Reduce Test Times and Increase Coverage with AI & ML

Kevin Surace

Chairman & CTO, Appvance.ai

With the need for frequent builds—often many times in a day—QEs can only keep pace through AI-led testing. It is the modern approach that allows quality engineers to create scripts and run tests autonomously to find bugs and provide diagnostic data to get to the root cause.

AI-driven testing means different things to different QA engineers. Some see it as using AI for identifying objects or helping create script-less testing; some consider it as autonomous generation of scripts while others would think in terms of leveraging system data to create scripts which mimic real user activity.

Our research shows that teams who are able to implement what they can in scripts and manual testing have, on average, less than 15% code, page, action, and likely user flow coverage. In essence, even if you have 100% code coverage, you are likely testing less than 15% of what users will do. That in itself is a serious issue.

Starting in 2012, Appvance set out to rethink the concept of QA automation. Today our AIQ Technology combines tens of thousands of hours of test automation machine learning with the deep domain knowledge, the essential business rules, each QE specialist knows about their application. We create an autonomous expert system that spawns multiple instances of itself that swarm over the application testing at the UX and at the API-levels. Along the way these Intelligences write the scripts, hundreds, and thousands of them, that describes their individual journeys through the application.

And why would we need to generate so many tests fully autonomously. Because applications today are 10X the size they were just ten years ago. But your QE team doesn’t have 10X the number of test automation engineers. And because you have 10X less time to do the work than 10 years ago. Just to keep pace with the dev team requires each quality engineer to be 100X more productive than they were 10 years ago.

Something had to change; that something is AI.

AI-testing in two steps

We leveraged AI and witnessed over 90% reduction in human effort to find the same bugs. So how does this work?

It’s really a two-stage process.

First, leveraging key AI capabilities in TestDesigner, Appvance’s codeless test creation system, we make it possible to write scripts faster, identify more resilient accessors, and substantially reduce maintenance of scripts.

With AI alongside you as you implement an automated test case, you get a technology that suggests the most stable accessors and constantly improves and refines them. It also creates “fallback accessors” when tests run and hit an accessor change enabling the script to continue even though changes have been made to the application. And finally, the AI can self-heal scripts which must and update them with new accessors without human assistance. These AI-based, built-in technologies give you the most stable scripts every time with the most robust accessor methodologies and self-healing. Nothing else comes close.

The final two points above deal with autonomous generation of tests. To beat the queue and crush it, you have to get a heavy lift for finding bugs. And as we have learnt, go far beyond the use cases that a business analyst listed. Job one is to find bugs and prioritize them, leveraging AI to generate tests autonomously.

Appvance’s patented AI engine has already been trained with millions of actions. You will teach it the business rules of your application (machine learning). It will then create real user flows, take every possible action, discover every page, fill out every form, get to every state, and validate the most critical outcomes just as you trained it to do. It does all this without writing or recording a single script. We call this is ‘blueprinting’ an application. We do this at every new build. Multiple instances of the AI will spin up, each selecting a unique path through the application, typically finding 1000s or more flows in a matter of minutes. When complete, the AI hands you the results including bugs, all the diagnostic data to help find the root cause, and the reusable test-scripts to repeat the bug. A further turn of the crank can refine these scripts into exact replicas of what production users are doing and apply them to the new build. Any modern approach to continuous testing needs to leverage AI in both helping QA engineers create scripts as well as autonomously create tests so that both parts work together to find bugs and provide data to get to the root cause. That AI driven future is available today from Appvance.

About the Author –

Kevin Surace is a highly lauded entrepreneur and innovator. He’s been awarded 93 worldwide patents, and was Inc. Magazine Entrepreneur of the Year, CNBC Innovator of the Decade, a Davos World Economic Forum Tech Pioneer, and inducted into the RIT Innovation Hall of Fame. Kevin has held leadership roles with Serious Energy, Perfect Commerce, CommerceNet and General Magic and is credited with pioneering work on AI virtual assistants, smartphones, QuietRock and the Empire State Building windows energy retrofit.

Artificial Intelligence in Healthcare

Dr. Ramjan Shaik

Scientific progress is about many small advancements and occasional big leaps. Medicine is no exception. In a time of rapid healthcare transformation, health organizations must quickly adapt to evolving technologies, regulations, and consumer demands. Since the inception of electronic health record (EHR) systems, volumes of patient data have been collected, creating an atmosphere suitable for translating data into actionable intelligence. The growing field of artificial intelligence (AI) has created new technology that can handle large data sets, solving complex problems that previously required human intelligence. AI integrates these data sources to develop new insights on individual health and public health.

Highly valuable information can sometimes get lost amongst trillions of data points, costing the industry around $100 billion a year. Providers must ensure that patient privacy is protected, and consider ways to find a balance between costs and potential benefits. The continued emphasis on cost, quality, and care outcomes will perpetuate the advancement of AI technology to realize additional adoption and value across healthcare. Although most organizations utilize structured data for analysis, valuable patient information is often “trapped” in an unstructured format. This type of data includes physician and patient notes, e-mails, and audio voice dictations. Unstructured data is frequently richer and more multifaceted. It may be more difficult to navigate, but unstructured data can lead to a plethora of new insights. Using AI to convert unstructured data to structured data enables healthcare providers to leverage automation and technology to enhance processes, reduce the staff required to monitor patients while filling gaps in healthcare labor shortages, lower operational costs, improve patient care, and monitor the AI system for challenges.

AI is playing a significant role in medical imaging and clinical practice. Providers and healthcare organizations have recognized the importance of AI and are tapping into intelligence tools. Growth in the AI health market is expected to reach $6.6 billion by 2021 and to exceed $10 billion by 2024.  AI offers the industry incredible potential to learn from past encounters and make better decisions in the future. Algorithms could standardize tests, prescriptions, and even procedures across the healthcare system, being kept up-to-date with the latest guidelines in the same way a phone’s operating system updates itself from time to time.

