Tag Archives: AI

Posted on Author Ramkumar Balasubramanian Categories Cloud

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 detection, regression, 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 Architect

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Posted on Author Ramkumar Balasubramanian Categories Cloud

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.

Conclusion

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.

Cloud Architect

Data References:

https://www.reportsanddata.com/report-detail/devops-market
https://dzone.com/articles/devops-trends-to-watch-for-in-2020

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Posted on Author Ramkumar Balasubramanian Categories IT

Serverless – The New Option Of Reducing The IT Infrastructure Cost

The word serverless does not mean applications can run without a server. Every application requires CPU, Memory to run the program which is a process in execution. However, Serverless enables applications to share the resource’s availability in an optimal manner. Serverless imply applications that are written in a stateless container, ephemeral and managed by a third party. The Serverless was first started by the AWS in 2014 by the launch of AWS Lambda. There are three aspects to the serverless namely application/services. Infrastructure and architecture. Let us look at all the aspects of the serverless.

Why Serverless?

There are three fundamental reasons to go serverless as listed below.

1. Lower Operational Cost: This means fewer servers, fewer people to manage servers and there is a division of labour.

2. Faster time to Value: Usually applications or services require servers to be provisioned. With serverless, there are zero applications to be provisioned.

3. Focus on core value: Serverless means outsourcing our architecture and focusing on the core value.

Perspectives of Serverless:

1. Application/services perspective: Serverless is lightweight event-based microservices like Google functions. Google cloud functions are light weight event-based response functions that allow a small single-purpose function that allows a lightweight response without needing a server to be managed at any given point in time. Effectively any lightweight function that is not dependent on a server can be run on a serverless architecture.

2. Infrastructure for Serverless: The infrastructure for serverless is totally managed by the vendor. Like AWS lambda enables the serverless infrastructure. Scaling is done automatically, and it is triggered by events.

3. Architecture: The architecture is usually stateless function; event-driven and uses API gateway to as an input to get triggered. An example of a stateless function in a website is the addition of an item to a cart.

 

Serverless Offerings

Serverless offerings are being done both by the public cloud service providers and private cloud service providers. AWS offers Lambda service for serverless mode. AWS lambda is very popular, and the shift has happened to AWS cloud lambda based on the fit for purpose. Not every service can run on serverless but whatever is doing only focused on a single purpose and uses independently the Compute power then serverless becomes an option to be used. Like AWS, Microsoft Azure offers serverless compute as well. Google cloud provides cloud serverless to deploy and develop APIs in the form of Microservices. Serverless provides a new way of running an application as a FaaS. (Function as a Service).

 

Disadvantages of Serverless

1. Cold Starts: Sometimes cold starts take quite a lot of time say anywhere from 200ms-600ms.

2. Parallel Requests: Parallel requests are not allowed inside the code. Parallelism is an issue.

3. Coding Language: Need the language to support application development. Node.js supports the Serverless architecture and not python. It is best suited for background jobs, API calls, batch jobs etc.

4. Hidden costs: The right job must use the serverless as some of the cloud service providers charge based on the no of requests/usage of API gateway though the cost of the CPU. So, RAM will be less as the cost is being shared.

5. Code Maintenance: This is higher on serverless architecture.
The transformation to Serverless is worth doing considering the fact it leads to the huge cost savings available due to shared CPU and RAM cost. At the same time, the right application must be chosen to run the serverless.

Cloud Architect

Data References:

https://www.marketsandmarkets.com/Market-Reports/serverless-architecture-market-64917099.html?gclid=Cj0KCQiA9P__BRC0ARIsAEZ6irhNcChPUksWfnmIYk6WXLCMKRIoGMPwHCMXzI04DnXZSBdDIWDj-8kaAmmWEALw_wcB

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Posted on Author Kunal Bhatt Categories Digital

Next Generation Cloud Adoption: Distributed Cloud

Cloud Computing is an evolving discipline. Newer innovations in cloud management are coming into fruition as we speak. What started out as a ‘High-Availability Storage Space’ is now integrated into every function of business. The Cloud opens possibilities for customers to gain benefits and be agile with their workloads. By shifting to cloud they leverage the economics offered by cloud like elasticity, pace-of-innovation, better uptimes and much more, from cloud-based scheduling, cloud-based applications to cloud-based Data-backup and DR. Practically everything has to come prefixed with ‘Cloud-based’ to ensure BAU continues uninterruptedly. However, there is still a pinch of resistance and hesitation seen in organizations when deciding to go for a public cloud model, entirely.

