container service to cluster virtual machines.

4.       Are you planning for Identity and Access Management on the Cloud for your organization? Store 50,000 objects with Azure Active Directory with Single Sign-On (SSO) for 10 apps per user.

5.       Do you want to try managing Identity and access of your customers? 50, 000 monthly stored users and 50,000 authentications per month with Azure Active Directory B2C

6.       You can build and operate always-on, scalable and distributed microservice apps using Azure Service Fabric

7.       Do you want to complement your IDE to share code, track work and ship software for any language – all in a single pack? List first 5 users free with Visual Studio Team Services

8.       Get actionable insights through application performance management and instant analytics - Unlimited nodes (server or platform-as-a-service instance) with Application Insights and 1 GB of telemetry data included per month

9.       You can quickly provision software product development and test environment for Linux and Windows applications at the Azure DevTest Labs and use it without limit

10.   Enterprises can use Machine learning with 100 modules and 1 hour per experiment with 10 GB included storage at the Azure Machine Learning Studio – just drag and drop to deploy a solution – no coding

11.   Capitalize on the free policy assessment and recommendations with Azure Security Center where you get unified security management and advanced threat protection across hybrid cloud workloads.

12.   Get unlimited personalized recommendations and Azure best practices with Azure Advisor

13.   Start connecting IoT assets, monitor and manage them at the Azure Iot Hub. The free edition includes 8,000 messages per day with 0.5 KB message meter size

14.   Start delivering high availability and network performance to your applications using the public load-balanced IP with Azure Load Balancer

15.   Integrate your data in a hybrid environment. You can now experiment with 5 low frequency activities with Azure Data Factory

16.   If you develop mobile and / or web apps, use this service to search the cloud 50 MB storage for 10,000 hosted documents with Azure Search including 3 indexes per service

17.   Get a free namespace and push 1 million notifications to any platform from any back end with Azure Notification Hubs

18.   Manage compute power without limit using Azure Batch for cloud-scale job scheduling and cluster management

19.   Automate your process and manage the cloud with a free 500 minute of job run time with Azure Automation

20.   Get more value from your data assets – include unlimited users and 5,000 catalog objects with Azure Data Catalog

21.   Detect human faces, compare similar ones and organize images – 30,000 transactions per month processing at 20 transactions per minute with Face API

22.   Convert 5,000 audio to text and vice versa transactions per month with Bing Speech API

23.   Easily conduct real-time text translation with a simple REST API call – free 2 million characters included for Translator Text API

24.   Transform your log data into actionable insights using this free 500 MB-per-day analysis plus 7-day retention period with Log Analytics

25.   Run 1 job, 5 jobs per collection and 3,600 job executions on simple or complex recurring schedules for free with Scheduler

26.   Get your first 50 private virtual networks free with Azure Virtual Network

27.   Unlimited inbound Inter-VNet data transfer

These services listed below are free for the first 12 months

1.       Deploy 1 or more Azure B1S General Purpose Virtual Machines for Microsoft Windows Server (1 core 1GB RAM, 2 GB SSD Disk space) and run them for 750 hours (aggregate)

2.       Deploy 1 or more Azure B1S General Purpose Virtual Machines for Linux (1 core 1GB RAM, 2 GB SSD Disk space) and run them for 750 hours (aggregate)

3.       Get 128 GB of Managed Disks (as a combination of two 64 GB (P6) SSD storage, plus 1 GB snapshot and 2 million I/O operations) for persistent secure disc storage for your VMs in Azure

4.       Get 5 GB of LRS-Hot Blob Storage – a massively scalable object storage for unstructured data - with 2 million read, 2 million write and 2 million write/list operations

5.       Get 5 GB of LRS File Storage – simple secure and fully managed files sharewith 2 million read, 2 million list and 2 million other file operations

6.       Deploy an SQL Database Standard S0 instance with 250 GB data and 10 database transaction units

7.       Deploy a globally distributed multi-model database service with Azure Cosmos DB to store 5 GB data with 400 reserved in units

8.       15 GB of bandwidth for outbound data transfer with free unlimited inbound transfer.

There is one service that is always free after first 12 months

1.       5 GB of bandwidth for outbound data transfer with free unlimited inbound transfer always free after first 12 months.

The Azure free account is available to all new customers of Azure. If you have never had an Azure free trial or have never been a paying Azure customer, you are eligible. You don’t have to pay anything at all at the start.

Please access the FAQ here for further details.

Digital Transformation - Sustaining the digital transformation

The challenge. Digital transformation is a journey with many predetermined milestones along the way for organizations to ensure that they stay on the intended path throughout, but the destination is not a well-defined spot. As technology changes are dynamic, unpredictable and quick, so is the digital destination. This might have to be redefined, moved further and prepared for further transformations dictated by newer disruptions that will arrive in future. It is essential that at least the foundational digital skills are laid strong enough for expansion and changes that would be required later in the transformation path.

