Latest news with #MachineLearning
Hans India
18 hours ago
- Business
- Hans India
Robotics education:A 21st-century catalyst for innovation
As Artificial Intelligence and Machine Learning reshape the digital world, their real-world impact depends on physical technologies—most notably, robotics. Robotics education is emerging as a crucial bridge between theoretical innovation and practical application. From smart manufacturing to national security, robotics is key to transforming India from a service-based economy to a technology-exporting powerhouse. With rising demand, robust government initiatives, and growing educational focus, robotics offers both financial promise and professional fulfillment. For today's students and professionals, investing in robotics education means leading tomorrow's tech evolution—far beyond just coding and programming The emergence of new-age digital technologies, especially in Artificial Intelligence (AI) and Machine Learning (ML) has led the world to emphasize these disciplines in a new light. However, while these technologies are projected to become the backbone of technological evolution in the coming years, their supremacy can only be established with physical means. For instance, AI's pinnacle of success could only be achieved through solutions that address physical challenges such as manufacturing, security, or construction to name a few. However, to undertake these challenges, integrating advanced robotics would be crucial. For India's abundant tech professionals, this highlights a real-life catalyst for driving their professional ambitions forward while also contributing to the country's overall development. India is already placing much emphasis on AI, however, to become a tech-sovereign nation the likes of Robotics would have to be supported massively. This can only be done via motivated participation in these disciplines from Indian talents, who must look beyond coding and programming to make India from a net service exporter to a net technology exporter. Additionally, the current state of Robotics in India also highlights significant financial incentives for a professional, with the added scope of becoming drivers of the industry in the country. At the same time, traditional colleges and universities are integrating Robotics as part of their curriculums. Furthermore, initiatives such as the Atal Tinkering Labs have proved to be a catalyst in this regard, being supplemented by the National Education Policy (NEP) 2020 that has detailed robotics and STEM education in K-12 education space, creating a funnel for more advanced educational & research platforms. The objective of these introductions is clear — to establish a robust Robotics ecosystem in the country, that can be used by millions of talents, professionals and other stakeholders to drive the country's vision in the future, while also assisting them in garnering professional excellence and financial assistance. Robotics education: A catalyst for professional aspirations? While the concepts of Robotics are not really new, the emergence of AI is transforming it dramatically. The world is integrating the two technologies to create solutions that are not only superior but also transform how we perceive work irrespective of its functionalities. This is where trained professionals in contemporary robotics education will be of particular value to the national interest, while also bridging the present gap in the space. At present, a Robotics Engineer in India with a mid-level experience of 5 years, can earn up to 10 lakh per annum. However, it is a serious challenge for organizations to get professionals with ample experience and capabilities in the domain, offering a unique opportunity for talents. This concern can be essentially addressed by establishing a robust education in Robotics and its intricacies around the country. The Indian government and a few state governments are already working on making this a reality, however, the push must also come from traditional educational bodies like colleges and universities. However, this remains a long-term plan, and to address this concern at present, professionals can look to garner valuable training or knowledge via upskilling. The Indian EdTech space has already pointed this aspect out and has been offering supplementary courses that will help professionals hone their skills as per the industrial requirements. This can help professionals to integrate themselves into the industry by equipping them with era-appropriate knowledge, and capabilities and most importantly, creating a supply line of talents to the industry stakeholders. Looking Ahead The foundations are carefully being laid by the Indian public and private sectors for the creation of a robust Robotics ecosystem in the country. However, the long-term efficacy of the efforts will depend on the talents themselves, and their innovation will be the primary catalysts for evolving the space into a full-fledged sector to drive India's national ambitions. In the coming decade, more Indian talents must look beyond coding and programming, skills that would soon be engulfed with AI coming in as a more time and cost-effective solution and instead look for more innovative technological skills. Robotics would be the perfect suitor in this aspect and could prove to be the catalyst of the professional ambitions and financial incentives Indian talents are looking for in the long term.