There are three main areas where AI efforts are being invested in the healthcare sector.

  • Engagement – This involves improvising on how patients interact with healthcare providers and systems.
  • Digitization – AI and other digital tools are expected to make operations more seamless and cost-effective.
  • Diagnostics – By using products and services that use AI algorithms diagnosis and patient care can be improved.

AI will be most beneficial in three other areas namely physician’s clinical judgment and diagnosis, AI-assisted robotic surgery, and virtual nursing assistants.

Following are some of the scenarios where AI makes a significant impact in healthcare:

  • AI can be utilized to provide personalized and interactive healthcare, including anytime face-to-face appointments with doctors. AI-powered chatbots can be powered with technology to review the patient symptoms and recommend whether a virtual consultation or a face-to-face visit with a healthcare professional is necessary.
  • AI can enhance the efficiency of hospitals and clinics in managing patient data, clinical history, and payment information by using predictive analytics. Hospitals are using AI to gather information on trillions of administrative and health record data points to streamline the patient experience. This collaboration of AI and data helps hospitals/clinics to personalize healthcare plans on an individual basis.
  • A taskforce augmented with artificial intelligence can quickly prioritize hospital activity for the benefit of all patients. Such projects can improve hospital admission and discharge procedures, bringing about enhanced patient experience.
  • Companies can use algorithms to scrutinize huge clinical and molecular data to personalize healthcare treatments by developing AI tools that collect and analyze data from genetic sequencing to image recognition empowering physicians in improved patient care. AI-powered image analysis helps in connecting data points that support cancer discovery and treatment.
  • Big data and artificial intelligence can be used in combination to predict clinical, financial, and operational risks by taking data from all the existing sources. AI analyzes data throughout a healthcare system to mine, automate, and predict processes. It can be used to predict ICU transfers, improve clinical workflows, and even pinpoint a patient’s risk of hospital-acquired infections. Using artificial intelligence to mine health data, hospitals can predict and detect sepsis, which ultimately reduces death rates.
  • AI helps healthcare professionals harness their data to optimize hospital efficiency, better engage with patients, and improve treatment. AI can notify doctors when a patient’s health deteriorates and can even help in the diagnosis of ailments by combing its massive dataset for comparable symptoms. By collecting symptoms of a patient and inputting them into the AI platform, doctors can diagnose quickly and more effectively.   
  • Robot-assisted surgeries ranging from minimally-invasive procedures to open-heart surgeries enables doctors to perform procedures with precision, flexibility, and control that goes beyond human capabilities, leading to fewer surgery-related complications, less pain, and a quicker recovery time. Robots can be developed to improve endoscopies by employing the latest AI techniques which helps doctors get a clearer view of a patient’s illness from both a physical and data perspective.

Having understood the advancements of AI in various facets of healthcare, it is to be realized that AI is not yet ready to fully interpret a patient’s nuanced response to a question, nor is it ready to replace examining patients – but it is efficient in making differential diagnoses from clinical results. It is to be understood very clearly that the role of AI in healthcare is to supplement and enhance human judgment, not to replace physicians and staff.

We at GAVS Technologies are fully equipped with cutting edge AI technology, skills, facilities, and manpower to make a difference in healthcare.

Following are the ongoing and in-pipeline projects that we are working on in healthcare:

ONGOING PROJECT:

AI Devops Automation Service Tools

PROJECTS IN PIPELINE:

AIOps Artificial Intelligence for IT Operations
AIOps Digital Transformation Solutions
Best AI Auto Discovery Tools
Best AIOps Platforms Software

Following are the projects that are being planned:

  • Controlling Alcohol Abuse
  • Management of Opioid Addiction
  • Pharmacy Support – drug monitoring and interactions
  • Reducing medication errors in hospitals
  • Patient Risk Scorecard
  • Patient Wellness – Chronic Disease management and monitoring

In conclusion, it is evident that the Advent of AI in the healthcare domain has shown a tremendous impact on patient treatment and care. For more information on how our AI-led solutions and services can help your healthcare enterprise, please reach out to us here.

About the Author –

Dr. Ramjan is a Data Analyst at GAVS. He has a Doctorate degree in the field of Pharmacy. He is passionate about drawing insights out of raw data and considers himself to be a ‘Data Person’.

He loves what he does and tries to make the most of his work. He is always learning something new from programming, data analytics, data visualization to ML, AI, and more.

Center of Excellence – Big Data

The Big Data CoE is a team of experts that experiments and builds various cutting-edge solutions by leveraging the latest technologies, like Hadoop, Spark, Tensor-flow, and emerging open-source technologies, to deliver robust business results. A CoE is where organizations identify new technologies, learn new skills, and develop appropriate processes that are then deployed into the business to accelerate adoption.

Leveraging data to drive competitive advantage has shifted from being an option to a requirement for hyper competitive business landscape. One of the main objectives of the CoE is deciding on the right strategy for the organization to become data-driven and benefit from a world of Big Data, Analytics, Machine Learning and the Internet of Things (IoT).

Cloud Migration Assessment Tool for Business
Triple Constraints of Projects

“According to Chaos Report, 52% of the projects are either delivered late or run over the allocated. The average across all companies is 189% of the original cost estimate. The average cost overrun is 178% for large companies, 182% for medium companies, and 214% for small companies. The average overrun is 222% of the original time estimate. For large companies, the average is 230%; for medium companies, the average is 202%; and for small companies, the average is 239%.”

Big Data CoE plays a vital role in bringing down the cost and reducing the response time to ensure project is delivered on time by helping the organization to build the skillful resources.

