Latest news with #AutoML
Time of India
01-08-2025
- Business
- Time of India
Data Scientists on AI's chopping block? Microsoft Research sounds a career alarm
For years, 'data scientist' was the crown jewel of tech careers—the high priesthood of big data, commanding fat paychecks and corporate reverence. But if Microsoft Research's latest study is anything to go by, the golden halo may be slipping. Their analysis of over 200,000 Bing Copilot interactions warns that data science is among the jobs most exposed to generative AI replacement, raising a brutal question: Is the very craft of data science at risk of automation? Also read: 40 jobs that AI cannot touch The uncomfortable truth: AI is eating its own maker's lunch Microsoft's findings do not mince words. The very tools designed to accelerate data processing, model building, and analytics now threaten to undercut the human experts who built the field. Tasks once billed as 'complex human judgment'—feature engineering, predictive modelling, even advanced analytics—are now being executed by AutoML pipelines and generative AI in minutes, not weeks. What was once a niche skillset is rapidly commoditised by automation. The data scientist of today faces a paradox: The more powerful their tools, the less scarce their skill appears to be. Not extinction, but demotion: The looming skills shift The Microsoft study stops short of announcing the death of data science, but it sketches a future where routine number-crunching is fully machine-led, and humans are relegated to supervisory, strategic, and ethical oversight roles. Hiring trends already hint at this shift: Job postings are quietly pivoting from 'Python + ML modeling' to 'business insight, AI interpretability, and cross-functional leadership.' Companies increasingly prefer candidates who can audit AI outputs, manage data ethics, and translate algorithmic outcomes into boardroom decisions, rather than hand-craft models line by line. In other words, data science is no longer about coding clever models—it's about curating, questioning, and governing machines that do the modelling for you. The harsh career reality for students For students burning midnight oil on coding bootcamps and hackathons, Microsoft's research reads like a career curveball. Investing purely in technical chops might not guarantee future-proof employability. Here's why: AI eats low-level modelling jobs first. Entry-level roles in data prep, cleaning, and regression modelling may vanish fastest. Senior positions are safe—but harder to reach. The AI-infused workplace values contextual expertise , leadership, and ethics. These can't be automated but take years to build. Domain knowledge will trump generic data skills. Data science married to finance, biotech, or climate science remains scarce and in demand. So, the 'generalist' data science could vanish soon. How to outsmart the machine: A career survival plan Microsoft Research has handed the data science community an uncomfortable mirror: the profession that rose to prominence by automating decisions is itself on the cusp of automation. The next decade won't kill data scientists, but it will brutally separate those who ride AI as a tool of leverage from those replaced by it. For ambitious students and mid-career professionals, the message is clear: stop being the algorithm's hands—start being its brain. Stop competing with AI; start commanding it. Learn to design AI workflows, not just models. Skills in prompt engineering, AI governance , explainability, and bias mitigation are future currency. Stack your skills. Pair data science with a domain speciality (healthcare analytics, energy markets, defence simulations). Machines can crunch numbers; they can't understand nuanced sectoral problems yet. Sharpen human edges. Storytelling with data, strategic decision-making, leadership in cross-disciplinary teams—these remain stubbornly human. Reskill, relentlessly. Expect to pivot every 2–3 years. Certifications in AI ethics, advanced ML ops, or policy-tech intersections may be more valuable than a traditional degree by 2030. Ready to navigate global policies? Secure your overseas future. Get expert guidance now!
