Categoria: Management

Are you overloaded with chores that are trivial and take a huge amount of time in the functioning of your business? Well, this is where hyperautomation comes into play and allows handling such extended and complicated business rules. This only translates to the next level of automation, or, in other words, a set of technologies undergoing revolution to revolutionize aspects of efficient working.

Picture intelligent robots working together with data analysis and machine learning to be able to orchestrate complex processes. The ability is to make all of this a reality through platforms of hyperautomation, which enable businesses to realize breakthrough results.

But is it worthwhile? It’s all about the ROI. Business managers will be in a position to show how hyperautomation impacts business operations so that they can make data-driven decisions and realize the actual potential of this transformational technology.

Cost Savings

Information technology (IT) isn’t all about fancy gadgets and troubleshooting; rather, it’s about wanting to streamline your business. Here’s how a solid IT strategy—one like how most managed service providers would do or go about this—does this:

Streamlined Operations: Automation eliminates what may be considered conventional activities, hence freeing more time for your staff to burrow into literally cream jobs, representing less labor cost and higher productivity.

Fewer Errors, Lower Costs: Proactive maintenance of systems will help detect and nip problems in the bud before snowballing into more costly errors. This sets you up to have smooth operations and reduces the risk of experiencing frustrating downtimes.

Resource Efficiency: A planned strategy for your IT enables your business to optimize its resources. You will efficiently use those at your disposal while cutting out unnecessary costs and ensuring a good return on investment.

Better Efficiency

Efficiency would be the key to reaping maximum results. Three important areas to consider are: lean processes, speed and productivity, and scaling. Lean processes make the workflow smooth with the help of automation. This could eradicate possible losses of effort and give a flow to the work. Better handling of tasks is bound to bring an increase in productivity, ensuring that you accomplish much within a short span of time. Finally, scalability ensures that your operation has the ability to scale with growth without running into inefficiencies or a spike in costs. This focus will help drive your business at full throttle.

To Know More, Read Full Article @ https://ai-techpark.com/hyperautomation-platforms-for-automation/ 

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DataStrike, a leader in data infrastructure services, announced the appointment of Carlo Finotti as its senior vice president of service delivery. A former client-side CIO and COO, Finotti is responsible for defining the strategic vision for DataStrike’s services offerings and technology innovation and building the entirely onshore team responsible for delivering those services to the company’s 200+ clients. DataStrike is the largest onshore provider of data infrastructure services for small- and mid-sized businesses (SMBs). Its end-to-end portfolio includes data management, cloud, enterprise application management and analytics.

“IT is undergoing a major shift given the proliferation of technologies like AI and cloud – with more on the way – and companies are trying to figure it all out, usually without the necessary resources,” said Finotti, senior vice president of service delivery at DataStrike. “They’re looking for a strong partner to guide and support them from strategy to ongoing management. They want to be able to sleep at night, and my entire focus is building the team and delivering the caliber of services that can give them exactly that peace of mind.”

Finotti comes to DataStrike from IT services provider XL.net, where he served as head of operations. Prior to XL.net, he served as CIO for North American Dental Group, where he led all facets of the organization’s IT function — from application delivery and software development to business intelligence and process automation —serving 240+ affiliated dental practices. Finotti began his career at rue21, leading IT operations and supporting the company’s 1,100+ retail stores.

Read Full News @ https://ai-techpark.com/datastrike-appoints-senior-vice-president-of-service-delivery/ 

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NETSCOUT SYSTEMS, INC. (NASDAQ: NTCT), a leading provider of performance management, cybersecurity, and DDoS attack protection solutions, today introduced its new suite of Business Edge Observability products, including the nGenius Edge Sensor and Remote InfiniStreamNG solutions to deliver IT observability for remote locations at the digital edge. As the prevalence and importance of mission-critical applications and services expand at remote sites, like retail stores, manufacturing facilities, banks, utility companies, hospitals, and government offices, proactive, deep-dive observability is more critical in reducing business risk.

Recent research from the Uptime Institute points to the painful costs of outages. 16% of respondents stated their most recent outage cost more than $1 million, and 54% said costs exceeded $100,000, reinforcing the need for automating IT operations so mission-critical applications and data can be available locally.