The approach. Enterprises must orchestrate their skills build-up around this transformation. It is essential that the organization has enough people who grasp the idea, can contribute to the cause voluntarily or otherwise and involve actively in the concentrated efforts towards the desired result. While it is desirable that the existing management and workforce in entirety come on-board, many organizations might not have enough people who would share the same vision and willingly stay on the well-defined transformation path.   In such cases, businesses must look outside for resources that are already skilled in technology and operations that align well with the transformation vision. Hiring might have to start at the top which in turn might help in identifying the right talent in the middle and lower levels. Some innovation might be required in the recruitment strategy and enterprises might have to cast their net wider for rare skills.

Training must be an integral part of the agenda to increase the digital awareness organization-wide. This will result in bringing employees up to speed in specific digital technologies. Organizing employee exchange programs across functions and locations and introducing reverse mentoring initiatives might yield quicker results. Building an enterprise-wide knowledgebase with documents, videos and do it yourself kits for existing employees and new hires would help enterprises simplify the learning path quicken the path for the staff to contribute to the efforts and results. A centralized digital platform that is accessible easily by the employees for any kind of corporate information and a seamless communication system that can bring people and information closer would make a big difference than the traditional approach.

A well-defined reward system must also be in place for sustaining the transformation and the structure might have to extend beyond corporate boundaries. Enterprises must also make sure rewards are more than financial - social recognition and executive-level appreciation might be other alternatives.

Partnering with organizations that might yield a synergic effect to the digital vision is one other option to be seriously considered by organizations that don’t have the required skillsets and resources ready. Acquiring businesses that already have skilled resources that can contribute to the organizational vision is another strategy.

It is also essential to build a close relationship between internal IT and the business so that they work in sync towards the digital goals. Results need to be measured, monitored, reviewed, course-corrected and iterated periodically to retain the pace and steer the efforts in the right direction. IT solutions need to be designed and implemented for such activities. Managing the enterprise strategic score card and driving the initiative-level business case and related KPIs are essential for sustaining the transformation.

Digital Transformation - Mobilizing the organization

The challenge. Motivating the senior management and driving the digital transformation is one thing but mobilizing the whole organization and on-boarding them in the journey is another and the tougher challenge. The enterprise needs to send clear signals and through as many channels as possible. The objective is to motivate the lower level employees to enroll themselves in this endeavor with zero or little force. Redefined policies and modified work practices must be clearly defined and enforced, and participation must be encouraged and rewarded. The goals and results of the transformation need to be transparently defined, and benefits clearly conveyed to the entire organization such that every team and every individual will contribute to the cause. 

The approach. The appointment of a CDO, a digital challenge thrown by the CEO that has a measurable result by a certain cutoff date, or the visible branding of the transformation in a large way across the organization such as declaring a digital year are some of the activities that will send clear signal to the entire organization that the business is serious about this transformation effort. The leaders must lead from the front engaging in digital transformational activities themselves and encouraging the team around them to adopt the new policies and newly set procedures. The transformation should be co-created with the teams shouldering the responsibilities together with the management.

New behaviors need to be standardized but enterprises must also allow the digital culture to evolve organically across the organization.  Digital champions who can liaison between the management and the end users must be identified in every department and team, trained and encouraged to help people around them to adopt the digital culture. Quick digital wins must be rightly identified, advertised and rewarded so that the whole organization is motivated and mobilized around the transformation efforts. Enterprises must make visible changes to work practices and institutionalize them. Adoption of solutions for transformation must be encouraged rather than just deployment.

Digital Transformation - Focusing investment

The challenge. Enterprises that aspire to transform digitally must have a keen focus on the things that are important, commit real money to fund initiatives and keep moving everything in the same direction. They need a clear vision and an action plan that highlights some of the major landmarks on their digital transformation journey while taking into consideration improved customer experience, increased operational performance and adaptation of the business model as the dominant objectives.

This must also translate into strategic goals that will guide them in building a roadmap of initiatives. These goals should not just be about financials but also in terms of customer experience, operations and building organizational capabilities. A strategic scorecard incorporating all these goals would be the basic template and a point of reference for the entire team. Recognizing the entry point is crucial. Enterprises must engage practitioners and operational specialists early in the design stage to minimize the traditional vision-to-execution gap. Design must focus on business outcomes, not technology. This roadmap can become the canvas of the transformation, but it must make allowance for iterations and course correction as the journey contains many unknowns.

The approach. Gartner recommends enterprises take a dual mode – continuing the traditional on one side while experimenting on the transformational model on the other, however, in small measures; a series of sprints – not launching out into marathons.

