AI adoption increases with Cloud Machine Learning as a service

The basic ingredient required for AI is a successful machine learning model. However, to create and run the model it is important to have the right capacity of infrastructure, followed by good domain knowledge and a large amount of data. A machine learning model is defined as a software entity created with algorithms and training data. The success of the model depends on getting the right training data with precisely tweaked algorithms. Running the model requires huge infrastructure with GPU / TPU CPU capacity and this makes most of the enterprises wary of investing in AI. The capex model for AI infrastructure calls for a very large investment, plus with the technology developing at a fast pace, enterprises will find it difficult to keep changing the infrastructure.

Essentially for Businesses to take advantage of AI, they must have a huge capacity investment. However, cloud comes to the rescue by offering infrastructure capacity with a lesser initial investment and pay as you use models. In the following blog let us look at the models offered by various cloud service providers for running machine learning as a service (MLaaS). We look at four major cloud service providers namely Amazon, Microsoft Azure, Google Cloud (GCP) and IBM Watson.

Amazon Machine Learning

SageMaker is an Amazon machine learning framework with built-in models with algorithms for classification, regression, multi-class classification, k-means clustering and so on. SageMaker helps to create the models quickly with advanced algorithms. Apart from this, Amazon also offers huge infrastructure on-demand as well as serverless-processing enabling the models to be run on the most optimized infrastructure. Amazon SageMaker also gives hook to Google Tools like Tensorflow, open-source Keras, Facebook Pytorch etc. A complete MLops (Equivalent of DevOps for Machine learning code) is offered by Amazon.

Azure Machine Learning Platform

Services from Azure Machine learning can be elaborated two-fold, Azure Machine Learning studio and Bot service. The graphical drag and drop machine language workflow creation ability is created by Azure using Azure ML Studio. This includes data-exploration, pre-processing, choosing methods, and validating modelling results.The main benefit of using Azure is the variety of algorithms available to play with. The Studio supports around 100 methods that address classification(binary multiclass), anomaly detectionregression, recommendation, and text analysis. It is worth mentioning that the platform has one clustering algorithm (K-means).

Azure serves different kinds of customers. Namely data Scientists, Data engineers, data analysts and so on. Azure’s approach is to provide an end-to-end platform for all types of customers and the product includes model management tools, python packages and workbench tools.

Google Machine Learning Services

Google being an AI-first company offers a variety of AI tools for the developers, enterprise operations, data scientists etc. Google recently started AutoML which requires no programming to develop a Machine Learning model. Google has been a great contributor to open-sources. Most recently they introduced Google BERT, TensorFlow, AutoML etc. Among all the service providers Google has done the maximum contribution to open-source and this, in turn, has improved the adoption of Google Tools. Today TensorFlow is the most widely used development tool amongst the developers. It has different libraries available from multiple open sources making it one of the more popular developing applications on the cloud. Google Cloud also offers a high-end computing environment with TPU processors along with robust data security making it one of the most versatile platforms for development and deployment. Many cloud-native deployments are possible in Google Cloud Platform.

IBM Watson

IBM Watson one of the earliest and very widely used machine learning platforms, has been in existence for some time. It offers a set of services for newcomers as well as experienced service providers. Separately, IBM offers deep neural network training workflow with flow editor interface similar to the one used in Azure ML Studio.

Machine Learning Services offered by the cloud providers.

  1. Speech and text service Translation service
  2. Image classification
  3. Text classification
  4. Speech classification
  5. Facial detection
  6. Facial analysis
  7. Celebrity recognition
  8. Written text recognition
  9. Video Analysis etc

In conclusion, many of the cloud service providers have recognized the fact that business transformation can be brought about by the use of AI technology and provide machine learning as a service so that enterprises can use the readily available models. This helps them in the areas of prediction, personalization, natural language processing, optimization, and anomaly detection. Businesses want a competitive edge and AI plays a key role. And to enable AI quickly, cloud is the way to go.