Entrepreneur
4 days ago
- Business
- Entrepreneur
Indian Retail Sector Embraces AI and Omnichannel Strategies to Drive Growth: Survey
Social media has emerged as a vital touchpoint, with 72% of businesses relying on it as their primary channel for customer discovery and engagement. You're reading Entrepreneur India, an international franchise of Entrepreneur Media. The Indian retail landscape is experiencing a remarkable shift, with 60% of businesses planning to adopt Artificial Intelligence (AI) and Machine Learning (ML) by 2030. Additionally, 44% are counting on AI-driven personalisation to enhance customer experiences, as revealed by a recent survey from Zoho. This study gathered insights from over 2,700 micro, small, and medium enterprises (MSMEs) throughout India. "Indian MSMEs are creating a pivotal shift in the retail sector," said Prashant Ganti, Vice President, Global Product Strategy, Finance and Operations Platform, Zoho. "Our study shows that embracing a seamless online and offline selling strategy, coupled with the adoption of the right tools, including AI, to enhance the buyer experience, is essential for growth and future-proofing a retail business." The survey shows that six out of ten MSMEs have already implemented an omnichannel strategy, utilising both physical and digital retail avenues. Among these, 75% believe this combined approach allows them to connect with more customers, while 68% report generating equal revenue from both channels. Moreover, 82% of offline retailers expressed a strong interest in expanding their digital presence, with marketplaces being the top choice for online sales. Despite the digital push, physical stores continue to play a vital role. The report indicates that 71% of consumers still prefer shopping in-store to physically inspect products. Retailers highlighted personalised service (66%) and immediate product access (59%) as significant benefits of in-person shopping experiences. Interestingly, nearly half of the retail MSMEs considering a physical location are leaning towards starting with pop-up stores due to their flexibility and cost-effectiveness. However, challenges are still on the horizon. About 60% of respondents pointed to logistics and supply chain hurdles, while 57% mentioned the high operational costs associated with maintaining physical stores. Customer expectations are changing fast. 57% of retailers say that fast delivery is their customers' top demand, with many noting a rising desire for same-day delivery. Convenience is a big deal too, with 82% of shoppers valuing it, while 71% are drawn in by competitive pricing. When it comes to in-store experiences, 70% of retailers now accept mobile payments, and others are enhancing the shopping journey with tablets (66%) and kiosks (50%). Social media has emerged as a vital touchpoint, with 72% of businesses relying on it as their primary channel for customer discovery and engagement. Platforms like WhatsApp, Instagram, and Facebook are also being tapped for gathering customer feedback and driving direct sales. In light of these shifting demands, Zoho has rolled out an upgraded version of its commerce platform, Zoho Commerce. This new platform boasts a sleek user interface, mobile app support, and features like multi-currency checkout, digital downloads, loyalty programs, and cart recovery. It also facilitates social selling through platforms like WhatsApp and offers tools for B2B businesses to manage quotes, credit limits, and negotiations. As technology and consumer expectations evolve at breakneck speed, Indian retailers are making bold moves towards innovation, and platforms like Zoho Commerce are here to help them take the lead.
