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How Machine Learning Can Help Employees Focus on Their Work

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How Machine Learning Can Help Employees Focus on Their Work

The average employee spends around four hours a day on administrative tasks, such as answering emails, scheduling meetings, and managing their workload.

While these tasks are essential, they can be time-consuming and take away from more critical work responsibilities. Machine learning has the potential to automate many administrative tasks, freeing up employees to focus on more strategic work. In this article, we will explore how machine learning can help employees focus on their work by answering administrative emails, with real-world examples.

Understanding How Machine Learning Works

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Source: Great Learning

Machine learning is a subset of artificial intelligence that involves teaching computers to learn and improve from experience without being explicitly programmed. In essence, machine learning algorithms learn from data and make predictions or decisions based on that data. Machine learning can be used for a wide range of applications, including email management.

Machine learning is a type of artificial intelligence (AI) that enables computers to learn from data and improve their performance on specific tasks over time. Here is a brief overview of how machine learning works:

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  • Data Collection: The first step in machine learning is to collect data. This can be any type of data, such as text, images, or numerical data. The data should be relevant to the problem you’re trying to solve and should be representative of the real-world scenarios the model will encounter.

  • Data Preprocessing: Once you have collected the data, you need to preprocess it to prepare it for use in a machine learning algorithm. This may involve cleaning the data, removing irrelevant features, and transforming the data into a format that can be used by the algorithm.

  • Model Selection: There are many types of machine learning algorithms, and selecting the right one for your problem is important. Some common types of algorithms include decision trees, neural networks, and support vector machines. The selection process depends on the type of data you have, the problem you’re trying to solve, and the performance metrics you’re targeting.

  • Training the Model: Once you’ve selected a model, you need to train it on your data. This involves feeding the algorithm your preprocessed data and allowing it to learn from the patterns and relationships in the data. During training, the model adjusts its parameters to minimize errors and improve its accuracy.

  • Evaluation: Once the model has been trained, it’s important to evaluate its performance. This involves testing the model on a set of data that it hasn’t seen before and measuring its accuracy, precision, recall, and other performance metrics. If the model isn’t performing well, you may need to adjust the model or collect more data to improve its accuracy.

  • Deployment: After the model has been trained and evaluated, it’s time to deploy it. This involves integrating the model into your application or system so that it can be used to solve real-world problems.

  • Monitoring: Finally, it’s important to monitor the performance of the model over time. This can help you identify issues or opportunities for improvement and ensure that the model is still performing well on new data.

Machine learning is a powerful tool that enables computers to learn from data and improve their performance over time. By understanding how machine learning works, you can apply it to a wide range of applications and solve complex problems more efficiently and accurately.

Machine Learning is Helping Employees Focus on Their Work

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Source: Consultancy.uk

Administrative tasks, such as email management, can be time-consuming and take away from more critical work responsibilities. By automating these tasks with machine learning, employees can focus on more strategic work, such as developing new products or services, improving customer experiences, or driving revenue growth. Machine learning can help employees focus on their work by:

Machine learning can automate repetitive tasks, such as answering routine emails or scheduling meetings, freeing up time for more critical work.

It can also analyze large volumes of data to identify patterns and trends, improving the accuracy of email responses and reducing errors.

Machine learning algorithms can learn from past email interactions to automate responses to similar emails in the future, improving efficiency and reducing response times.

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Use Cases of Machine Learning Applications in Email Management

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Source: Science Direct

There are several examples of machine learning applications in email management. Here are a few examples:

Smart Reply by Google

Smart Reply by Google is an AI-powered feature that suggests responses to emails. When a user receives an email, Smart Reply analyzes the email’s content and provides several suggested responses that the user can choose from. Smart Reply uses natural language processing (NLP) and machine learning algorithms to generate responses that are relevant and personalized to the email’s content.

X.AI

X.AI is an AI-powered virtual assistant that helps schedule meetings. When a user wants to schedule a meeting, they can copy X.AI on the email thread, and X.AI will take over the conversation. X.AI uses NLP and machine learning algorithms to understand the context of the email conversation and find a mutually convenient time for the meeting.

Salesforce Einstein

Salesforce Einstein is an AI-powered platform that integrates with Salesforce to automate customer interactions. Einstein can analyze customer emails and provide suggested responses that are personalized to the customer’s needs. Einstein can also automate follow-up emails and provide insights into customer behavior and preferences.