Big Data’s Role

Helping the organization to build quality big data applications on their own by maximizing their ability to leverage data. Data engineers are committed to helping ensure the data:

  • define your strategic data assets and data audience
  • gather the required data and put in place new collection methods
  • get the most from predictive analytics and machine learning
  • have the right technology, data infrastructure, and key data competencies
  • ensure you have an effective security and governance system in place to avoid huge financial, legal, and reputational problems.
Cyber Security and Compliance Services

Data Analytics Stages

Architecture optimized building blocks covering all data analytics stages: data acquisition from a data source, preprocessing, transformation, data mining, modeling, validation, and decision making.

Cyber Security Mdr Services

Focus areas

Algorithms support the following computation modes:

  • Batch processing
  • Online processing
  • Distributed processing
  • Stream processing

The Big Data analytics lifecycle can be divided into the following nine stages:

  • Business Case Evaluation
  • Data Identification
  • Data Acquisition & Filtering
  • Data Extraction
  • Data Validation & Cleansing
  • Data Aggregation & Representation
  • Data Analysis
  • Data Visualization
  • Utilization of Analysis Results

A key focus of Big-data CoE is to establish a data-driven organization by developing proof of concept with the latest technologies with Big Data and Machine learning models. As of part of CoE initiatives, we are involved in developing the AI widgets to various market places, such as Azure, AWS, Magento and others. We are also actively involved in engaging and motivating the team to learn cutting edge technologies and tools like Apache Spark and Scala. We encourage the team to approach each problem in a pragmatic way by making them understand the latest architectural patterns over the traditional MVC methods.

It has been established that business-critical decisions supported by data-driven insights have been more successful. We aim to take our organization forward by unleashing the true potential of data!

If you have any questions about the CoE, you may reach out to them at SME_BIGDATA@gavstech.com

CoE Team Members

  • Abdul Fayaz
  • Adithyan CR
  • Aditya Narayan Patra
  • Ajay Viswanath V
  • Balakrishnan M
  • Bargunan Somasundaram
  • Bavya V
  • Bipin V
  • Champa N
  • Dharmeswaran P
  • Diamond Das
  • Inthazamuddin K
  • Kadhambari Manoharan
  • Kalpana Ashokan
  • Karthikeyan K
  • Mahaboobhee Mohamedfarook
  • Manju Vellaichamy
  • Manojkumar Rajendran
  • Masthan Rao Yenikapati
  • Nagarajan A
  • Neelagandan K
  • Nithil Raj Tharammal Paramb
  • Radhika M
  • Ramesh Jayachandar
  • Ramesh Natarajan
  • Ruban Salamon
  • Senthil Amarnath
  • T Mohammed Anas Aadil
  • Thulasi Ram G
  • Vijay Anand Shanmughadass
  • Vimalraj Subash

Center of Excellence – .Net

Best Cyber Security Services Companies

“Maximizing the quality, efficiency, and reusability by providing innovative technical solutions, creating intellectual capital, inculcating best practices and processes to instill greater trust and provide incremental value to the Stakeholders.”

With the above mission,we have embarked on our journey to establish and strengthen the .NET Center of excellence (CoE).

“The only way to do great work is to love what you do.” – Steve Jobs

Expertise in this CoE is drawn from top talent across all customer engagements within GAVS. Team engagement is maintained at a very high level with various connects such as regular technology sessions, advanced trainings for CoE members from MS, support and guidance for becoming a MS MVP. Members also socialize new trending articles, tools, whitepapers and blogs within the CoE team and MS Teams channels setup for collaboration. All communications from MS Premier Communications sent to Gold Partners is also shared within the group. The high-level roadmap as planned for this group is laid out below.

Best DCaas Providers in USA
<!–td {border: 1px solid #ccc;}br {mso-data-placement:same-cell;}–>
Best DCaas Providers in USA<!–td {border: 1px solid #ccc;}br {mso-data-placement:same-cell;}–>
Best DCaas Providers in USA

The .NET CoEfocused on assistingourcustomers in every stage of theengagement right from on-boarding, planning, execution, technical implementation and finally all the way to launching and growing. Our prescriptive approach is to leverage industry-proven best practices, solutions, reusable components and include robust resources, training, and making a vibrant partner community.

With the above as the primary goal in mind the CoE group is currently engaged inor planning the following initiatives.

Technology Maturity Assessment

One of the main objectivesof this group is to provide constant feedback to all .NET stack project for improvement and improvisation. The goal for this initiative is to build the technology maturity index for all projects for the below parameters.

Best Virtual Desktop Infrastructure Software

Using those approaches within a short span of time we were able to make a significant impact for some of our engagements.

Client – Online Chain Store: Identified cheaper cloud hosting option for application UI.

Benefits: Huge cost and time savings.

Client – Health care sector: Provided alternate solution for DB migrations from DEV to various environments.

Benefits: Huge cost savings due to licensing annually.

Competency Building

“Anyone who stops learning is old, whether at twenty or eighty.” – Henry Ford

Continuous learning and upskilling are the new norms in today’s fast changing technology landscape. This initiative is focused on providing learning and upskilling support to all technology teams in GAVS. Identifying code mentors, supporting team members to become full stack developers are some of the activities planned under this initiative.  Working along with the Learning & Development team,the .NET CoE isformulating different training tracks to upskill the team members and provide support for external assessments and MS certifications.

Solution Accelerators

“Good, better, best. Never let it rest. ‘Till your good is better and your better is best.” – St. Jerome

The primary determinants of CoE effectiveness are involvement in solutions and accelerators and in maintaining standard practices of the relevant technologies across customer engagements across the organization.

As part of this initiative we are focusing on building project templates, DevOps pipelines and automated testing templates for different technology stacks for both Serverless and Server Hosted scenarios. We also are planning similar activities for the Desktop/Mobile Stack with the Multi-Platform App UI (MAUI) framework which is planned to be released for Preview in Q4 2020.

Blockchain Solution and Services

Additionally, we are also adoptingless-code, no-code development platforms for accelerated development cycles for specific use-cases.

As we progress on our journey to strengthen the .NET CoE, we want to act as acatalyst in rapid and early adoption of new technology solutions and work as trusted partners with all our customer and stakeholders.