“With the rise of edge computing, IoT solutions, front-line interactions, perimeter threats, and hybrid workers, detailed visibility into remote resources, exchanges, and application experiences is more critical than ever,” said Mark Leary, research director, network analytics and automation, IDC. “Key functions such as scalable deep packet inspection and synthetic transaction testing provide comprehensive views and valuable control mechanisms for the digital infrastructure, bolstering operational efficiency, worker productivity, customer satisfaction, security posture, and, ultimately, financial performance.”

Read Full News @ https://ai-techpark.com/netscout-expands-it-observability-for-the-digital-edge/ 

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Rise of Low-Code and No-Code

Leslie, can you please introduce yourself and share your experience as a CEO and Founder at TurinTech?

As you say, I’m the CEO and co-founder at TurinTech AI. Before TurinTech came into being, I worked for a range of financial institutions, including Credit Suisse and Bank of America. I met the other co-founders of TurinTech while completing my Ph.D. in Computer Science at University College London. I have a special interest in graph theory, quantitative research, and efficient similarity search techniques.

While in our respective financial jobs, we became frustrated with the manual machine learning development and code optimization processes in place. There was a real gap in the market for something better. So, in 2018, we founded TurinTech to develop our very own AI code optimization platform.

When I became CEO, I had to carry out a lot of non-technical and non-research-based work alongside the scientific work I’m accustomed to. Much of the job comes down to managing people and expectations, meaning I have to take on a variety of different areas. For instance, as well as overseeing the research side of things, I also have to understand the different management roles, know the financials, and be across all of our clients and stakeholders.

One thing I have learned in particular as a CEO is to run the company as horizontally as possible. This means creating an environment where people feel comfortable coming to me with any concerns or recommendations they have. This is really valuable for helping to guide my decisions, as I can use all the intel I am receiving from the ground up.

To set the stage, could you provide a brief overview of what code optimization means in the context of AI and its significance in modern businesses?

Code optimization refers to the process of refining and improving the underlying source code to make AI and software systems run more efficiently and effectively. It’s a critical aspect of enhancing code performance for scalability, profitability, and sustainability.

The significance of code optimization in modern businesses cannot be overstated. As businesses increasingly rely on AI, and more recently, on compute-intensive Generative AI, for various applications — ranging from data analysis to customer service — the performance of these AI systems becomes paramount.

Code optimization directly contributes to this performance by speeding up execution time and minimizing compute costs, which are crucial for business competitiveness and innovation.

For example, recent TurinTech research found that code optimization can lead to substantial improvements in execution times for machine learning codebases — up to around 20% in some cases. This not only boosts the efficiency of AI operations but also brings considerable cost savings. In the research, optimized code in an Azure-based cloud environment resulted in about a 30% cost reduction per hour for the utilized virtual machine size.

To Know More, Read Full Interview @ https://ai-techpark.com/ai-tech-interview-with-leslie-kanthan/ 

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Programming is the backbone of modern software development, as this aids in driving creation when creating innovative applications and systems that eventually power the digital world. However, the coding process can be complicated and challenging, but thankfully, with the development of modern software, software developers (DEV) can easily navigate intricate syntax, troubleshoot errors, and manage large codebases. Therefore, with the introduction of AI, coding assistants have emerged as valuable partners that can change the programming game forever and enhance the coding experience for DEV.

In today’s AITechPark article, we will explore the top five AI Coding Assistant tools, especially those made for developers, to streamline every process and boost workflow.

Codiga

The first AI coding assistant tool on our list is Codiga, which focuses on elevating the coding experience by providing intelligent support, code optimizations, and autocomplete suggestions that enable developers to write codes more efficiently. The tool analyzes code for potential errors and vulnerabilities, allowing users to identify and fix the problem before it is set out for production. Codiga excels at code refactoring as it supports more than 12 programming languages, including PHP, C++, Java, and Python, enhancing performance and readability. Regarding pricing, Codiga offers free individual plans, while the paid subscription starts at $14 per month.

Amazon CodeWhisper

Amazon’s CodeWhisperer provides real-time suggestions for developers, allowing full functions tailored to existing code that will help streamline their coding experience and make it easier to encounter unfamiliar APIs. The platform is identified as an open-source suggestion providing information related to the projects, such as URLs and licenses for easy reviewing of codes and their attributions. Beyond code suggestions, Amazon CodeWhisper facilitates comment completions for increased documentation. The tool supports popular programming languages such as Python, Java, and JavaScript and seamlessly integrates with IDEs such as Visual Studio Code, AWS Cloud9, and PyCharm. Amazon CodeWhisper is free for individual users, but the professional plan starts at $19 per month.