Best Practices for Implementing Machine Learning in Email Management

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Source: Corpnce

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Conclusion

Machine learning has the potential to transform email management, freeing up employees to focus on more strategic work. With AI-powered features such as Smart Reply by Google, X.AI, and Salesforce Einstein, employees can automate routine tasks, such as answering emails and scheduling meetings. However, it is important to approach machine learning with caution and to follow best practices when implementing it in email management. By starting small, using high-quality data, focusing on user experience, and monitoring performance, organizations can maximize the benefits of machine learning while minimizing its risks.

Machine learning can help employees focus on their work by automating administrative tasks such as email management. With the increasing adoption of machine learning in business, it is crucial for organizations to explore and leverage its potential to improve efficiency, accuracy, and productivity. By embracing machine learning, organizations can enhance their employees’ experience, improve customer satisfaction, and gain a competitive advantage in the marketplace.

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Next-gen chips, Amazon Q, and speedy S3

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AWS re:Invent, which has been taking place from November 27 and runs to December 1, has had its usual plethora of announcements: a total of 21 at time of print.

Perhaps not surprisingly, given the huge potential impact of generative AI – ChatGPT officially turns one year old today – a lot of focus has been on the AI side for AWS’ announcements, including a major partnership inked with NVIDIA across infrastructure, software, and services.

Yet there has been plenty more announced at the Las Vegas jamboree besides. Here, CloudTech rounds up the best of the rest:

Next-generation chips

This was the other major AI-focused announcement at re:Invent: the launch of two new chips, AWS Graviton4 and AWS Trainium2, for training and running AI and machine learning (ML) models, among other customer workloads. Graviton4 shapes up against its predecessor with 30% better compute performance, 50% more cores and 75% more memory bandwidth, while Trainium2 delivers up to four times faster training than before and will be able to be deployed in EC2 UltraClusters of up to 100,000 chips.

The EC2 UltraClusters are designed to ‘deliver the highest performance, most energy efficient AI model training infrastructure in the cloud’, as AWS puts it. With it, customers will be able to train large language models in ‘a fraction of the time’, as well as double energy efficiency.

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As ever, AWS offers customers who are already utilising these tools. Databricks, Epic and SAP are among the companies cited as using the new AWS-designed chips.

Zero-ETL integrations

AWS announced new Amazon Aurora PostgreSQL, Amazon DynamoDB, and Amazon Relational Database Services (Amazon RDS) for MySQL integrations with Amazon Redshift, AWS’ cloud data warehouse. The zero-ETL integrations – eliminating the need to build ETL (extract, transform, load) data pipelines – make it easier to connect and analyse transactional data across various relational and non-relational databases in Amazon Redshift.

A simple example of how zero-ETL functions can be seen is in a hypothetical company which stores transactional data – time of transaction, items bought, where the transaction occurred – in a relational database, but use another analytics tool to analyse data in a non-relational database. To connect it all up, companies would previously have to construct ETL data pipelines which are a time and money sink.

The latest integrations “build on AWS’s zero-ETL foundation… so customers can quickly and easily connect all of their data, no matter where it lives,” the company said.

Amazon S3 Express One Zone

AWS announced the general availability of Amazon S3 Express One Zone, a new storage class purpose-built for customers’ most frequently-accessed data. Data access speed is up to 10 times faster and request costs up to 50% lower than standard S3. Companies can also opt to collocate their Amazon S3 Express One Zone data in the same availability zone as their compute resources.  

Companies and partners who are using Amazon S3 Express One Zone include ChaosSearch, Cloudera, and Pinterest.

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Amazon Q

A new product, and an interesting pivot, again with generative AI at its core. Amazon Q was announced as a ‘new type of generative AI-powered assistant’ which can be tailored to a customer’s business. “Customers can get fast, relevant answers to pressing questions, generate content, and take actions – all informed by a customer’s information repositories, code, and enterprise systems,” AWS added. The service also can assist companies building on AWS, as well as companies using AWS applications for business intelligence, contact centres, and supply chain management.

Customers cited as early adopters include Accenture, BMW and Wunderkind.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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HCLTech and Cisco create collaborative hybrid workplaces

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Digital comms specialist Cisco and global tech firm HCLTech have teamed up to launch Meeting-Rooms-as-a-Service (MRaaS).

Available on a subscription model, this solution modernises legacy meeting rooms and enables users to join meetings from any meeting solution provider using Webex devices.

The MRaaS solution helps enterprises simplify the design, implementation and maintenance of integrated meeting rooms, enabling seamless collaboration for their globally distributed hybrid workforces.

