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Optimizing applications with EagleDream in Amazon CodeGuru Profiler

This is a guest post by Dustin Potter at EagleDream Technologies. In their own words, “EagleDream Technologies educates, enables, and empowers the world’s greatest companies to use cloud-native technology to transform their business. With extensive experience architecting workloads on the cloud, as well as a full suite of skills in application modernization, data engineering, data […]

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This is a guest post by Dustin Potter at EagleDream Technologies. In their own words, “EagleDream Technologies educates, enables, and empowers the world’s greatest companies to use cloud-native technology to transform their business. With extensive experience architecting workloads on the cloud, as well as a full suite of skills in application modernization, data engineering, data lake design, and analytics, EagleDream has built a growing practice in helping businesses redefine what’s possible with technology.”

EagleDream Technologies is a trusted cloud-native transformation company and APN Premier Consulting Partner for businesses using AWS. EagleDream is unique in using its cloud-native software engineering and application modernization expertise to guide you through your journey to the cloud, optimize your operations, and transform how you do business using AWS. Our team of highly trained professionals helps accelerate projects at every stage of the cloud journey. This post shares our experience using Amazon CodeGuru Profiler to help one of our customers optimize their application under tight deadlines.

Project overview

Our team received a unique opportunity to work with one of the industry’s most disruptive airline technology leaders, who uses their expertise to build custom integrated airline booking, loyalty management, and ecommerce platforms. This customer reached out to our team to help optimize their new application. They already had a few clients using the system, but they recently signed a deal with a major airline that would represent a load increase to their platform five times in size. It was critical that they prepare for this significant increase in activity. The customer was running a traditional three-tier application written in Java that used Amazon Aurora for the data layer. They had already implemented autoscaling for the web servers and database but realized something was wrong when they started running load tests. During the first load test, the web tier expanded to over 80 servers and Aurora reached the max number of read replicas.

Our team knew we had to dive deep and investigate the application code. We had previously used other application profiling tools and realized how invaluable they can be when diagnosing these types of issues. Also, AWS recently announced Amazon CodeGuru and we were eager to try it out. On top of that, the price and ease of setup was a driving factor for us. We had looked at an existing commercial application performance monitoring tool, but it required more invasive changes to utilize. To automate the install of these tools, we would have needed to make changes to the customer’s deployment and infrastructure setup. We had to move quickly with as little disruption to their ongoing feature development as possible, which contributed to our final decision to use CodeGuru.

CodeGuru workflow

After we decided on CodeGuru, it was easy to get CodeGuru Profiler installed and start capturing metrics. There are two ways to profile an application. The first is to reference the profiler agent during the start of the application by using the standard -javaagent parameter. This is useful if the group performing the profiling isn’t the development team, for example in an organization with more traditional development and operation silos. This is easy to set up because all that’s needed is to download the .jar published in the documentation and alter any startup scripts to include the agent and the name of the profiling group to use.

The second way to profile the application is to include the profiler code via a dependency in your build system and instantiate a profiling thread somewhere at the entry point of the program. This option is great if the development team is handling the profiling. For this particular use case, we fell into the second group, so including it in the code was the quickest and easiest approach. We added the library as a Maven dependency and added a single line of application code. After the code was committed, we used the customer’s existing Jenkins setup to deploy the latest build to an integration environment. The final step of the pipeline was to run load tests against the new build. After the tests completed, we had a flame graph that we used to start identifying any issues.

The workflow includes the following steps:

  1. Developers check in code.
  2. The check-in triggers a Jenkins job.
  3. Maven compiles the code.
  4. Jenkins deploys the artifact to the development environment.
  5. Load tests run against the newly deployed code.
  6. CodeGuru Profiler monitors the environment and generates a flame graph and a recommendation report.

The following diagram illustrates the workflow.

Flame graphs group together stack traces and highlight which part of the code consumes the most resources. The following screenshot is a sample flame graph from an AWS demo application for reference.

After CodeGuru generated the flame graphs and recommendations report, we took an iterative approach and tackled the biggest offenders first. The flame graphs provided perceptive guidance for actionable recommendations that it discovers and made it easy to identify which execution paths were taking the longest to complete. By looking at the longest frames first, we identified that the customer faced challenges around thread safety, which was leading to locking issues. To resolve issues collaboratively with the client, we created a Slack channel to review the latest graphs and provide recommendations directly to the developers. After the developers implemented the suggested changes, we deployed a new build and had a corresponding graph in a few minutes.