Building the right governance model is another area of focus. Appointing a digital transformation owner – a Chief Digital Office - who would steer all the efforts in a common direction amid a broad cross-functional set of stakeholders would make a significant difference.  The CDO can decide on the coordination and sharing of resources, technology platform, talent and data that will be needed among teams involved. The CDO would encourage standardization but also simultaneously encourage innovation. Other governance mechanisms like steering committees, digital champions and shared digital units would also help. Such units centralized with shared infrastructure like unified customer database, enterprise wide platform, advanced analytics teams or innovation labs would drive synergy across the firm.

Building the right governance model is another area of focus. Appointing a digital transformation owner – a Chief Digital Office - who would steer all the efforts in a common direction amid a broad cross-functional set of stakeholders would make a significant difference.  The CDO can decide on the coordination and sharing of resources, technology platform, talent and data that will be needed among teams involved. The CDO would encourage standardization but also simultaneously encourage innovation. Other governance mechanisms like steering committees, digital champions and shared digital units would also help. Such units centralized with shared infrastructure like unified customer database, enterprise wide platform, advanced analytics teams or innovation labs would drive synergy across the firm.

Funding the transformation is the decider. As most of the digital transformation journey would be across uncharted territory a pragmatic approach is needed. Doing a diligent cost benefit analysis that incorporates digital skill building, organizational change, communication and training would reveal the cost of the journey versus the business benefits projected. Foundational investments for core systems, platforms and tools needed for the launch, maintenance investments that are RoI driven and early stage innovation investments must be considered during the analysis. As these investments are speculative and returns are highly variable enterprises must take a test and learn approach.

Digital Transformation - Framing the digital challenge

The challenge. Building awareness in the enterprise is the starting point to any digital transformation initiative. Transformation drivers from the top must put digital transformation at the top of the enterprise’s agenda. It is essential that people in the organization understand the scale and pace of the digital impact their industry is undergoing because of the disruptions brought in by the nexus of forces – Social media, mobility, information flow pattern and analytics and the Cloud. The creating awareness drive can gain momentum if this awareness process is made experiential.

The approach. It is essential to build a coalition of believers who can understand the potential impact of digital technologies on the business and the need and the urgency with which this transformation has to be pursued. Align the top team with this temperament will make things easier for the enterprise to drive this sentiment down to the last rung of employees whose participation would hasten the transformation.

Businesses need to identify potential threats and opportunities that they possess as an organization that will either contribute to or hinder the endeavor. Creating a risk profile containing the consequences that the business would have to encounter in the absence or slow pursue of this transformation journey would contribute to the overall support of the executives towards this strategic change in the business model. In most cases the CEO is responsible for roping in the senior leaders into the transformation team.

It is also essential that enterprises know their starting point in the journey. They need to measure how mature their digital competencies are, and which strategic asset will help them to excel amidst the competition.  Assessing the strategic assets of the enterprise and defining those that will be relevant in a digital world and those that won’t – physical assets, competencies, intangible assets and digital assets like data which in most cases would turn out to be a high value currency can set the framework for further acquisition, development or realigning of the resources.

It is also essential that enterprises know their starting point in the journey. They need to measure how mature their digital competencies are, and which strategic asset will help them to excel amidst the competition.  Assessing the strategic assets of the enterprise and defining those that will be relevant in a digital world and those that won’t – physical assets, competencies, intangible assets and digital assets like data which in most cases would turn out to be a high value currency can set the framework for further acquisition, development or realigning of the resources.

Enterprises should focus on their potential and the distance between where they are and their potential – not the distance between them and their competition. The executive sponsors who have grasped the reality of the digital transformation mandate would have to digitally challenge their current business model by craft the transformative vision and charting out the transformation journey. Unifying the company through a strong IT vision of the company’s digital future would be a sure step towards a successful digital transformation.

Digital Transformation - The strategy for transformation

The challenge. Digital Transformation is not a linear process. Many enterprises have already kickstarted a number of digital initiatives. Business compulsions and competition from outside might have been the factors for such initiatives but a coordinated and concerted effort to transform the enterprise from the traditional to a customer-centric organization requires much more than such disparate initiatives. Such a transformation would touch the vital organs of the business – Executives and employees, business processes and operations and even products and services.

Many business decision makers as well as technical influencers do not see the urgency to transform yet. The top management has settled down in the traditional style for too long and many decision makers are quite seniors in their field who had seen success in the traditional way that the mandate to transform gets little attention. It is an elephant in the room that nobody dares to deal with. They keep reacting to threats many of the times instead of shaping the future for their enterprise themselves.

The reality. Enterprises that aspire to transform use technology better than their competitors. They build not only digital capabilities but also leadership capabilities to stay in the right path during this journey so that the desired destination is reached with minimal obstructions. They may need to build skills in different areas and redirect their efforts from time to time. A joint research program between Capgemini Consulting and the MIT center for Digital Business recommends a comprehensive strategy for this transformation in enterprises.