Rakshit Ghura, senior VP and Global head of digital workplace services, HCLTech, said: “MRaaS combines our consulting and managed services expertise with Cisco’s proficiency in Webex devices to change the way employees conceptualise, organise and interact in a collaborative environment for a modern hybrid work model.

“The common vision of our partnership is to elevate the collaboration experience at work and drive productivity through modern meeting rooms.”

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Alexandra Zagury, VP of partner managed and as-a-Service Sales at Cisco, said: “Our partnership with HCLTech helps our clients transform their offices through cost-effective managed services that support the ongoing evolution of workspaces.

“As we reimagine the modern office, we are making it easier to support collaboration and productivity among workers, whether they are in the office or elsewhere.”

Cisco’s Webex collaboration devices harness the power of artificial intelligence to offer intuitive, seamless collaboration experiences, enabling meeting rooms with smart features such as meeting zones, intelligent people framing, optimised attendee audio and background noise removal, among others.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: Cisco, collaboration, HCLTech, Hybrid, meetings

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Canonical releases low-touch private cloud MicroCloud

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Canonical has announced the general availability of MicroCloud, a low-touch, open source cloud solution. MicroCloud is part of Canonical’s growing cloud infrastructure portfolio.

It is purpose-built for scalable clusters and edge deployments for all types of enterprises. It is designed with simplicity, security and automation in mind, minimising the time and effort to both deploy and maintain it. Conveniently, enterprise support for MicroCloud is offered as part of Canonical’s Ubuntu Pro subscription, with several support tiers available, and priced per node.

MicroClouds are optimised for repeatable and reliable remote deployments. A single command initiates the orchestration and clustering of various components with minimal involvement by the user, resulting in a fully functional cloud within minutes. This simplified deployment process significantly reduces the barrier to entry, putting a production-grade cloud at everyone’s fingertips.

Juan Manuel Ventura, head of architectures & technologies at Spindox, said: “Cloud computing is not only about technology, it’s the beating heart of any modern industrial transformation, driving agility and innovation. Our mission is to provide our customers with the most effective ways to innovate and bring value; having a complexity-free cloud infrastructure is one important piece of that puzzle. With MicroCloud, the focus shifts away from struggling with cloud operations to solving real business challenges” says

In addition to seamless deployment, MicroCloud prioritises security and ease of maintenance. All MicroCloud components are built with strict confinement for increased security, with over-the-air transactional updates that preserve data and roll back on errors automatically. Upgrades to newer versions are handled automatically and without downtime, with the mechanisms to hold or schedule them as needed.

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With this approach, MicroCloud caters to both on-premise clouds but also edge deployments at remote locations, allowing organisations to use the same infrastructure primitives and services wherever they are needed. It is suitable for business-in-branch office locations or industrial use inside a factory, as well as distributed locations where the focus is on replicability and unattended operations.

Cedric Gegout, VP of product at Canonical, said: “As data becomes more distributed, the infrastructure has to follow. Cloud computing is now distributed, spanning across data centres, far and near edge computing appliances. MicroCloud is our answer to that.

“By packaging known infrastructure primitives in a portable and unattended way, we are delivering a simpler, more prescriptive cloud experience that makes zero-ops a reality for many Industries.“

MicroCloud’s lightweight architecture makes it usable on both commodity and high-end hardware, with several ways to further reduce its footprint depending on your workload needs. In addition to the standard Ubuntu Server or Desktop, MicroClouds can be run on Ubuntu Core – a lightweight OS optimised for the edge. With Ubuntu Core, MicroClouds are a perfect solution for far-edge locations with limited computing capabilities. Users can choose to run their workloads using Kubernetes or via system containers. System containers based on LXD behave similarly to traditional VMs but consume fewer resources while providing bare-metal performance.

Coupled with Canonical’s Ubuntu Pro + Support subscription, MicroCloud users can benefit from an enterprise-grade open source cloud solution that is fully supported and with better economics. An Ubuntu Pro subscription offers security maintenance for the broadest collection of open-source software available from a single vendor today. It covers over 30k packages with a consistent security maintenance commitment, and additional features such as kernel livepatch, systems management at scale, certified compliance and hardening profiles enabling easy adoption for enterprises. With per-node pricing and no hidden fees, customers can rest assured that their environment is secure and supported without the expensive price tag typically associated with cloud solutions.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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Tags: automation, Canonical, MicroCloud, private cloud

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