With the advancement in technology, AI coding assistant tools play an essential role in the software development industry, especially with continuous learning and improvement. These tools have the potential to reshape the coding experience and foster innovation and code excellence. Hence, by embracing the above AI Coding Assistants’ software, developers and programmers can overcome the challenges related to coding and enhance their skills to create high-quality software solutions.

To Know More, Read Full Article @ https://ai-techpark.com/the-top-five-best-ai-coding-assistant-tools-in-2024/

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AI bias has the potential to cause significant damage to cybersecurity, especially when it is not controlled effectively. It is important to incorporate human intelligence alongside digital technologies to protect digital infrastructures from causing severe issues.

AI technology has significantly evolved over the past few years, showing a relatively nuanced nature within cybersecurity. By tapping into vast amounts of information, artificial intelligence can quickly retrieve details and make decisions based on the data it was trained to use. The data can be received and used within a matter of minutes, which is something that human intelligence might not be able to do.

With that said, the vast databases of AI technologies can also lead the systems to make ethically incorrect or biased decisions. For this reason, human intelligence is essential in controlling potential ethical errors of AI and preventing the systems from going rogue. This article will discuss why AI technology cannot fully replace humans and why artificial intelligence and human intelligence should be used side-by-side in security systems.

Inherent Limitations of AI

AI technology has significantly improved throughout the years, especially regarding facial recognition and other security measures. That said, while its recognition abilities have become superior, it is still lacking when it comes to mimicking human judgment.

Human intelligence is influenced by factors like intuition, experience, context, and values. This allows humans to make decisions while considering different perspectives, which may or may not be present in a data pool. As AI systems are still far from being perfectly trained with all the information in the world, they can present errors in judgment that could have otherwise not happened with human intelligence.

AI data pools also draw information from “majorities,” registering through information that was published decades ago. Unless effectively trained and updated, it may be influenced by information that is now irrelevant. For instance, AI could unfairly target specific groups subjected to stereotypes in the past, and the lack of moral compass could create injustice in the results.

One significant problem of using AI as the sole system for data gathering is that it can have substantial limitations in fact-checking. Data pools are updated day by day, which can be problematic as AI systems can take years to train fully. AI can wrongfully assume that a piece of information is false, even though the data is correct. Without human intelligence to fact-check the details, the risk of using incorrect data might cause someone to misinterpret crucial information.

Unfortunately, AI bias can cause significant disruptions within an algorithm, making it pull inaccurate or potentially harmful information from its data pool. Without human intelligence to control it, not only can it lead to misinformation, but it could also inflict severe privacy and security breaches. Hybrid systems could be the answer to this because they are better at detecting ethical issues or errors.

To Know More, Read Full Article @ https://ai-techpark.com/human-role-in-ai-security/ 

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Data warehouses have an older design, which becomes stifling in a world where information and data escalate at an exponential pace. Just try to picture hundreds of hours dedicated to managing infrastructure, fine-tuning the clusters to address the workload variance, and dealing with significant upfront costs before you get a chance to analyze the data.

Unfortunately, this is the best that one can expect out of traditional data warehousing methodologies. For data architects, engineers, and scientists, these burdens become a thorn in their side, reducing innovation by 30% and slowing the process of gaining insights from increasingly large data sets by up to 50%.

Serverless Data Warehousing: A Revolution for the Modern Data Master

But what if there was a better way? Serverless data warehousing is a new concept, and it provides a revolutionary solution away from the chaining constraints that come with managing complex infrastructure.  Think about the future, where servers are self-provisioning and can scale up or down based on the load. A world where one pays only for the resources consumed or needed, excluding hefty charges and data investments.

Serverless data warehousing opens up this very possibility. By leveraging the power of the cloud, data engineers or scientists can focus on what truly matters: turning collected information into insights from which organizations can make relevant decisions and gain benefits.