Cloud Optimization – The Necessary Conundrum

The cloud migration services market was valued at USD 119.13 billion in 2019 and is expected to reach USD 448.34 billion by 2025, at a CAGR of 28.89% over the forecast period 2020 – 2025. Over the past decade, cloud computing adoption is rising, owing to increasing investments from small and medium enterprises. About 21 billion dollars are wasted per year on the cloud spend. There are three kinds of enterprises approach to the cloud – some enterprises are completely public cloud-only companies, some are only private while others use a combination of Private and Public cloud called the hybrid cloud. There is a world of difference between datacentre cost optimization and cloud optimization. Cloud Optimization is a lever that needs to be utilized by the CFOs to make sure optimal-utilization is achieved. Back in the years of data centre, the budget is predetermined. However, the public cloud is something you pay for to use and therefore gives leverage to control the cost.

1. Visibility to the IT Environment
Unlike data centre which is a onetime budgeting exercise at the start of the finance year cost optimization in the cloud is a continuous exercise. This continuous exercise gives the opportunity to reduce the IT costs. The first requirement is a tool that gives complete visibility to the IT resources usage on a continuous basis and their impacts on the billing. All cloud service providers give this visibility and to that extent, the usage of IT resources can be tracked. Real-time spend visualization is a must.

2. Migration with ROI
When you perform the migration from on-premises to data centre have a framework for an ROI-based on the current IT system usage and future business requirements. While cloud avoids the capex costs, the operational costs should be optimized for the best ROI possible. All cloud service providers have different sets of products. The objective is to map every task in the datacentre to be done on cloud but the way we do the task can possibly change, though the outcome has to be the same.

3. Look for Idle resources in your cloud.
Cloud has multiple resources like server instances, idle load balancers, idle containers and so on. Turn off anything that is not utilized. Unused instances make a big portion of the cost for the cloud. Ideally, all cloud service providers give a resource utilization report and any resource not utilized should be turned off.

4. Try and use serverless resources.
Unlike data centres, many applications do not require exclusive resources specifically CPU and RAM. All cloud service providers give serverless options. This avoids provisioning time, and the utilization time is only for the period the program uses the serverless infrastructure. AWS gives lambda and GCP gives cloud which helps in running long-running CRON jobs.

5. Reserve capacity usage
Many cloud service providers give deep discounts when we buy instances after planning the capacity. They are known by different names like reserved instances, in AWS. Avoid on-demand buying of instances as it will cost much higher, and plan for Reserved instances. Unutilized reserved instances is another reason why the spend going up on the cloud.

6. Understand the discount policy of each cloud service provider.
Every cloud service provider is different, and two instances are equal. Understand the discount policy of each service provider. As an example, GCP gives Deep discounts on sustained usage of VM. The more we use the more discount we get from GCP.

7. Look for Product Specific Free Tier Usage
GCP has 90 products approximately and AWS has 169 products. It is difficult to understand each product. Free tiers associated with the product of a cloud service provider should be utilized completely. Utilize the product-specific free tier usage.

8. Use DevOps and Automation
If you are managing a large infrastructure, the DevOps tools chain must be used to maximize the operational efficiency and reduce the usage of resources. DevOps culture should be brought in with cloud migration to take maximum advantage of Cloud resources.

9. Cloud-Native application development
Development organizations need to switch over to cloud-native development to take advantage of the flexibility the cloud offers for containers and container-deployments. Usage of containers can further optimize the license costs and migration costs.

10. Use Multiple Cloud Service Providers
Avoid vendor lock-in with Cloud service providers. Have a minimum of two vendors and ensure that the applications are written to migrate the workload from one vendor to another vendor. GCP offers live migration. Use the best of the multiple worlds of the cloud service provider to do a better managing of the costs.

To Conclude cloud cost management is an engineering problem and it is not a finance or operations problem. A strong engineering team as part of the operations will enable more savings for cloud operations.

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Cloud DevOps: Ensuring Business, Tech and Security go hand in Hand

DevOps is a new area where both the Development and Operations are intertwined together as a single organization. Cloud DevOps is a newer development area, the need for which had arisen for agile development, automated deployment, as well as for faster time to scale. DevOps on premises is different from Cloud DevOps as Cloud DevOps require both cloud-expertise as well as DevOps knowledge to master the development of the same. DevOps Practices in different clouds are different and holds great promise if the awareness to handle the DevOps in the cloud is there.

Requirements of Cloud DevOps

Cloud Expertise: Cloud is still considered a new technology although the cloud concept has been there for more than a decade. The tools required for DevOps from Agile tracking of development, Continuous integration with new builds, Continuous delivery of code to production, and Site Reliability Engineering consisting of monitoring the availability, performance, and fault management of Infra and applications, are different for different cloud service providers. A cloud DevOps engineer has the knowledge of complete cloud DevOps Tools chain specifically optimized to the cloud service provider.