Some prefer private cloud or to an extent are willing to adopt hybrid cloud. Private cloud, is designed in a way that they are, owned and controlled by the customer and operated by the service provider’s teams or the customer’s own technology team and in the hybrid cloud, the public cloud provider manages their set of cloud offerings.

Hybrid Cloud was introduced to further the ‘best of both worlds objective’ for businesses that were not keen on completely abandoning their Legacy Systems in favour of a fully Cloud-based IT Infrastructure. It provided a sort of ‘safety net’ whose requirement was triggered mostly by data security concerns. Distributed Cloud does all this and more.

Distributed Cloud is Cloud-based Technology’s newest offering. Gartner identified Distributed Cloud as one of the top 10 trends of 2020 and the hype around it does not seem to be slowing down and will seemingly continue well into 2021 as well, by the look of things. Distributed Cloud basically leverages Public Cloud to interconnect IT Infrastructure irrespective of Physical/Geographical Location.

Gartner describes Distributed Cloud as “the distribution of public cloud services to different physical locations, while the operation, governance, updates and evolution of the services are the responsibility of the originating public cloud provider.”

Let’s consider the scenario where a business maintains some data on-site, some on private/public cloud and others on edge environments. Maintaining all these complex IT environments require overhead and maintenance to some degree. There is also the issue of all these being physically apart. Not to mention delay/latency concerns. What a Distributed Cloud Arrangement brings to the table is the ability to extend Public Cloud Capabilities to these complex systems and manage all of a business’s spread-out IT Infrastructure.

Cloud-computing involving Distributed Cloud utilizes so-called ‘substations’ as coined by Gartner. These tactically located substations act as a shared cloud pseudo-availability zones with networking, computing and storage capabilities.

Hybrid Digital Infrastructure Management vs Distributed Cloud

In a way Distributed Cloud Management makes up for everything HDIM falls short of. This type of cloud management does not rely on a unified approach to IT Infrastructure Management. It rather focuses on usage-consistency, customization and most importantly governance.

Firstly, Distributed Cloud raises the bar in terms of networking capabilities of IT Infrastructure Clusters. Inter-communication amongst IT clusters whether it is based on-premises and on Public platforms or Edge environments, is a striking feature of Distributed Cloud. This ensures users will have consistency across the board while utilizing the IT Infrastructure. DC also dissipates chances of network failure owing to the presence of sub-stations. This was not possible in a hybrid cloud arrangement.

This uniformity in usage does not hinder customization in Distributed Cloud Systems. Personalization based on the pertinent requirements of a particular location is possible while using distributed cloud. This drives value for the customer as well as the system administrator.

Dev Ops efficiency while deploying high-value services is also augmented by Distributed Cloud. It gives freedom of choice to users when it comes to deciding their preferred cloud clusters/locations. Integrating with Public cloud features allows Distributed Cloud to have the ability to implement innovations like AI/ML based automation capabilities to all IT environments.

Source: O’Reilly- Cloud Adoption in 2020

Another key characteristic of Distributed cloud is its ease-of-governance. If any new policy is introduced at the on-site level, it will be reflected on all cloud-based and edge systems as well. Data security is thus maintainable across the whole IT Infrastructure. This ensures the same level of security at all IT environments regardless of whether it is Cloud-based or on-site. This obliterates the security concerns posed by Hybrid Cloud.

Unifying Public Cloud and IT Infrastructure

To say it in the simplest of terms, Distributed Cloud can bring the unique competencies offered by Public Cloud to IT Infrastructure and make the experience of using cloud-based and non-cloud-based infrastructure less challenging, not to mention the reduction in cost. All this drastically reduces delays to service-delivery and makes the customer-business interaction a delightful encounter.

Source: IDC 2020

But with the unification comes issues like trouble-shooting complexities due to increased chances of interaction between cloud and on-site environments. Replicated data at all these environments also have to be kept track of and secured. So, although it is the same level of security across all platforms, the intricacies regarding the same may increase. Another factor to consider is the cost of deployment. Although operational costs may drop, the resources required to deploy such distributed systems may shoot.

Is this truly ‘The Best of Both Worlds’?

HDIM is constantly described as such but distributed cloud systems may be the new ‘the best of both worlds’ scenario that will see more adoption-rates with businesses requiring more customized offerings that do not compromise on security. But Distributed Cloud is not as ‘tried and tested’ as HDIM and may only look good on paper. that may depreciate ROI, as mentioned earlier. Only time will tell. But once perfected Distributed Cloud Systems are projected to be the future of cloud-based IT Infrastructure management.