Building a B2B Serverless Data Warehouse on AWS: Recommended Design Patterns

As data architects and engineers, we need to see the importance of proper data pipelines for solid B2B analytics and insights. In this case, serverless data warehousing on AWS remains a suitable solution due to its flexibility and affordability. Now, let us explore the proposed design patterns for creating your B2B serverless data warehousing architecture.

Data Ingestion Pipeline

The building block is to create a proper data ingestion process that feeds into the ‘real-time’ layer. Here, the AWS Kinesis Firehose stands out. It is a fully managed service that can integrate streaming data in real-time from B2B sources like your CRM or ERP system. Firehose consumes the data and directs it to storage layer S3, which is a low-cost storage layer for storing raw and processed data.

Data Transformation and Orchestration

In most cases, transformations are made when extracting value from raw data. Enter AWS Glue as the serverless ETL (extract, transform, load) solution. Glue allows you to fulfill data transformations with Python scripts and, at the same time, manage all the stages of data ingestion. This helps in the proper flow of data from B2B sources to the data warehouse without any hitches.

Data Storage and Catalog

Amazon S3 can be considered the foundation of your data store or data lake. This fast-scaled-out object storage service is an economical solution to store all the B2B data, both in its raw and transformed forms. Also, manage and use the AWS Glue Data Catalog effectively. This centralized metadata repository reduces the problem of finding your data by making data search easy by presenting a list of the data stored in S3 in a catalog.

To Know More, Read Full Article @ https://ai-techpark.com/serverless-data-warehousing-in-aws/ 

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Joel, how have the unique challenges faced by small and medium-sized enterprises influenced their adoption of AI in identity management and security practices?

So we commission a biannual small to medium-sized enterprise (SME) IT Trends Report that looks specifically at the state of SME IT. This most recent version shows how quickly AI has impacted identity management and highlights that SMEs are kind of ambivalent as they look at AI. IT admins are excited and aggressively preparing for it—but they also have significant concerns about AI’s impact. For example, nearly 80% say that AI will be a net positive for their organization, 20% believe their organizations are moving too slowly concerning AI initiatives, and 62% already have AI policies in place, which is pretty remarkable considering all that IT teams at SMEs have to manage. But SMEs are also pretty wary about AI in other areas. Nearly six in ten (62%) agree that AI is outpacing their organization’s ability to protect against threats and nearly half (45%) agree they’re worried about AI’s impact on their job. I think this ambivalence reflects the challenges of SMEs evaluating and adopting AI initiatives – with smaller teams and smaller budgets, SMEs don’t have the budget, training, and staff their enterprise counterparts have. But I think it’s not unique to SMEs. Until AI matures a little bit, I think that AI can feel more like a distraction.

Considering your background in identity, what critical considerations should SMEs prioritize to protect identity in an era dominated by AI advancements?

I think caution is probably the key consideration. A couple of suggestions for getting started:

Data security and privacy should be the foundation of any initiative. Put in place robust data protection measures to safeguard against breaches like encryption, secure access controls, and regular security audits. Also, make sure you’re adhering to existing data protection regulations like GDPR and keep abreast of impending regulations in case new controls need to be implemented to avoid penalties and legal issues.

When integrating AI solutions, make sure they’re from reputable sources and are secure by design. Conduct thorough risk assessments and evaluate their data handling practices and security measures. And for firms working more actively with AI, research and use legal and technical measures to protect your innovations, like patents or trademarks.

With AI, it’s even more important to use advanced identity and authentication management (IAM) solutions so that only authorized individuals have access to sensitive data. Multi-factor authentication (MFA), biometric verification, and role-based access controls can significantly reduce that risk. Continuous monitoring systems can help identify and thwart AI-related risks in real time, and having an incident response plan in place can help mitigate any security breaches.

Lastly, but perhaps most importantly, make sure that the AI technologies are used ethically, respecting privacy rights and avoiding bias. Developing an ethical AI framework can guide your decision-making process. Train employees on the importance of data privacy, recognizing phishing attacks, and secure handling of information. And be prepared to regularly update (and communicate!) security practices given the evolving nature of AI threats.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-joel-rennich/ 

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In the data-driven world, data visualization is the ultimate BI tool that takes large datasets from numerous sources, aiding data visualization engineers to analyze data and visualize it into actionable insights. In the data analysis process, data visualization is the final chapter that includes a variety of graphs, charts, and histograms in the form of reports and dashboards to make the data more friendly and understandable.