Techday NZ
18-07-2025
- Business
- Techday NZ
Open-source AutoML eases edge AI deployment for developers
An open-source AutoML solution called AutoML for Embedded, co-developed by Analogue Devices and Antmicro, is now available as part of the Kenning framework, aimed at easing the deployment of machine learning models on embedded edge devices. AutoML for Embedded is designed to streamline and automate many of the typical tasks developers encounter when attempting to implement artificial intelligence on microcontrollers and other resource-constrained hardware. These tasks often include data preprocessing, model selection, hyperparameter tuning, and device-specific optimisation. Workflow and compatibility This solution is distributed as a Visual Studio Code plugin and is built upon the Kenning library, emphasising cross-platform compatibility. It integrates with CodeFusion Studio and offers support for ADI's MAX78002 AI Accelerator Microcontroller Units (MCUs) and MAX32690, enabling direct model deployment to these hardware platforms. The workflow also supports rapid prototyping and testing through Renode-based simulation environments and the Zephyr real-time operating system (RTOS). According to the developers, this flexibility allows users to construct and deploy machine learning models on a wide variety of target platforms, avoiding vendor lock-in. Step-by-step tutorials, reproducible pipelines, and sample datasets are included to assist users in moving from raw data to edge AI deployment without requiring specialist data science expertise. Developer-oriented features The solution is the outcome of collaboration between Analogue Devices and Antmicro, who have combined hardware knowledge with open-source approaches. "Building on the flexibility of our open-source AI benchmarking and deployment framework, Kenning, we were able to develop an automated flow and VS code plugin that vastly reduces complexity of building optimised edge AI models," said Michael Gielda, Vice President of Business Development at Antmicro. "Enabling workflows based on proven open-source solutions is the backbone of our end-to-end development services that help customers take full control of their product. With flexible simulation using Renode and seamless integration with the highly configurable and standardised Zepher RTOS, the road to transparent and efficient edge AI development using AutoML in Kenning is open." How the automation works AutoML for Embedded utilises sequential model-based algorithm configuration (SMAC) to automate the search for optimal model architectures and training parameters. Hyperband with Successive Halving is applied to allocate computational resources towards the most promising candidate models. One of the key features is the automated verification that candidate models will fit within the memory limitations of target devices, allowing for more successful deployment on constrained systems. After the search and optimisation stages, models can be further refined, evaluated, and benchmarked using standard workflows within the Kenning framework. Detailed reports on model size, inference speed, and accuracy inform user decisions prior to deployment. Applications and demonstrations AutoML for Embedded has already been utilised in use cases such as anomaly detection for sensory time series data. In a detailed demonstration, a model created by the tool was deployed on the ADI MAX32690 MCU and tested in both a physical hardware setup and its digital twin using Renode simulation, enabling performance monitoring in real time. Potential application areas outlined by the project include image classification and object detection on low-power camera systems, predictive maintenance and anomaly detection in industrial IoT sensors, natural language processing for on-device text analysis, and real-time action recognition for sports and robotics settings. The package is made available to developers via the Visual Studio Code Marketplace and GitHub, reflecting its open-source nature and broad accessibility.
Business Wire
16-07-2025
- Business
- Business Wire
H2o.Ai Announces Availability of H2O AI Cloud in the New AWS Marketplace AI Agents and Tools Category
SEATTLE & MOUNTAIN VIEW, Calif.--(BUSINESS WIRE)-- the world's leading Agentic AI company and a pioneer in Sovereign AI with secure, on-premise and air-gapped deployments or in the cloud, today announced the availability of H2O AI Cloud in the new AI Agents and Tools category of AWS Marketplace. Customers can now use AWS Marketplace to easily discover, buy, and deploy AI agent solutions, including AI Cloud solution using their AWS accounts, accelerating agent and agentic workflow development. H2O AI Cloud helps organizations accelerate AI adoption, reduce risk, and optimize resource allocation across departments, while ensuring secure handling of sensitive or regulated data. This solution enables customers to rapidly build and deploy AI models without managing infrastructure, thanks to its fully hosted, secure, and scalable AutoML platform. "H2O AI Cloud in AWS Marketplace provides customers with an efficient way to access our H2O Hybrid Cloud solution, assisting them to buy and deploy agent solutions quickly and easily," said Sri Ambati, CEO and founder at "Our customers in financial services, healthcare, and