Results

After just one week, our team successfully alleviated their scaling challenges at the web service layer. When we ran the load tests, we saw expected results of a few servers instead of the more than 80 servers previously. Additionally, because we optimized the code, we reduced the existing application footprint, which saved our customer 30% of compute load.

Cost savings aside, one of the most notable benefits of this project was developer education. With CodeGuru Profiler pinpointing where the bottlenecks were, the developers could recognize inefficient patterns in the code that might lead to severe performance hits down the road. This helped them better understand the features of the language they’re using and armed them with increased efficiency in future development and debugging.

Conclusion

With the web service layer better optimized, our next step is to use CodeGuru and other AWS tools like Performance Insights to tackle the database layer. Even if you aren’t experiencing extreme performance challenges, CodeGuru Profiler can provide valuable insights to the health of your application in any environment, from development all the way to production, with minimal CPU utilization. Integrating these results as part of the SDLC or DevOps process leads to better efficiency and gives you and your developers the tools you need to be successful. To learn more about how to get started with CodeGuru Profiler and CodeGuru Reviewer, check the documentation found here.


About the Author

Dustin Potter is a Principal Cloud Solutions Architect at EagleDream Technologies.

Source: https://aws.amazon.com/blogs/machine-learning/optimizing-applications-with-eagledream-in-amazon-codeguru-profiler/

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A Quick Guide to Conversational AI And It’s Working Process

Customer support is an integral part of every business; without offering support services, it is difficult to achieve maximum customer satisfaction. To ensure the same, […]

The post A Quick Guide to Conversational AI And It’s Working Process appeared first on Quytech Blog.

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Customer support is an integral part of every business; without offering support services, it is difficult to achieve maximum customer satisfaction. To ensure the same, businesses hire professionals who work round the clock to deliver support services. No matter how efficiently a business handles this segment, they might have to face problems such as “delay in responding customers’ queries” or “making a customer wait to connect with the support professionals”, and more.

A Conversational AI is a perfect solution to this most common challenge that manufacturing, FMCG, retail, e-commerce, and other industries are facing. Never heard of this term?

Well, this is the latest trend in almost all the industries that are already using artificial intelligence technology or wanting to adopt the same in their business operations. Let’s read about the same in detail.

What is Conversational AI?

Conversational AI is a specific kind of artificial intelligence that makes software interact and converse with users in a highly intuitive way. It uses natural language processing (NLP) to understand and interact in human language.

The conversational AI, when integrated into chatbots, messengers, and voice assistants, enables businesses to deliver personalized customer experience and achieve 100% customer satisfaction. Google Home and Amazon Echo are the two popular examples of it.

Applications of Conversational AI

Conversational AI a new and automated way of offering customer support services. Healthcare, Adtech, logistics, insurance, travel, hospitality, finances, and other industries are using technology in the following:

Messaging applications

Conversational AI can be used in a messaging application to offer personalized support services through chat. Your customers can choose the “chat support” option and talk to the chatbot to get the support.

Speech-based virtual assistants

A conversational AI can use speech recognition technology so that you can offer a speech-based virtual assistant for your customers. These are the type of chatbots where users can get any information through voice commands.

Virtual customer assistants

This type of conversational AI helps in offering online support to customers; you can develop the same to offer support through Web, SMS, messaging applications, and more.

Virtual personal assistants

A virtual personal assistant, powered by conversational AI, minimizes the need of hiring a huge team to offer dedicated support services to each of your customers.

Virtual employee assistants

Employees working in big organizations might need various types of assistants. A conversational AI used to build a virtual employee assistant can be the point of contact for all such employees. They can find the required information just by interacting with that assistant.

Robotic process automation

Robotic process automation using the potential of conversational AI helps a machine to understand human conversations and their intent to perform automated tasks.

Working of conversational AI

Machine learning, deep learning, and natural language processing are the three main technologies behind conversational AI. Here is how it works:

  1. Collection of unstructured data from various sources
  2. Data preprocessing and feature engineering
  3. Creating an AI model
  4. Training the model to automatically improve from experiences
  5. Testing the model
  6. Detecting patterns and making decisions
  7. AI deployment

What benefits businesses can get by using conversational AI?

Apart from helping businesses to deliver an unmatched customer experience, conversational AI can offer a plethora of other benefits that include:

Saves Time

Having an automated chatbot would help you save a considerable amount of time. The saved time can be used to perform other tasks or focus on your business’ marketing and promotion.