Therefore, to create a data analysis report that stands out, AITechPark has accumulated the top five most popular data visualization tools. These data visualization tools will assist data visualization engineers, further help businesses understand their needs, and provide real-time solutions to streamline the business process.

Tableau

Tableau is one of the most popular data visualization tools used by data scientists and analysts to create customized charts and complex visualizations. The users can connect the data sources, which include databases, spreadsheets, cloud services, and other big data references, allowing them to import and transform data for their analysis. However, Tableau is not the right tool for data creation and preprocessing, as it does not support spreadsheet tools for multi-layered operations. Tableau is expensive when compared to other data visualization tools on the market. The cost of Tableau subscriptions varies; for instance, Tableau Public and Tableau Reader are free, while Tableau Desktop is available for $70/user/month, Tableau Explorer for $42/user/month, and Tableau Viewer for $15/user/month.

LookerML

LookerML is a powerful tool that helps data teams visualize capabilities and data inputs and create a powerful modeling layer that allows them to turn SQL into object-oriented code. To keep the workflow up and running without any challenges, teams can take advantage of Looker Blocks, a robust library of analytics code. However, beginners will still need some apprenticeship to learn the art of data visualization before working with Looker, as it provides complicated tools that might be difficult to understand at first glance. The tool also comes with pre-defined built-in visualizations that have some fixed standards and specifications, giving limited options for customization. The pricing varies from $5,000 per month to $7,000 per month, depending on the size and usage of the tool.

With the growing reliance on data volume available in the market, organizations have started realizing the power of data analytics, which can source real-time data internally and externally as a predictive and prescriptive source. However, to improve data analysis and visualization, engineers are required to select the right tool that aligns with their business goals and needs. Opting for the right tool will help in curating the vast amount of information without human error, eventually aiding in streamlining businesses.

To Know More, Read Full Article @ https://ai-techpark.com/top-five-best-data-visualization-tools-in-2024/ 

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On an average, HR managers and recruiters go through a resume in almost six to seven seconds. It’s a really short time and shows that your resume must be outstanding and unique to catch their eye. Using difficult fonts, flashy designs, and a bad layout can become a reason for you to miss out an opportunity, even if you are well-qualified for that role.

Your resume tells about your past work history, skills, hobbies, competencies, etc. Just like many other industries, Artificial Intelligence (AI) can help you with writing your resume. Most people make silly mistakes or are unable to include all necessary information about themselves in their resume. An AI job search tool can help craft a flawless resume for you apart from just searching jobs.

How AI Tools Transform Resume Writing?

Instead of doing it by yourself, when you take the help of AI, it will ensure that your resume has the right format and headings.

Also, AI goes through the job posting and optimizes your resume based on it so that you have an edge over other candidates. This is how AI is transforming the art of writing resumes.

Suggest Ideal Templates

Most people choose a template for their resume and keep using it for all future applications. This is not the correct way because recruitment trends keep changing and not all organizations are looking for a similar thing.

A template may be good for a particular job opportunity but it doesn’t mean that it will work everywhere. AI tools suggest templates depending on the company you’re applying to. The right template will ensure clarity and visual appeal, highlighting relevant skills to impress HRs.

Analyzes Job Descriptions & Optimizes Your Resume Accordingly

You should never use the same resume for different job opportunities as every role demands different skills. AI tools carefully go through job descriptions and understand the requirements. They optimize your resume with several keywords and skills that recruiters are looking for.

Also, these tools will place relevant terms in such a way that recruiters surely see them while going through your resume. Using a single resume does not work anymore and you should use AI tools if you want a perfect resume based on the role you’re applying for.

Focuses on Your Top Skills & Achievements

Many people don’t put emphasis on their top skills and previous achievements when creating their resume. Recruiters won’t put in the effort to read every single word of your resume and it’s your duty to showcase your skills and experience in a way that they have high visibility.

When you use an AI job search tool, it will help you in highlighting the in-demand skills you have and your past work history relevant to the role. Even if you are well-qualified for a job, if your resume does not showcase your skills properly, you’ll miss out.

To Know More, Read Full Article @ https://ai-techpark.com/ai-elevates-resume-crafting/ 

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