Enterprises must first realize that the business scenario that they have been thriving on for so long is fast transforming into a digital oriented one. They must sense the real threat from outside in the form of startups and fast movers who are already enlightened on the role of digital to transform the way businesses are forced to go. The disruption created by digital technology is real and here to stay so that there is no choice but to jump into the bandwagon of digital transformation. They must do a deep dive into their existing capabilities and skills that would need course correction, development or rebuilding.

Enterprises must also focus on investment in the right areas for the digital transformation to be put on the right path so that it takes off and stays at the right pace. The whole organization needs to be mobilized towards the achieving of the pre-determined goals with the digital initiative from the top and percolating to the lower levels. Taking the organization together in the digital journey is essential and leadership skills are mandatory for this journey to be successful. While there might be various milestones in the way, enterprises should have a clearly defined destination in this journey. Some of the milestones might be reached sooner than the others but sustaining the transformation throughout and well beyond the perceived destination is essential as the business environment is set to see more disruptions than ever.

Digital Transformation - Uncover new revenue models

The challenge. There is a real undeniable threat by startups to the established businesses that have been ruling the market for centuries. The traditional business models are losing relevance today. Businesses can sell products that they don’t produce, can provide customers with services that they don’t directly offer, and many are moving away from selling products to selling product based services.

Customer preferences keep changing and the value they expect from goods and services that they consume is increasing. Technology is contributing hugely to this shift in customer sentiments. They want to connect to their brands through the smart devices that they hold in their pockets. They are willing to pay for the additional value they get from such systems that bring them closer to their service providers.

The reality. Thankfully, technology is contributing heavily in adding greater functionality to improve the worth of products and services that enterprises offer today. 50% of new business processes and systems will incorporate IoT in their products by 2020 and 25 billion connected “things” will be in use by then. There is also a change in the way employees look at this transformation in customer expectations. 41% of employees say mobile business apps are already changing the way they work. 40% of survey respondents say predictive analytics held the most potential to predict business events.

Enterprises can now create new revenue opportunities with intelligent technology that spans across systems. The Cloud offers larger avenues for innovation and holds the power to uncover possibilities that remained dormant all along. Data that once used be stored for compliance purposes are now being put to greater use through analysis and interactive visualization for decision makers to create new roadmaps for products and services like never before. Predictive analysis helps them look ahead and track market dynamics and change in customer sentiments.

Digitizing the enterprise can transform business practices and enable re-designing of business processes to stay ahead in the marketplace. Businesses need to re-invent products and services and deliver new market offerings that caters to customer needs.

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Digital Transformation - Using technology to drive efficiency

The challenge. 81% of medium businesses say that technology solutions could help improve business outcomes or run the business better. Meanwhile, technical decision makers agree unequivocally that there are two technology challenges that top the list – implementing new solutions or upgrades & containing technology costs. And both are seemingly at loggerheads. As the technology landscape keeps changing by the minute today, even newly installed solutions lose their relevance very quickly. Unfortunately, many conservative enterprises are still stuck with their decade old legacy systems. This results in compromised security, efficiency and productivity. Businesses reported a 34% increase in financial losses ($2.7M) due to security incidents even in 2014. Today, with the advent of sophisticated threats targeting enterprises, the impact is much higher.

The entry of the Cloud has brought a radical approach to this paradox. 87% of midmarket BDMs say Cloud solutions would help the business grow.

The reality. Enterprises can drive maximum business efficiencies with flexible, intelligent technology. They can capitalize on the IT investments already made for on-premises solutions by tweaking them to sync with the challenging requirements of the business. The cloud can come in as a less costly alternative when new needs arise in the system. Moving critical workloads to the cloud could also mean greater scalability at reduced costs. A hybrid setup can bring flexibility to the system by allowing administrators to weigh and choose the better option for their mushrooming needs.

Enterprises must realize that the greater the investment in digital the simpler it gets to manage and run the business. Less systems to maintain and more freedom to expand (or contract) brings in a desirable flexibility. Aligning IT with the business process with the help of a DevOps team would optimize business processes, enable more efficiency and faster decision making.

Going digital also ensures business continuity and improves security and protection against unexpected interruptions, data loss and modern security threats.

Optimizing operations involves digitizing corporate functions and developing a digital culture all around the enterprise.  A state of the art Enterprise Resource Planning mechanism is essential for minimizing wasted hours and financial resources. All efforts must be focused on maximizing the business benefits derived from IT Projects. Minimizing clutter in IT would lead to fewer systems to maintain and less cost to keep it running – a lean IT. Service operations need to be digitally streamlined to produce the greatest value by reducing response time and increasing agility.