Helps in providing Real-Time Support Services

A conversational AI can handle multiple queries at one time, without even letting other customers wait. In short, every customer will feel like they are getting dedicated support services in real-time.

Improves business efficiency

With a conversational AI, you can have the assurance that your customers are being taken care of properly. In short, their queries are being handled and resolved immediately. You can focus on other segments of your business and improve efficiency.

Helps in lowering down Customers’ Complaints

Since a conversational AI can immediately respond to queries of the customers, it can help to reduce the number of complaints. Resolving customers’ complaints without making them call a support professional would increase customer loyalty and increase your brand reputation.

Increases chances of sales

By providing a persistent communication channel that precedes the context further, you can make your customers explore more and shop more. Moreover, a conversational AI can also help in reducing the cart abandonment rate as it can provide immediate assistance regarding the issues a user is facing while making the payment, applying a discount code, or at any other time.

The user would not even have to contact the support center. To understand this better, let’s take an example- a user has added one or more products to the cart, but he/she is unable to find his/her preferred mode of payment. After a few minutes, what would the customer do, either visit the contact us section to get the customer support number and connect to a professional (which is a time taking process) or simply get the support through a conversational AI. The latter would automatically send a message to the customer to ask the query and provide a resolution.

Now when you know everything about conversational AI and want to build one for you, then contact Quytech, a trusted AI app development company. Quytech has more than a decade of experience in working on artificial intelligence, machine learning, and other latest technologies.

Final Words

Are you curious to know about conversational artificial intelligence? Give this article a read to know the definition and working of conversational AI in detail. We have also mentioned the reasons why this technology is becoming the talk of the town among businesses of all sizes and types. After reading the article, if you want to develop a tailor-made conversational AI for your business, then reach out to a reliable and experienced AI development company or hire AI developers with considerable experience.

Source: https://www.quytech.com/blog/what-is-conversational-ai-and-how-does-it-work/

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Are Legal chatbots worth the time and effort?

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KLoBot — the Best AI-Chatbot builder platform

The legal industry is always known for its resistance to change, but technology in the legal landscape has seen rapid growth from the past few years. The global Coronavirus pandemic has also accelerated the pace of investments in legal technology, which is likely to transform the legal marketplace.

Several law firms are majorly focusing on the adoption of innovative technology, which has the capability to modernize the practice of law. Innovative technologies, including Artificial Intelligence, Analytics, and Blockchain, among others, prioritizes the speed and efficiency of legal services.

Technology in the legal sector is an enabler that empowers attorneys and paralegals to perform their jobs better.

The use of AI and its applications in the legal industry is moving higher and is becoming the next big thing for legal firms. By 2024 the legal AI software market is expected to reach $1,236 million and is forecasted to grow at a CAGR of 31.3% during 2019–2024. (2)

Incorporating AI into legal practice can augment the workflows and streamline the work processes. AI-powered chatbots are disrupting the legal industry and are poised to become a preferred mode of communication for internal as well as external users. Leveraging NLP and NLU algorithm power, which are one of the prominent fields in AI, chatbots can understand intents, contexts, and further handle end to end human-machine interactions.

Law firms, as well as corporate legal departments, continue to look for new ways to enhance efficiency and drive productivity. AI-enabled chatbots are one such way that has the potential to revolutionize the law firm operations. These chatbots are a new approach for law firms to imitate human conversations and automatically respond to clients as well as attorneys’ queries.

Legal chatbots have the capabilities to make better and quicker decisions when compared to human agents. It reduces the burden on attorneys and paralegals to repetitively answer the same queries, which further brings consistency to users’ responses.

1. 8 Proven Ways to Use Chatbots for Marketing (with Real Examples)

2. How to Use Texthero to Prepare a Text-based Dataset for Your NLP Project

3. 5 Top Tips For Human-Centred Chatbot Design

4. Chatbot Conference Online

Internal Chatbots

Internal chatbots are nothing but the chatbots for internal operations and communications, helping law firms manage enterprise collaboration. Internal legal chatbots help law firms automate the contract review process, which is one of the most tedious tasks for attorneys and in-house counsel.

Legal chatbots for attorneys come with a predefined set of policies to review & analyze documents, perform due diligence, and automate other monotonous tasks that attorneys perform. Other basic tasks comprising scheduling meetings, setting up reminders, and searching relevant matter information can also be performed by legal chatbots. Internal legal chatbots empower attorneys to reduce the risk of human errors by automating the monotonous administrative chores and allow them to focus more on higher value and complicated tasks that need attorney’s intervention.