Head, Automation Practice

Data References:

https://www.oreilly.com/radar/cloud-adoption-in-2020/

https://www.idc.com/getdoc.jsp?containerId=US46796120/

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Posted on Author Kunal Bhatt Categories Automation

Ensure Customer Delight: Automate your Customer-Service Lines with Conversational AI

Providing amazing customer service is one of the primary goals of any business. In the current scenario AI Chatbots are turning out to be the safest way to interact with customers. Chatbots allow us to eliminate a fair share of the ‘human’ factor from the interaction chain. Especially for answering standard queries and sharing general or specific information that customers might require. Whether a customer is retained or not is highly dependent on how smooth their initial interaction with the company is and how well they are treated during this crucial step.

Chatbots work along the same lines as a human in terms of communicating with prospects, but it is more apt to say the chatbot works practically the way it is programmed to. Chatbots powered by Artificial Intelligence, do not rely on written process material or videos, it learns from real-time scenarios or the archives of earlier conversations. The way chatbots can stimulate the conversation and keep customers engaged has made them a promising option in the market.

Companies are drawn towards digital services and/or digital platforms today but the apprehensiveness over the loss of existing or potential customers due to unsatisfactory service remains. While most enterprises have more or less accepted the Chatbot trend and are providing a complete digital experience for their customers, some still prefer a human touch or intervention to the transaction.

At times, the communication channel of the chatbot may hit a dead end, and to avoid such situations the most important thing is to have common and critical intents fed to the Bot. But complications could still pop up post ironing all teething issues. The AI Chatbot may be functioning properly but may fail in considering the limits and be unable to deliver information at its best level of proficiency during a customer conversation. At such critical juncture, it is important for a human to take over the conversation to diffuse the situation and avoid the user from taking away a negative experience from the interaction. To avoid such stray experiences, it is necessary to set the communication patterns and expectations, so that users are not let down due to restricted knowledge or competence during communication.

Organizations willing to invest in chatbots need to choose the right platform and have the BRD (Business Requirement Document) ready in hand. They also need to invest time in designing the intents that are recognised by humans and bots. It is also advisable to use a human-agent-based bot, with clear indicators for the agent to understand and pick up the conversation from the bot, to ensure a seamless transition of the customer’s query.

 

AI Chatbots can efficiently tap into their understanding of automated workflows and knowledge management to ensure the user has a delightful experience where they had a faster resolution of their concerns assisted by technology, in the right way and at the right time. Chatbots have self-service/self-help features that lead users to pointed solutions, web pages and documents instead of deviating them and wasting time. They can also raise a service request ticket on behalf of the customer with all the communication history for the agent to act precisely. They can even book conference rooms and suggest travel itineraries.

So Customer engagement via AI-driven Chatbots is a trend that is here to stay. AI Chatbots are programmed and well-equipped to deal with customers and respond to their queries without human interference. This not only increases efficiency but makes great strides in reducing the workload of customer support teams and at the same time ensures a smooth transition for the customer.

Embrace AI-powered customer acquisition, support and engagement.

Drive business continuity and growth

Head, Automation Practice

Data References:

Top 12 Benefits of Chatbots: Comprehensive Guide [2020 update], MAY 12, 2020 by AI Multiple based on Drift’s 2018 State of Chatbots Report.

https://research.aimultiple.com/chatbot-benefits/

What Is a Chatbot and How Is It Changing Customer Experience? APRIL 25, 2019 by Salesforce Blog.

https://www.salesforce.com/blog/2019/04/what-is-a-chatbot.html

Chatbot Report 2019: Global Trends and Analysis, APRIL 19, 2019 by ChatBot Magazine

https://chatbotsmagazine.com/chatbot-report-2019-global-trends-and-analysis-a487afec05b

4 Evolving Technologies That Are Empowering Chatbots by AITHORITY, JAN 16, 2020

https://www.aithority.com/guest-authors/4-evolving-technologies-that-are-empowering-chatbots/

Two-out-of-three Americans interact with AI chatbots, but we still prefer humans by ZDNET, MAY 18, 2019

https://www.zdnet.com/article/two-out-of-three-americans-interact-with-ai-chatbots-but-we-still-prefer-humans/

Conversational AI Statistics: NLP Chatbots in 2020 by Landbot.io FEB 21,2020

https://landbot.io/blog/conversational-ai-statistics/

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