Cloud Costing Model: Awareness of the cloud costing model is a must. The number of products by a cloud services provider is daunting. As an example, AWS has 169 products whereas GCP has 90 products. Many costs are hidden in nature and many of them must be discovered on the way. Therefore, right experts are necessary to make sure the cloud costs are optimized to the best of the ability.

Scaling: One of the facets of DevOps is automation and requirement for automation is varies according to cloud service providers. As an example, with AWS lot of third-party service providers are available to automate the operations whereas in GCP many operations are automated by default. Standardization and automation are necessary to scale the operations. Cloud-native development has become the order of the day and many open-source tools are used to scale the deployment speed. DevOps as code should be used to scale the pipelines.

Security and Compliance: Code Security is still an important aspect of developing the code on the cloud. Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST) are necessary in the cloud. Security and compliance scaling happens more with automation. SAST check should be automatically done with every code check-in and DAST check should automatically be done with every build. Security is a continuous service and public cloud service providers are enabling DevSecOps as a new practice. Application security level checks are now reaching new levels which many security professionals have been asking for as well. The goal of the DevSecOps Practice is to introduce security earlier in the SDLC lifecycle. The Objective of the DevSecOps is to make business, tech, and security work together.

AI in DevOps Chain: DevOps throws a lot of data and it is important to have complete visibility of the entire DevOps chain. One can use the Data with AIOPS and get important inferences for actionable intelligence. Data on DevOps is important to optimize the complete process. A new approach of combining DevOps with AIOPs is being done by public cloud service providers. Many of the AI applications require DevOps by default as well. AI is more iterative. While AI can help with DevOps data the DevOps practice in AI can help with more actionable intelligence in anomaly detection, prediction, and natural language processing. All AI applications will have DevOps approach. Cloud offers AI ML tools and can be used as part of the DevOps tools chain for optimization.


While DevOps practice itself has delivered faster productivity with enterprises setting up CI and CD chain it is important to understand the cloud DevOps chain and use it effectively for business purposes. The migration from On-prem DevOps to Cloud DevOps should be carefully calibrated for maximum benefits at minimal cost.

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Cloud Based Mobility IT Solutions: Benefits

The term “mobility cloud computing” is used for the entire gamut of storage and processing of data remotely from a device, while the user can access their data uninterrupted and process it seamlessly through any given device, securely.
As in case of mobile device and application landscape, the mobile management ecosystem is constantly evolving. There are various components of mobility management, including mobile device management, mobile app management and mobile security.
Earlier, in our previous blog we discussed the issues related to cloud mobility implementation and management. Although the process of implementation needs extreme expertise and can be challenging, however Cloud based mobility management has its own benefits that cannot be ignored. Let’s briefly discuss some of the important ones.
Benefits of Cloud based Mobility Solutions
There are multiple vendors and numerous deployment mobility devices – including on-premise, full cloud and hybrid. The dynamics of the components included in any solutions differ from one vendor to another.
Rapid deployment:  It takes quite a long phase for a business to roll out Mobile Device Management (MDM) system. However, cloud-based solutions can be activated in a day which empowers an enterprise to rapidly deploy policies and control access with a mere click for configuration and provisioning.
Flexible expense management: In majority of cloud-based models the payment mode is predictable, and service based, and you scale at your pace aligning to business requirements. Whether the business requirement is for 500 or 1000 employees at any given time, it takes the same amount of IT resources and can be achieved in the same time frame.
Cost-effective for Businesses: Employees and users share application and resources without any huge investment on a software and hardware that enables enterprises to have least expenditure using cloud computing tools. The technical setup and operational resource allocation are minimal that results in optimum price structure, quick and simple.
Device diversity:  Today, businesses support RIM’s blackberry operating system and also have Apple’s iOS devices. In addition, there are Android and Windows based phones. A cloud-based management solution is a one-stop solution that supports all the operating systems.
One console for all: In extension to device diversity ecosystem, while you support multiple operating systems, you will have many consoles. You can get an integrated console view through an on-premise MDM solution but only when you roll out the solution. With cloud mobility management, you establish the capability to support iOS-based devices on one console and use that same console to support Android devices as well.
Almost Zero-day updates: Operating system landscape evolves rapidly. Each time there is a new version of operating system rolled out, you have the painstaking process of updating your mobility management solution. As a result, your IT team lags for weeks in supporting the newest releases of an OS. As opposed to the cloud-based providers who have the service updated almost instantly and the effort is minimal.
The cloud is constantly evolving and so has been the way mobile applications are developed and used within the companies. Though the marriage of mobility and the cloud is like a match made in heaven for disparate teams to collaborate and access business-critical applications unanimously and virtually from any corner of the globe 24/7. Still, IT leaders have the existing challenges to be allayed before they turn cloud and mobility into a “happening couple” to drive business innovation and growth.
CMS IT Services, with its wide expertise and experience brings together tailored and enterprise-based cloud mobility solutions that are robust, agile and completely meant for next generation employees to counter the cloud and mobility challenges and exploit the several benefits for a future ready business IT infrastructure.
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Cloud Mobility: Key Issues