If you have any questions about the CoE, you may reach out to them at COE_DOTNET@gavstech.com

CoE Team Members

  • Bismillakhan Mohammed
  • Gokul Bose
  • Kirubakaran Girijanandan
  • Neeraj Kumar
  • Prasad D
  • Ramakrishnan S
  • SaphalMalol
  • Saravanan Swaminathan
  • SenthilkumarKamayaswami
  • Sethuraman Varadhan
  • Srinivasan Radhakrishnan
  • Thaufeeq Ahmed
  • Thomas T
  • Vijay Mahalingam

Center of Excellence – Database

Data Center as a Service Providers in USA

“During the World War II, there was a time when the Germans winning on every front and the fear of Hitler taking over the world was looming. At that point in time, had the Allies not taken drastic measures and invested in ground-breaking technologies such as radars, aircraft, atomic energy, etc., the world would have been starkly different from what it is today.

Even in today’s world, the pace at which things are changing is incredible. The evolution of technology is unstoppable, and companies must be ready. There is an inherent need for them to differentiate themselves by providing solutions that showcase a deep understanding of domain and technology to address evolving customer expectations. What becomes extremely important for companies is to establish themselves as incubators of innovation and possess the ability to constantly innovate and fail fast. Centers of Excellence can be an effective solution to address these challenges.

“An Organisation’s ability to learn, and translate that learning into action rapidly, is the ultimate competitive advantage”

  • Jack Welch, former Chairman and CEO of General Electric

The Database CoE was formed with a mission to groom, enhance and incubate talents within GAVS to stay abreast of the evolving technology landscape and help our customers with cutting edge technology solutions.

We identify the expert and the requirements across all customer engagements within GAVS. Regular connects and technology sessions ensure everyone in the CoE is learning at least one new topic in a week. Below is our charter and roadmap by priority:

Data Center Consolidation Initiative Services

Data Center Migration Planning Tools

Database CoE is focused on assisting our customers in every stage of the engagement right from on-boarding, planning, execution with consultative approach and a futuristic mindset. With above primary goals we are currently working on below initiatives:

Competency Building

When we help each other and stand together we evolve to be the strongest.

Continuous learning is an imperative in the current times. Our fast-paced trainings on project teams is an alternate to the primitive classroom sessions. We believe true learning happen when you are working on it hands-on. With this key aspect in mind, we divide the teams in smaller groups and map them to projects to get larger exposure and gain from experience.

This started off with a pilot with an ISP provider where we trained 4 CoE members in Azure and Power BI within a span of 2 months.

Desktop-as-a-Service (DaaS) Solution

Database Maturity Assessment

“When digital transformation is done right, it’s like a caterpillar turning into a butterfly “

  • George Westerman, research scientist at the MIT Center for Digital Business

Why Bother with a Database Assessment?

We often know we have a problem and can visualize the ideal state we want our technology solution to get us to.  However, it is challenging to figure out how to get there because it’s easy to confuse the symptoms with the cause of a problem. Thus, you end up solving the ‘symptom’ with a (potentially expensive) piece of technology that is ill-equipped to address the underlying cause.

We offer a structured process to assess your current database estate and select a technology solution helps you get around this problem, reduce risks and fast track the path to your true objective with futureproofing, by forcing you to both identify the right problem and solve it the right way.

Assessment Framework

Digital Service Desk AI Software

Below are the three key drivers powering the assessment.

Accelerated Assessment:

  • Automated assessment and benchmark of existing and new database estates against industry best practices and standards.
  • Analyze & Finetune
    • Analyze assessment findings and implement recommendations on performance, consistency, and security aspect
  • NOC+ZERO TOUCH L2
    • Shift Left and Automate L1/L2 Service requests and incidents with help of Database COE- Automation experts

As we progress on our journey, we want to establish ourselves as a catalyst to help our customers future-proof technology and help in early adoption of new solutions seamlessly.

If you have any questions about the CoE, you may reach out to them at COE_DATABASE@gavstech.com

CoE Team Members

  • Ashwin Kumar K
  • Ayesha Yasmin
  • Backiyalakshmi M
  • Dharmeswaran P
  • Gopinathan Sivasubramanian
  • Karthikeyan Rajasekaran
  • Lakshmi Kiran  
  • Manju Vellaichamy  
  • Manjunath Kadubayi  
  • Nagarajan A  
  • Nirosha Venkatesalu  
  • Praveen kumar Ralla  
  • Praveena M  
  • Rajesh Kumar Reddy Mannuru  
  • Satheesh Kumar K  
  • Sivagami R  
  • Subramanian Krishnan
  • Venkatesh Raghavendran

RASA – an Open Source Chatbot Solution

Maruvada Deepti

Ever wondered if the agent you are chatting with online is a human or a robot? The answer would be the latter for an increasing number of industries. Conversational agents or chatbots are being employed by organizations as their first-line of support to reduce their response times.

The first generation of bots were not too smart, they could understand only a limited set of queries based on keywords. However, commoditization of NLP and machine learning by Wit.ai, API.ai, Luis.ai, Amazon Alexa, IBM Watson, and others, has resulted in intelligent bots.

What are the different chatbot platforms?

There are many platforms out there which are easy to use, like DialogFlow, Bot Framework, IBM Watson etc. But most of them are closed systems, not open source. These cannot be hosted on our servers and are mostly on-premise. These are mostly generalized and not very specific for a reason.

DialogFlow vs.  RASA

DialogFlow

  • Formerly known as API.ai before being acquired by Google.
  • It is a mostly complete tool for the creation of a chatbot. Mostly complete here means that it does almost everything you need for most chatbots.
  • Specifically, it can handle classification of intents and entities. It uses what it known as context to handle dialogue. It allows web hooks for fulfillment.
  • One thing it does not have, that is often desirable for chatbots, is some form of end-user management.
  • It has a robust API, which allows us to define entities/intents/etc. either via the API or with their web based interface.
  • Data is hosted in the cloud and any interaction with API.ai require cloud related communications.
  • It cannot be operated on premise.