Time of India
5 days ago
- Business
- Time of India
Digital transformation and AI in energy
Artificial Intelligence (AI) and Machine Learning (ML) are transforming the energy sector in multiple ways- enhancing efficiency, reducing costs, and promoting sustainability. AI/ ML is leading the transformation in several potential areas, e.g. renewable resources management, smart grid operations, demand forecasting, predictive maintenance, energy optimisation and emission reduction. The Workshop on 'Digital Transformation and AI in Energy' will unfold the power of AI/ ML and its transformative potential in the energy industry. The workshop will comprise of 6 interactive sessions over 1 covering the following areas: · Digital Transformation and role of AI/ ML in Energy· Use cases and applications of AI/ML in Energy · Challenges, Regulations and Ethical aspects of AI/ ML · Benefits of AI/ML in Energy · Future Trends and Innovations in AI/ ML · Recap, Group activity and Team quiz The Workshop is specifically designed for energy sector and technology professionals, industry leaders, developers, researchers and students. It will offer a glimpse into the amazing possibilities of AI/ ML in transforming the future of energy, corroborated by industry best practices and use cases. The workshop will demystify the world of AI/ML-driven digital transformation in the energy sector. Here are the key takeaways that you can expect on completing the workshop, which you can use productively in your professional, academic or research capacity: · AI/ ML concepts in digital transformation of the energy sector· AI/ ML applications across energy value chain in Power, Renewables, and Oil & Gas· Industry use cases in predictive maintenance, grid optimization, renewable management, energy demand forecasting, customer hyper-personalization, etc.· Convergence of Digital Twins, IoT, AR/ VR, Cloud and AI/ ML to drive digital innovations· Industry relevance of AI/ ML in solving real-world operational challenges including climate change, energy security and sustainability· Considerations like cybersecurity, data privacy, compliance, regulations, and ethical responsibility of AI/ ML deployment About the Workshop Trainer: The Workshop in ' Digital Transformation and AI in Energy ' will be conducted by our master trainer Mr. Jayant Sinha , who has over 36 years serving both the public and private sectors in the energy and utilities domain. An engineering graduate from BITS, Pilani, and a post-graduate in management, Mr Sinha is an accredited management teacher , specialising in clean energy technologies, industry digital transformation and sustainability . He has led large business transformation projects with focus on technology-driven innovations in India and abroad. He has published over 30 technical papers in the energy domain covering diverse areas of Smart Grid, Renewable Energy, Green Hydrogen, Carbon Capture, Circular economy, Energy Data Management, Utility GIS, Industry 4.0, Cyber Security, Digital Twin, IoT and AI/ ML.>
Yahoo
5 days ago
- Health
- Yahoo
AI & ML in Clinical Trials: Fundamentals, Applications, and Regulatory Aspects - One-Day Training Course on 30th June 2025: Master AI & ML Applications in Clinical Research
Unlock the future of clinical trials with this one-day course on AI & ML. Dive into their role in trial optimization, ethical challenges, and regulatory compliance, including EU AI Act insights. Learn through real-world case studies and gain CPD certification. Enhance your skill set now! Dublin, June 25, 2025 (GLOBE NEWSWIRE) -- The "AI & ML in Clinical Trials: Fundamentals, Applications, and Regulatory Aspects Training Course (ONLINE EVENT: June 30, 2025)" has been added to offering. Artificial Intelligence (AI) and Machine Learning (ML) are rapidly reshaping the clinical trials landscape, driving innovation in how research is designed, conducted and evaluated. While these technologies hold immense promise to enhance efficiency, reduce costs, and improve outcomes, their adoption is paired with ethical concerns, prompting the development of robust regulatory frameworks to guide their responsible use. For professionals in the field, understanding the fundamentals of AI and ML and their implications is becoming increasingly essential. This comprehensive one-day training course provides an overview of AI and ML, focusing on their applications in clinical trials and the regulatory and ethical considerations that accompany their use. Participants will explore how AI and ML are being used to optimize trial efficiency, predict patient outcomes, and support adaptive trial designs. The course will also examine the regulatory frameworks, including the EU AI Act and related regulatory initiatives, to ensure compliance and ethical use of these technologies in a highly regulated environment. Through engaging lectures, real-world case studies, and interactive assessments, attendees will gain valuable insights into the transformative potential of AI and ML in clinical trials while understanding the