technology industries are already using these capabilities to advance the development of agents and agent-driven workflows, showcasing the real-world benefits of H2O AI Cloud.' H2O AI Cloud delivers essential capabilities including automated machine learning (AutoML), a no-code AI app studio, and seamless model deployment and monitoring. These features empower customers to rapidly build, deploy, and scale AI models across their organizations while maintaining control, security, and transparency. With the availability of AI Agents and Tools in AWS Marketplace, customers can significantly accelerate their procurement process to drive AI innovation, reducing the time needed for vendor evaluations and complex negotiations. With centralized purchasing using AWS accounts, customers maintain visibility and control over licensing, payments, and access through AWS. Available as a vendor-hosted solution, H2O AI Cloud offers advanced agentic capabilities through seamless integration with the Model Context Protocol (MCP), while also enabling and fine-tuning agent performance for non-MCP resources. AI Agents are exceptional problem solvers—highly adaptable and capable of addressing virtually any use case. Designed to operate across the full agentic toolspace both within and external to AWS, Agentic solutions consistently deliver across all agent functions. This enables customers to seamlessly connect with other AWS services and flexibly deploy across their AWS environment. To learn more about H2O AI Cloud in AWS Marketplace, visit AWS Marketplace. To learn more about the new Agents and Tools category in AWS Marketplace, visit About Founded in 2012, is on a mission to democratize AI. As the world's leading agentic AI company, converges Generative and Predictive AI to help enterprises and public sector agencies develop purpose-built GenAI applications on their private data. Trusted by 20,000+ global organizations, and over half of the Fortune 500, powers AI transformation for companies like AT&T, Commonwealth Bank of Australia, Singtel, Chipotle, Workday, Progressive Insurance, and NIH. partners include Dell Technologies, Deloitte, Ernst & Young (EY), NVIDIA, Snowflake, AWS, Google Cloud Platform (GCP) and Microsoft Azure. AI for Good program supports nonprofit groups, foundations, and communities in advancing education, healthcare, and environmental conservation. With a vibrant community of 2 million data scientists worldwide, aims to co-create valuable AI applications for all users. has raised $256 million from investors, including Commonwealth Bank, NVIDIA, Goldman Sachs, Wells Fargo, Capital One, Nexus Ventures and New York Life. About Amazon Web Services Since 2006, Amazon Web Services has been the world's most comprehensive and broadly adopted cloud. AWS has been continually expanding its services to support virtually any workload, and it now has more than 240 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, media, and application development, deployment, and management from 117 Availability Zones within 37 geographic regions, with announced plans for 13 more Availability Zones and four more AWS Regions in Chile, New Zealand, the Kingdom of Saudi Arabia, and the AWS European Sovereign Cloud. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit
Yahoo
01-04-2025
- Automotive
- Yahoo
Fujitsu and Macquarie University partner to help address critical shortage of machine learning engineers
New micro credentials course will give students access to Fujitsu's AutoML technology, allowing them to generate AI models more rapidly KAWASAKI, Japan, March 31, 2025 /CNW/ -- Faster and highly accurate AI models will be available and accessible to students through a strategic partnership between global digital transformation leader, Fujitsu, and Macquarie University. The collaboration, formed through an established strategic partnership, will offer university-developed AI micro credentials via Macquarie University's online learning platforms as well as popular platforms including LinkedIn Learning and Udemy, allowing students, researchers and industry professionals around the world access to Fujitsu's proprietary AutoML technology while developing valuable knowledge and skills in automated machine learning. The new four-week online course, "Fujitsu AutoML: Mastering Automated Machine Learning" will open for registrations. The course is tailored to bridge the gap in AI education and will teach basic theory with practical exercises on topics including automated machine learning, models and algorithms using the Fujitsu AutoML tool. Accelerating AI implementation process with Fujitsu AutoML technology The Fujitsu AutoML technology speeds up analysis by evaluating the most promising machine learning pipelines, rather than all combinations of possible outcomes. The technology also enhances transparency for data-driven decision-making by showing users how the AI model is built. Fujitsu AutoML is an integral component of Fujitsu's cloud-based AI Platform, Fujitsu Kozuchi, which enables rapid development, testing, and implementation of AI across seven areas: Generative AI, AutoML, predictive analytics, text, vision, AI trust, explainable AI. For full release click here View original content: SOURCE Fujitsu Limited View original content: Sign in to access your portfolio