Rasa NLU + Core

  • To compete with the best Frameworks like Google DialogFlow and Microsoft Luis, RASA came up with two built features NLU and CORE.
  • RASA NLU handles the intent and entity. Whereas, the RASA CORE takes care of the dialogue flow and guesses the “probable” next state of the conversation.
  • Unlike DialogFlow, RASA does not provide a complete user interface, the users are free to customize and develop Python scripts on top of it.
  • In contrast to DialogFlow, RASA does not provide hosting facilities. The user can host in their own sever, which also gives the user the ownership of the data.

What makes RASA different?

Rasa is an open source machine learning tool for developers and product teams to expand the abilities of bots beyond answering simple questions. It also gives control to the NLU, through which we can customize accordingly to a specific use case.

Rasa takes inspiration from different sources for building a conversational AI. It uses machine learning libraries and deep learning frameworks like TensorFlow, Keras.

Also, Rasa Stack is a platform that has seen some fast growth within 2 years.

RASA terminologies

  • Intent: Consider it as the intention or purpose of the user input. If a user says, “Which day is today?”, the intent would be finding the day of the week.
  • Entity: It is useful information from the user input that can be extracted like place or time. From the previous example, by intent, we understand the aim is to find the day of the week, but of which date? If we extract “Today” as an entity, we can perform the action on today.
  • Actions: As the name suggests, it’s an operation which can be performed by the bot. It could be replying something (Text, Image, Video, Suggestion, etc.) in return, querying a database or any other possibility by code.
  • Stories: These are sample interactions between the user and bot, defined in terms of intents captured and actions performed. So, the developer can mention what to do if you get a user input of some intent with/without some entities. Like saying if user intent is to find the day of the week and entity is today, find the day of the week of today and reply.

RASA Stack

Rasa has two major components:

  • RASA NLU: a library for natural language understanding that provides the function of intent classification and entity extraction. This helps the chatbot to understand what the user is saying. Refer to the below diagram of how NLU processes user input.
RASA Chatbot

  • RASA CORE: it uses machine learning techniques to generalize the dialogue flow of the system. It also predicts next best action based on the input from NLU, the conversation history, and the training data.

RASA architecture

This diagram shows the basic steps of how an assistant built with Rasa responds to a message:

RASA Chatbot

The steps are as follows:

  • The message is received and passed to an Interpreter, which converts it into a dictionary including the original text, the intent, and any entities that were found. This part is handled by NLU.
  • The Tracker is the object which keeps track of conversation state. It receives the info that a new message has come in.
  • The policy receives the current state of the tracker.
  • The policy chooses which action to take next.
  • The chosen action is logged by the tracker.
  • A response is sent to the user.

Areas of application

RASA is all one-stop solution in various industries like:

  • Customer Service: broadly used for technical support, accounts and billings, conversational search, travel concierge.
  • Financial Service: used in many banks for account management, bills, financial advices and fraud protection.
  • Healthcare: mainly used for fitness and wellbeing, health insurances and others

What’s next?

As any machine learning developer will tell you, improving an AI assistant is an ongoing task, but the RASA team has set their sights on one big roadmap item: updating to use the Response Selector NLU component, introduced with Rasa 1.3. “The response selector is a completely different model that uses the actual text of an incoming user message to directly predict a response for it.”

References:

https://rasa.com/product/features/

https://rasa.com/docs/rasa/user-guide/rasa-tutorial/

About the Author –

Deepti is an ML Engineer at Location Zero in GAVS. She is a voracious reader and has a keen interest in learning newer technologies. In her leisure time, she likes to sing and draw illustrations.
She believes that nothing influences her more than a shared experience.

JAVA – Cache Management

Sivaprakash Krishnan

This article explores the offering of the various Java caching technologies that can play critical roles in improving application performance.

What is Cache Management?

A cache is a hot or a temporary memory buffer which stores most frequently used data like the live transactions, logical datasets, etc. This intensely improves the performance of an application, as read/write happens in the memory buffer thus reducing retrieval time and load on the primary source. Implementing and maintaining a cache in any Java enterprise application is important.

  • The client-side cache is used to temporarily store the static data transmitted over the network from the server to avoid unnecessarily calling to the server.
  • The server-side cache could be a query cache, CDN cache or a proxy cache where the data is stored in the respective servers instead of temporarily storing it on the browser.

Adoption of the right caching technique and tools allows the programmer to focus on the implementation of business logic; leaving the backend complexities like cache expiration, mutual exclusion, spooling, cache consistency to the frameworks and tools.

Caching should be designed specifically for the environment considering a single/multiple JVM and clusters. Given below multiple scenarios where caching can be used to improve performance.

1. In-process Cache – The In-process/local cache is the simplest cache, where the cache-store is effectively an object which is accessed inside the application process. It is much faster than any other cache accessed over a network and is strictly available only to the process that hosted it.

Data Center Consolidation Initiative Services

  • If the application is deployed only in one node, then in-process caching is the right candidate to store frequently accessed data with fast data access.
  • If the in-process cache is to be deployed in multiple instances of the application, then keeping data in-sync across all instances could be a challenge and cause data inconsistency.
  • An in-process cache can bring down the performance of any application where the server memory is limited and shared. In such cases, a garbage collector will be invoked often to clean up objects that may lead to performance overhead.

In-Memory Distributed Cache

Distributed caches can be built externally to an application that supports read/write to/from data repositories, keeps frequently accessed data in RAM, and avoid continuous fetching data from the data source. Such caches can be deployed on a cluster of multiple nodes, forming a single logical view.

  • In-memory distributed cache is suitable for applications running on multiple clusters where performance is key. Data inconsistency and shared memory aren’t matters of concern, as a distributed cache is deployed in the cluster as a single logical state.
  • As inter-process is required to access caches over a network, latency, failure, and object serialization are some overheads that could degrade performance.