challenges and responsibilities associated with their implementation. Join us to enhance your knowledge of these cutting-edge technologies and their role in advancing clinical research. Benefits of attending Explore the fundamental concepts of AI and ML Learn how to address common challenges with cutting-edge solutions Explore real-world use cases of AI-powered tools for clinical trial optimization Understand the ethical and regulatory requirements essential to adopting AI in clinical settings Reflect on change management in people, process, and tools for implementing an AI-based tools Prepare for the future of clinical trials and stay ahead of industry advancements Certifications: CPD: 6 hours for your records Certificate of completion Who Should Attend: This course is aimed at anyone working in clinical research, clinical operations, data management, regulatory and compliance, and associated functions seeking to leverage AI and ML in clinical trials. Whether you're new to AI/ML or looking to deepen your understanding, this course provides valuable insights into how these technologies are reshaping the clinical research landscape. Course Agenda: Introduction to AI and ML Key concepts and terminologies Types of machine learning Applications in healthcare, trends, and innovations Applications of AI and ML in clinical trials Opportunities and challenges Real-world data analysis Trial design and simulation Patient recruitment and retention optimization Predictive modelling for outcomes Applications of AI and ML in clinical trials cont'd Patient monitoring and safety surveillance Clinical data management and analysis Workflow optimization Regulatory landscape for AI in clinical trials Overview of FDA, EMA, and other relevant agencies' positions on AI and ML Validation and approval processes for AI-based tools Requirements for data handling and reporting Ethical aspects Transparency, fairness, and accountability Mitigating bias in AI models Balancing innovation with patient safety Integration and future directions Steps to incorporate AI into clinical trial workflows Overcoming common obstacles in AI/ML adoption Future directions For more information about this training visit About is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends. CONTACT: CONTACT: Laura Wood,Senior Press Manager press@ For E.S.T Office Hours Call 1-917-300-0470 For U.S./ CAN Toll Free Call 1-800-526-8630 For GMT Office Hours Call +353-1-416-8900Sign in to access your portfolio
Time Business News
23-06-2025
- Business
- Time Business News
The Role of AI and Machine Learning in Modern DevOps
In recent years, the landscape of software development and deployment has undergone a transformative shift, largely driven by advancements in Artificial Intelligence (AI) and Machine Learning (ML). These technologies are increasingly integrated into DevOps practices to automate, optimize, and enhance various stages of the software development lifecycle. This convergence is paving the way for smarter, faster, and more reliable delivery pipelines. In this article, we explore how AI and ML are revolutionizing modern DevOps. DevOps is a set of practices that combines software development (Dev) and IT operations (Ops) to shorten the development lifecycle, improve deployment frequency, and deliver high-quality software continuously. Traditional DevOps relies heavily on automation tools, version control, continuous integration/continuous delivery (CI/CD), monitoring, and collaboration. However, as systems grow more complex, the need for intelligent automation becomes evident—this is where AI and ML come into play. You can check Best DevOps companies in USA for your business. AI refers to machines' ability to simulate human intelligence, including problem-solving, decision-making, and pattern recognition. Machine Learning, a subset of AI, focuses on algorithms that learn from data to make predictions or decisions. In DevOps, these technologies serve as new engines of automation, capable of handling complex tasks such as anomaly detection, predictive analytics, and intelligent decision-making, far beyond traditional scripted automation. Continuous Integration and Continuous Deployment are core components of DevOps, enabling rapid and reliable software releases. AI and ML improve CI/CD processes in several ways: Automated Code Quality Analysis : ML models analyze code changes for potential bugs, vulnerabilities, or code smells before deployment, reducing human error and improving code quality. : ML models analyze code changes for potential bugs, vulnerabilities, or code smells before deployment, reducing human error and improving code quality. Intelligent Test Prioritization : AI algorithms prioritize test cases based on historical failure data and code changes, speeding up test cycles and focusing on high-risk areas. : AI algorithms prioritize test cases based on historical failure data and code changes, speeding up test cycles and focusing on high-risk areas. Predictive Deployment: ML models predict the success probability of