In the past few years, Telecom sector has been jubilant over the groundbreaking sales of smartphones and tablets. The digital space and internet world are brim, with numerous compelling devices being added every day and the number doesn’t seem to stop anytime soon. There is an exponential surge in consumerization of IT – Bring your own Device (BYOD) being the top in the list.
On one hand if there is a boom in smartphone and tablets market, on the other side companies grapple with the challenge of supporting and managing such a gamut of devices owned by businesses and employees.
The intersection of cloud and mobility has created trends of explosive growth, but they also come with key issues and IT pressure. Let’s discuss some of the impending grey areas.
Challenges of Cloud & Mobility
Data security & Network capabilities: Your IT environment needs to be robust enough and sufficiently developed to allow seamless transfer of applications to a hosted model or software-as-a-service (SaaS) solution where the application itself resides in the cloud. With growing budget of corporate software development for mobile apps, issues of scaling and management of growing demand also arise, especially with the routine increase in density of mobile users accessing cloud-based data and applications.
Data Security of Mobile devices: Although cloud platforms are secured with SSL and digital certificates yet data security for mobile devices remains to be looming – more importantly it stems from when people lose their devices which happens often. Similarly, managing the data integrity is a pressing issue when users sync their devices with the cloud. If one of your resource is updating a document and doesn’t sync the latest back to the cloud, other users will be stuck with the older version.
Multi-platform access: To provide multi-platform access to your users when operating within private cloud networks is a huge challenge since the private cloud architecture is very complex.
Updating Security policies: With evolving cloud ecosystem and mobile apps the security policies too need to be constantly updated which will be a proverbial task in progress. As per an IDC cloud survey of nearly 1700 technology decision makers, “Concerns about the security of various cloud computing solutions and the risk of unauthorized access as well as concerns over data integrity protection is ranked No. 1”.
Irrespective of the company policies people have the tendency of using their mobile devices both for official and personal purposes. One of the options for remote management of mobile devices is offering tools like encryption and passwords to create “enterprise sandboxes” that segregates the personal and corporate data conveniently.
BYOD prepared infrastructure: Another daunting problem that IT providers come across is the preparedness of their IT infrastructure for an enterprise BYOD policy to make it possible for data to be transmitted and accessed easily from a range of mobile devices with various operating systems.
Collaboration of access: Accessing the cloud via mobile devices can become a big problem for collaboration. Several mobile platforms are not supported by sophisticated document editing tools. In addition, there are a very few options for multi-party video conferencing while using document sharing option over the cloud.
Network Infrastructure: Network infrastructure need to be continuously upgraded, should be latest and strong to maintain consistence in connectivity, else the cloud app will be rendered completely inefficient and useless.
This problem can be handled by using the HTML5 that enables data caching that further empowers the mobile cloud application to function normally and continuously even during an outage.
Mobile Cloud Computing is a hybrid model that is a mix of Mobile devices accessing the services remotely available on the cloud. Many organizations are still in its initial stage of implementation and getting a grip of exploiting the benefits of it. In tow of these issues the benefits of cloud mobility can be impeded which needs to be addressed for optimized cloud-based mobility infrastructure.
We will discuss the benefits of cloud-based mobility solutions in our next blog.
Please post your thoughts in the comments section.