2. In-memory database

In-memory database (IMDB) stores data in the main memory instead of a disk to produce quicker response times. The query is executed directly on the dataset stored in memory, thereby avoiding frequent read/writes to disk which provides better throughput and faster response times. It provides a configurable data persistence mechanism to avoid data loss.

Redis is an open-source in-memory data structure store used as a database, cache, and message broker. It offers data replication, different levels of persistence, HA, automatic partitioning that improves read/write.

Replacing the RDBMS with an in-memory database will improve the performance of an application without changing the application layer.

3. In-Memory Data Grid

An in-memory data grid (IMDG) is a data structure that resides entirely in RAM and is distributed among multiple servers.

Key features

  • Parallel computation of the data in memory
  • Search, aggregation, and sorting of the data in memory
  • Transactions management in memory
  • Event-handling

Cache Use Cases

There are use cases where a specific caching should be adapted to improve the performance of the application.

1. Application Cache

Application cache caches web content that can be accessed offline. Application owners/developers have the flexibility to configure what to cache and make it available for offline users. It has the following advantages:

  • Offline browsing
  • Quicker retrieval of data
  • Reduced load on servers

2. Level 1 (L1) Cache

This is the default transactional cache per session. It can be managed by any Java persistence framework (JPA) or object-relational mapping (ORM) tool.

The L1 cache stores entities that fall under a specific session and are cleared once a session is closed. If there are multiple transactions inside one session, all entities will be stored from all these transactions.

3. Level 2 (L2) Cache

The L2 cache can be configured to provide custom caches that can hold onto the data for all entities to be cached. It’s configured at the session factory-level and exists as long as the session factory is available.

  • Sessions in an application.
  • Applications on the same servers with the same database.
  • Application clusters running on multiple nodes but pointing to the same database.

4. Proxy / Load balancer cache

Enabling this reduces the load on application servers. When similar content is queried/requested frequently, proxy takes care of serving the content from the cache rather than routing the request back to application servers.

When a dataset is requested for the first time, proxy saves the response from the application server to a disk cache and uses them to respond to subsequent client requests without having to route the request back to the application server. Apache, NGINX, and F5 support proxy cache.

Desktop-as-a-Service (DaaS) Solution

5. Hybrid Cache

A hybrid cache is a combination of JPA/ORM frameworks and open source services. It is used in applications where response time is a key factor.

Caching Design Considerations

  • Data loading/updating
  • Performance/memory size
  • Eviction policy
  • Concurrency
  • Cache statistics.

1. Data Loading/Updating

Data loading into a cache is an important design decision to maintain consistency across all cached content. The following approaches can be considered to load data:

  • Using default function/configuration provided by JPA and ORM frameworks to load/update data.
  • Implementing key-value maps using open-source cache APIs.
  • Programmatically loading entities through automatic or explicit insertion.
  • External application through synchronous or asynchronous communication.

2. Performance/Memory Size

Resource configuration is an important factor in achieving the performance SLA. Available memory and CPU architecture play a vital role in application performance. Available memory has a direct impact on garbage collection performance. More GC cycles can bring down the performance.

3. Eviction Policy

An eviction policy enables a cache to ensure that the size of the cache doesn’t exceed the maximum limit. The eviction algorithm decides what elements can be removed from the cache depending on the configured eviction policy thereby creating space for the new datasets.

There are various popular eviction algorithms used in cache solution:

  • Least Recently Used (LRU)
  • Least Frequently Used (LFU)
  • First In, First Out (FIFO)

4. Concurrency

Concurrency is a common issue in enterprise applications. It creates conflict and leaves the system in an inconsistent state. It can occur when multiple clients try to update the same data object at the same time during cache refresh. A common solution is to use a lock, but this may affect performance. Hence, optimization techniques should be considered.

5. Cache Statistics

Cache statistics are used to identify the health of cache and provide insights about its behavior and performance. Following attributes can be used:

  • Hit Count: Indicates the number of times the cache lookup has returned a cached value.
  • Miss Count: Indicates number of times cache lookup has returned a null or newly loaded or uncached value
  • Load success count: Indicates the number of times the cache lookup has successfully loaded a new value.
  • Total load time: Indicates time spent (nanoseconds) in loading new values.
  • Load exception count: Number of exceptions thrown while loading an entry
  • Eviction count: Number of entries evicted from the cache

Various Caching Solutions

There are various Java caching solutions available — the right choice depends on the use case.

Software Test Automation Platform

At GAVS, we focus on building a strong foundation of coding practices. We encourage and implement the “Design First, Code Later” principle and “Design Oriented Coding Practices” to bring in design thinking and engineering mindset to build stronger solutions.

We have been training and mentoring our talent on cutting-edge JAVA technologies, building reusable frameworks, templates, and solutions on the major areas like Security, DevOps, Migration, Performance, etc. Our objective is to “Partner with customers to realize business benefits through effective adoption of cutting-edge JAVA technologies thereby enabling customer success”.

About the Author –

Sivaprakash is a solutions architect with strong solutions and design skills. He is a seasoned expert in JAVA, Big Data, DevOps, Cloud, Containers, and Micro Services. He has successfully designed and implemented a stable monitoring platform for ZIF. He has also designed and driven Cloud assessment/migration, enterprise BRMS, and IoT-based solutions for many of our customers. At present, his focus is on building ‘ZIF Business’ a new-generation AIOps platform aligned to business outcomes.

Hyperautomation

Machine learning service provider

Bindu Vijayan

According to Gartner, “Hyper-automation refers to an approach in which organizations rapidly identify and automate as many business processes as possible. It involves the use of a combination of technology tools, including but not limited to machine learning, packaged software and automation tools to deliver work”.  Hyper-automation is to be among the year’s top 10 technologies, according to them.