deployments based on past data, helping teams decide optimal deployment windows and reduce rollbacks. Traditional monitoring relies on threshold-based alerts, which can generate false positives or overlook subtle issues. AI enhances monitoring through: Anomaly Detection : ML models analyze applications, infrastructure, and network metrics in real time to identify anomalies that could indicate security breaches, performance degradation, or failures. : ML models analyze applications, infrastructure, and network metrics in real time to identify anomalies that could indicate security breaches, performance degradation, or failures. Root Cause Analysis : When incidents occur, AI-driven tools can automatically trace issues back to their source, reducing mean time to resolution (MTTR). : When incidents occur, AI-driven tools can automatically trace issues back to their source, reducing mean time to resolution (MTTR). Predictive Analytics: AI predicts potential system failures and suggests proactive measures, minimizing downtime and service disruptions. Infrastructure management is vital in a DevOps environment. AI and ML facilitate: Auto-scaling and Load Balancing : ML models analyze traffic patterns and system metrics to dynamically adjust resources, maintaining optimal performance. : ML models analyze traffic patterns and system metrics to dynamically adjust resources, maintaining optimal performance. Infrastructure as Code (IaC) Optimization : AI tools analyze infrastructure configurations and suggest improvements for security, cost-efficiency, and performance. : AI tools analyze infrastructure configurations and suggest improvements for security, cost-efficiency, and performance. Chaos Engineering: AI-based chaos engineering tools can simulate failures to test system resilience and suggest improvements. Security is integral to DevOps with the rise of DevSecOps. AI and ML contribute by: Threat Detection : ML models scan logs, network traffic, and system activities to detect sophisticated threats in real time. : ML models scan logs, network traffic, and system activities to detect sophisticated threats in real time. Vulnerability Management : AI tools analyze code and dependencies to identify security vulnerabilities early in the development cycle. : AI tools analyze code and dependencies to identify security vulnerabilities early in the development cycle. Automated Compliance Checks: AI-driven systems ensure configurations and deployments adhere to compliance standards, reducing manual overhead. While AI and ML bring significant benefits, they also introduce challenges: Data Quality and Bias : ML models depend on high-quality data; biased or incomplete data can lead to unreliable predictions. : ML models depend on high-quality data; biased or incomplete data can lead to unreliable predictions. Complexity and Expertise : Implementing AI/ML solutions requires specialized skills and understanding of both AI and DevOps environments. : Implementing AI/ML solutions requires specialized skills and understanding of both AI and DevOps environments. Security and Privacy : AI systems can be targets for adversarial attacks or data breaches if not properly secured. : AI systems can be targets for adversarial attacks or data breaches if not properly secured. Transparency and Explainability: It's crucial that AI-driven decisions in deployment and monitoring are transparent and explainable to build trust among developers and operations teams. The integration of AI and ML into DevOps is still evolving. Future trends include: AutoML : Tools that automate the development of ML models will become more accessible, enabling DevOps teams to leverage AI without deep expertise. : Tools that automate the development of ML models will become more accessible, enabling DevOps teams to leverage AI without deep expertise. Integration with Edge Computing : AI-driven DevOps will increasingly operate at the edge, managing IoT devices and distributed systems intelligently. : AI-driven DevOps will increasingly operate at the edge, managing IoT devices and distributed systems intelligently. Synthetic Data Generation : Using AI to generate training data for models, enhancing predictive analytics without privacy concerns. : Using AI to generate training data for models, enhancing predictive analytics without privacy concerns. AI-Driven DevOps Platforms: End-to-end platforms that embed AI/ML for everything from code analysis to deployment and monitoring. AI and Machine Learning are transforming modern DevOps from a predominantly automation-driven process to a smarter, more predictive, and resilient approach. By automating complex tasks, enhancing monitoring, optimizing resource management, and strengthening security, these technologies enable organizations to deliver software faster, safer, and more reliably. While challenges remain, the continued evolution of AI and ML promises a future where DevOps becomes significantly more intelligent and autonomous, opening new frontiers for innovation and efficiency. TIME BUSINESS NEWS