External Chatbots

External chatbots, on the other side, are the client-facing legal chatbots. These chatbots can draft the legal documents, including UCC filings, divorce forms, and other non-disclosure agreements based on the client inputs.

External legal chatbots empower law firms to handle the client intake process efficiently and generate leads, which further reduces an attorney’s time spent on these activities.

In the current scenario of receiving information instantly at a fingertip, legal chatbots serve as the best solution to handle client queries and provide legal advice.

External, as well as internal legal chatbots with their 24/7 supporting abilities, facilitate law firms to manage operational costs and meet the evolving client expectations.

Although chatbots are taking time to augment legal services but are worth the effort.

KLoBot is an incredibly intelligent AI chatbot builder platform that allows legal firms to create text and voice-based chatbots within minutes. KLoBot’s easy drag and drop skill interface helps law firms to design no-code chatbots that can be deployed across an organization’s favorite channels. The chatbots built on the KLoBot platform help law firms perform simple and complex routine tasks, including QnA and knowledge repository search. Few other jobs, including scheduling meetings, setting up reminders, completing actions on behalf of attorneys, finding colleagues, assisting attorneys, and much more, are also being performed by KLoBot enabled chatbots.

KLoBot enabled chatbots to act as a personal assistant and enhance attorneys as well as client experiences. These chatbots empower law firms to simplify internal as well as external communications and streamline business processes.

KLoBot AI chatbots with its feature-rich admin console, provide law firms robust security controls. To know more about KLoBot click here.

References

[1] The Law Society Capturing Technological Innovation in Legal Services Report

[2] https://www.marketsandmarkets.com/

Source: https://chatbotslife.com/are-legal-chatbots-worth-the-time-and-effort-5f44936f7e89?source=rss—-a49517e4c30b—4

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Inbenta Announces Partnership with IntelePeer to Deliver Smarter Workflows to Customers

Inbenta Technologies, a global leader in Symbolic AI-based Customer Interactions applications (artificial intelligence (AI) and natural language processing (NLP) products) announced today a new partnership with IntelePeer, a leading Communications Platform as a Service (CPaaS), provider. The combined partnership will empower users to build smarter and more powerful workflows so organizations can provide more innovative, […]

The post Inbenta Announces Partnership with IntelePeer to Deliver Smarter Workflows to Customers appeared first on Inbenta.

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New Product Offering Defines How Voice, Messaging and Chatbots Interoperate

Inbenta Technologies, a global leader in Symbolic AI-based Customer Interactions applications (artificial intelligence (AI) and natural language processing (NLP) products) announced today a new partnership with IntelePeer, a leading Communications Platform as a Service (CPaaS), provider. The combined partnership will empower users to build smarter and more powerful workflows so organizations can provide more innovative, agile, and scalable customer and employee support processes.

The Inbenta platform integrated with Atmosphere SmartFlows enables organizations to easily configure, automate, measure, and improve business interactions across multiple channels such as voice, SMS, Social Media and Enterprise Collaboration Platforms.  Customers will be able to use the intuitive drag and drop features, without any complicated coding. This will offload agent workload and create superior digital experiences.

“We are really happy to see this partnership going forward,” said Inbenta CEO Jordi Torras. “Combining the IntelePeer easy-to-use omni-channel platform with our Symbolic AI will empower our customers to build workflows across very different channels in a cohesive way, providing intelligence along the way.”

“With customer expectations on the rise and a constantly changing business climate, companies must stay ahead of the market with agile solutions,” said Jeremy Jones, IntelePeer’s Chief Commercial Officer. “We look forward to a successful partnership with Inbenta. With growing demand for more intelligent interactions, Inbenta’s AI helps detect customer’s intents, and respond with SmartFlows across different channels including voice, SMS, and social messaging to consistently stay ahead of the curve in a fast-changing digital business.”

About Inbenta

Inbenta was founded by Jordi Torras in Barcelona and is now headquartered in Silicon Valley. Inbenta empowers the world’s largest enterprise and e-commerce companies to improve customer satisfaction rates and reduce support costs with best-in-class functionality.

About IntelePeer

IntelePeer powers the new customer experience. Our Atmosphere® CPaaS  enables companies to communicate better – driving more revenue, improving their customer experience, and making better business decisions – leveraging omni-channel Automation & Self-Service, AI, and Analytics, all delivered through a single easy-to-use cloud platform that works seamlessly with your existing business solutions. For more information visit: www.intelepeer.com

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