It is expected that by 2024, organizations will be able to lower their operational costs by 30% by combining hyper-automation technologies with redesigned operational processes. According to Coherent Market Insights, “Hyper Automation Market will Surpass US$ 23.7 Billion by the end of 2027.  The global hyper automation market was valued at US$ 4.2 Billion in 2017 and is expected to exhibit a CAGR of 18.9% over the forecast period (2019-2027).”

How it works

To put it simply, hyper-automation uses AI to dramatically enhance automation technologies to augment human capabilities. Given the spectrum of tools it uses like Robotic Process Automation (RPA), Machine Learning (ML), and Artificial Intelligence (AI), all functioning in sync to automate complex business processes, even those that once called for inputs from SMEs,  implies this is a powerful tool for organisations in their digital transformation journey.

Hyperautomation allows for robotic intelligence into the traditional automation process, and enhances the completion of processes to make it more efficient, faster and errorless.  Combining AI tools with RPA, the technology can automate almost any repetitive task; it automates the automation by identifying business processes and creates bots to automate them. It calls for different technologies to be leveraged, and that means the businesses investing in it should have the right tools, and the tools should be interoperable. The main feature of hyperautomation is, it merges several forms of automation and works seamlessly together, and so a hyperautomation strategy can consist of RPA, AI, Advanced Analytics, Intelligent Business Management and so on. With RPA, bots are programmed to get into software, manipulate data and respond to prompts. RPA can be as complex as handling multiple systems through several transactions, or as simple as copying information from applications. Combine that with the concept of Process Automation or Business Process Automation which enables the management of processes across systems, it can help streamline processes to increase business performance.    The tool or the platform should be easy to use and importantly scalable; investing in a platform that can integrate with the existing systems is crucial. The selection of the right tools is what  Gartner calls “architecting for hyperautomation.”

Impact of hyperautomation

Hyperautomation has a huge potential for impacting the speed of digital transformation for businesses, given that it automates complex work which is usually dependent on inputs from humans. With the work moved to intelligent digital workers (RPA with AI) that can perform repetitive tasks endlessly, human performance is augmented. These digital workers can then become real game-changers with their efficiency and capability to connect to multiple business applications, discover processes, work with voluminous data, and analyse in order to arrive at decisions for further / new automation.

The impact of being able to leverage previously inaccessible data and processes and automating them often results in the creation of a digital twin of the organization (DTO); virtual models of every physical asset and process in an organization.  Sensors and other devices monitor digital twins to gather vital information on their condition, and insights are gathered regarding their health and performance. As with data, the more data there is, the systems get smarter with it, and are able to provide sharp insights that can thwart problems, help businesses make informed decisions on new services/products, and in general make informed assessments. Having a DTO throws light on the hitherto unknown interactions between functions and processes, and how they can drive value and business opportunities.  That’s powerful – you get to see the business outcome it brings in as it happens or the negative effect it causes, that sort of intelligence within the organization is a powerful tool to make very informed decisions.

Hyperautomation is the future, an unavoidable market state

hyperautomation is an unavoidable market state in which organizations must rapidly identify and automate all possible business processes.” – Gartner

It is interesting to note that some companies are coming up with no-code automation. Creating tools that can be easily used even by those who cannot read or write code can be a major advantage – It can, for e.g., if employees are able to automate the multiple processes that they are responsible for, hyperautomation can help get more done at a much faster pace, sparing time for them to get involved in planning and strategy.  This brings more flexibility and agility within teams, as automation can be managed by the teams for the processes that they are involved in.

Conclusion

With hyperautomation, it would be easy for companies to actually see the ROI they are realizing from the amount of processes that have been automated, with clear visibility on the time and money saved. Hyperautomation enables seamless communication between different data systems, to provide organizations flexibility and digital agility. Businesses enjoy the advantages of increased productivity, quality output, greater compliance, better insights, advanced analytics, and of course automated processes. It allows machines to have real insights on business processes and understand them to make significant improvements.

“Organizations need the ability to reconfigure operations and supporting processes in response to evolving needs and competitive threats in the market. A hyperautomated future state can only be achieved through hyper agile working practices and tools.”  – Gartner

References:

Assess Your Organization’s Maturity in Adopting AIOps

IT operations analytics

Anoop Aravindakshan

Artificial Intelligence for IT operations (AIOps) is adopted by organizations to deliver tangible Business Outcomes. These business outcomes have a direct impact on companies’ revenue and customer satisfaction.

A survey from AIOps Exchange 2019, reports that 84% of business owners who attended the survey, confirmed that they are actively evaluating AIOps to be adopted in their organizations.

So, is AIOps just automation? Absolutely NOT!

Artificial Intelligence for IT operations implies the implementation of true Autonomous Artificial Intelligence in ITOps, which needs to be adopted as an organization-wide strategy. Organizations will have to assess their existing landscape, processes, and decide where to start. That is the only way to achieve the true implementation of AIOps.

Every organization trying to evaluate AIOps as a strategy should read through this article to understand their current maturity, and then move forward to reach the pinnacle of Artificial Intelligence in IT Operations.

The primary success factor in adopting AIOps is derived from the Business Outcomes the organization is trying to achieve by implementing AIOps – that is the only way to calculate ROI.

There are 4 levels of Maturity in AIOps adoption. Based on our experience in developing an AIOps platform and implementing the platform across multiple industries, we have arrived at these 4 levels. Assessing an organization against each of these levels, helps in achieving the goal of TRUE Artificial Intelligence in IT Operations.

Level 1: Knee-jerk

Events, logs are generated in silos and collected from various applications and devices in the infrastructure. These are used to generate alerts that are commissioned to command centres to escalate as per the SOPs (standard operating procedures) defined. The engineering teams work in silos, not aware of the business impact that these alerts could potentially create. Here, operations are very reactive which could cost the organization millions of dollars.

Level 2: Unified

All events, logs, and alerts are integrated into one central locale. ITSM processes are unified. This helps in breaking silos and engineering teams are better prepared to tackle business impacts. SOPs have been adjusted since the process is unified, but this is still reactive incident management.

Level 3: Intelligent

Machine Learning algorithms (either supervised or unsupervised) have been implemented on the unified data to derive insights. There are baseline metrics that are calibrated and will be used as a reference for future events. With more data, the metrics get richer. IT operations team can correlate incidents / events with business impacts by leveraging AI & ML. If Mean-Time-To-Resolve (MTTR) an incident has been reduced by automated identification of the root cause, then the organization has attained level 3 maturity in AIOps.

Level 4: Predictive & Autonomous

The pinnacle of AIOps is level 4. If incidents and performance degradation of applications can be predicted by leveraging Artificial Intelligence, it implies improved application availability. Autonomous remediation bots can be triggered spontaneously based on the predictive insights, to fix incidents that are prone to happen in the enterprise. Level 4 is a paradigm shift in IT operations – moving operations entirely from being reactive, to becoming proactive.

Conclusion

As IT operations teams move up each level, the essential goal to keep in mind is the long-term strategy that needs to be attained by adopting AIOps. Artificial Intelligence has matured over the past few decades, and it is up to AIOps platforms to embrace it effectively. While choosing an AIOps platform, measure the maturity of the platform’s artificial intelligent coefficient.

About the Author:

An evangelist of Zero Incident FrameworkTM, Anoop has been a part of the product engineering team for long and has recently forayed into product marketing. He has over 14 years of experience in Information Technology across various verticals, which include Banking, Healthcare, Aerospace, Manufacturing, CRM, Gaming and Mobile.

Creating Purposeful Corporations, In pursuit of Conscious Capitalism

Gavs technologies ceo

Sumit Ganguli

More than 8 million metric tons of plastic leak into the ocean every year, so building infrastructure that stops plastic before it gets into the ocean is key to solving this issue,” said H. Fisk Johnson, Chairman, and CEO of SC Johnson. SC Johnson, an industry-leading manufacturer of household consumer brands, has launched a global partnership to stop plastic waste from entering the ocean and fight poverty.

In August 2019, after 42 years of its inception, Business Roundtable,  that has periodically issued Principles of Corporate Governance, with emphasis on serving shareholders, has released a new statement of Purpose of a Corporation. This new statement was signed by 181 CEOs who have committed to lead their companies to benefit all stakeholders – customers, employees, suppliers, communities and shareholders.  Jamie Dimon, Chairman and CEO of JPMorgan Chase & Co., is the Chairman of Business Roundtable. He went on to say, “The American dream is alive, but fraying,” “Major employers are investing in their workers and communities because they know it is the only way to be successful over the long term. These modernized principles reflect the business community’s unwavering commitment to continue to push for an economy that serves all Americans.

Today the definition of corporate purpose seems to be changing. Companies are now focused on the environment and all the stakeholders.  There is a growing ambivalence about Capitalism that only promoted the pursuit of wealth, according to a Harvard Business School survey.

But this is a far cry from when we were growing up in India as youths, in the 1980s. Our definition of personal success was to expeditiously acquire wealth. Most of us who were studying Engineering, Medicine or pursuing other professional degrees, were all looking for a job that would sustain us and support our immediate family. The other option was to emigrate to America or other developed countries, for further studies and make a life here – to celebrate Capitalism in all its glory. 

In India, we were quite steeped in religious festivals and rituals. We attended Baal Mandir and had moral science in school, but the concept of Service, Altruism,  Seva, Sharing were largely platitudes and they were not a part of our daily lives.  There was an inbuilt cynicism about charity and we never felt that when we grow up, we need to think about the greater good of the society. 

And that is where Conscious Capitalism comes in. Instead of espousing Ayn Rand’s version of scorched earth capitalism, “ Selfishness is a Virtue”, or blindly following  Gordon Gekko’s “Greed is good”,  the media, parents, teachers, influence makers could promote and ingrain in all of the youth, students and people at large that there is merit in wealth creation, but it could be infused with altruism. We could celebrate the successful who also share. This could dispel the notion that charity and sharing of wealth is only for the rich and the famous.  

ai automation in cloud computing

America gets criticized for many things around the world, but often the world overlooks that the largest amount of charity and donations have been from the USA.  Bill & Melinda Gates Foundation, Warren Buffet, Larry Elison of Oracle who has pledged a significant portion of his wealth to the Bill & Melinda Gates Foundation, Mark Zuckerberg of Facebook and many others have absolutely embraced the concept of Conscious Capitalism for their corporations. But what would really broaden the pyramid, would be when early entrepreneurs and upcoming executives are also engaged in sharing and giving, and not wait till they reach the pinnacle of success. We cannot expect only governmental initiatives to support the underprivileged. We need to celebrate Conscious Capitalism and entrepreneurs and business leaders who are pursuing their dreams and are also sharing some portion of their wealth with the society.

At GAVS and through the Private Equity firm Basil Partners we are privileged to have been involved in an initiative to nurture and support a small isolated village named Ramanwadi in Maharashtra, through a project named Venu Madhuri (www.venumadhuri.org).  The volunteers involved in supporting this small village have brought success in several areas of rural development and the small hamlet is inching towards self-sufficiency.

Basil Partners along with Apar Industries seed-funded the Midday meal program, (www.annamrita.org)  that feeds almost 1.26 Million school students per day in Mumbai; and have promoted the Bhakti Vedanta Hospital in Mumbai.

These are all very humble efforts compared to some of the massive projects undertaken by the largest of groups and individuals. However, they all make a difference. I truly believe that we need to internalize some of the credo and values that have been espoused by H Fisk Johnson & the work companies like SC Johnson is doing, emulate Azim Premji, Satya Nadella and many others. They are the true ambassadors of Conscious Capitalism and are creating purposeful corporations.