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Discover NVIDIA cuOpt: Power of Accelerated Data Analytics

NVIDIA cuOpt

AI is driving innovation across industries via machine-powered computation. Bankers are using AI to detect fraud faster and keep accounts safe, telecommunications providers are improving networks to deliver better service, scientists are developing new treatments for rare diseases,Utility companies are developing more dependable and clean energy infrastructures, while automakers are improving the safety and accessibility of self-driving vehicles.

The foundation of top AI use cases is data. Training AI models on large datasets is necessary for accuracy. AI-powered enterprises must create a data pipeline to extract data from many sources, standardise it, and store it efficiently.

Data scientists use several tests to optimise AI models for real-world use. To work in real time, voice assistants and personalised recommendation systems must analyse massive data quantities quickly.

Complex AI models that handle text, audio, pictures, and video require speedy data processing. Data bottlenecks, rising data centre expenses, and limited processing capabilities hinder innovation and performance in legacy CPU-based organisations.

Accelerated computers is helping many companies implement AI. This technology uses GPUs, specialised hardware and software, and parallel processing to raise computing performance by 150x and energy efficiency by 42x.

Accelerated data processing is powering groundbreaking AI developments at top companies.

Financial institutions detect fraud in milliseconds

Financial institutions struggle to detect fraud due to the large amount of transactional data that needs speedy processing. Training AI models is also problematic due to the lack of labelled fraud data. Fraud detection data volumes are too huge for traditional data science pipelines to accelerate. This slows processing, preventing real-time data analysis and fraud detection.

American Express, which processes over 8 billion transactions annually, trains and deploys LSTM models using accelerated computing to tackle these issues. These models are useful for fraud detection because they can adapt and learn from fresh data and sequentially analyse abnormalities.

American Express trains its LSTM models faster using GPU parallel computing. GPUs allow live models to process massive transactional data for real-time fraud detection.

To secure customers and merchants, the system functions within two milliseconds, 50x faster than a CPU-based design. American Express increased fraud detection accuracy by 6% in certain segments by merging the accelerated LSTM deep neural network with its existing approaches.

Accelerated computing can lower data processing expenses for financial companies. PayPal showed that NVIDIA GPUs may save cloud expenses by 70% for big data processing and AI applications by running Spark3 workloads.

Financial organisations can detect fraud in real time by processing data more effectively, allowing speedier decision-making without disturbing transaction flow and reducing financial loss.

The telcos simplify complex routing operations by NVIDIA cuOpt

Telecommunications companies create massive amounts of data from network devices, client contacts, invoicing systems, and network performance and maintenance.

Managing national networks that handle hundreds of petabytes of data daily involves intricate technician routing for service delivery. Advanced routing engines compute millions of times, including weather, technician skills, client requests, and fleet dispersal, to optimise technician dispatch. These operations require careful data preparation and enough computational power. It introduced the NVIDIA cuOpt

NVIDIA cuOpt

AT&T, which has one of the nation’s largest field dispatch teams, is improving data-heavy routing operations with NVIDIA cuOpt, which calculates difficult vehicle routing problems using heuristics, metaheuristics, and optimisations.

In early experiments,NVIDIA cuOpt delivered routing solutions in 10 seconds, reducing cloud expenses by 90% and allowing personnel to perform more service calls everydayby using NVIDIA cuOpt . NVIDIA RAPIDS, a package of software libraries that accelerates data science and analytics pipelines, speeds NVIDIA cuOpt, allowing organisations to use local search methods and metaheuristics like Tabu search for continuous route optimisation.

NVIDIA RAPIDS

AT&T is using NVIDIA RAPIDS Accelerator for Apache Spark to improve Spark-based AI and data pipelines. The organisation can now train AI models, maintain network quality, reduce customer churn, and detect fraud more efficiently. AT&T is decreasing cloud computing spend for target applications, improving performance, and lowering its carbon footprint with RAPIDS Accelerator.

Telcos need faster data pipelines and processing to boost operational efficiency and service quality.

Medical researchers condense drug discovery timelines

Medical data and peer-reviewed research publications have exploded as academics use technology to explore the 25,000 genes in the human genome and their effects on diseases. Medical researchers use these publications to restrict their hunt for new medicines. Such a huge and growing body of relevant research makes literature reviews impractical.

Pharma giant AstraZeneca created a Biological Insights Knowledge Graph (BIKG) to help scientists with literature reviews, screen hit rate, target identification, and more. This graph models 10 million to 1 billion complex biological interactions using public and internal datasets and scholarly publications.

Gene ranking using BIKG has helped scientists identify high-potential targets for novel disease treatments. AstraZeneca presented a lung cancer treatment resistance gene discovery initiative at NVIDIA GTC.

Data scientists and biological researchers defined criteria and gene features for therapy development gene targeting to narrow down potential genes. A machine learning algorithm searched the BIKG databases for genes with treatable properties listed in literature. NVIDIA RAPIDS was used to decrease the gene pool from 3,000 to 40 target genes in seconds, a task that previously took months.

By using accelerated computers and AI, pharmaceutical companies and researchers may finally leverage the massive medical data sets to produce breakthrough treatments faster and safer, saving lives.

Utility Companies Create Clean Energy’s Future

Energy sector shifts to carbon-neutral sources are widely promoted.The cost of harvesting renewable resources, such as solar energy, has decreased over the last ten years, making the transition to clean energy more straightforward than before.

Integrating clean energy from wind farms, solar farms, and household batteries has complicated grid management. Grid management is more data-intensive as energy infrastructure diversifies and two-way power flows are required. New smart grids must handle high-voltage vehicle charging locations. Distribution of stored energy sources and network usage changes must also be managed.

Utilidata, a leading grid-edge software business, and NVIDIA developed Karman, a distributed AI platform for the grid edge, employing a bespoke Jetson Orin edge AI module. This special chip and platform in electricity metres turns them into data gathering and control points that can handle thousands of data points per second.

Karman handles real-time, high-resolution metre data from the network edge. This lets utility firms analyse system conditions, estimate usage, and integrate distributed energy resources in seconds. Inference models on edge devices allow network operators to quickly identify line defects to predict outages and do preventative maintenance to improve grid reliability.

Karman helps utilities create smart grids using AI and fast data analytics. This permits tailored, localised electricity distribution to satisfy variable demand patterns without substantial infrastructure modifications, making grid modernization more cost-effective.

Automakers Improve Safety and Accessibility of Self-Driving Vehicles

Automakers want self-driving vehicles with real-time navigation and object recognition. This involves high-speed data processing, including feeding live camera, lidar, radar, and GPS data into AI models that make road safety navigation decisions.

Multiple AI models, preprocessing, and postprocessing make the autonomous driving inference pipeline difficult. These processes were traditionally done by CPUs on the client side. This might cause severe processing speed bottlenecks, which is unacceptable for a safety-critical application.

Electric vehicle manufacturer NIO added NVIDIA Triton Inference Server to its inference pipeline to improve autonomous driving workflows. Inference-serving, open-source NVIDIA Triton uses multiple frameworks. NIO centralised data processing operations to reduce latency by 6x in some essential areas and enhance data throughput by 5x.

NIO’s GPU-centric strategy enabled updating and deploying new AI models easier without vehicle changes. The corporation may also employ numerous AI models on the same photographs without sending data over a network, saving money and improving efficiency.

Autonomous vehicle software engineers employ rapid data processing to achieve high performance to reduce traffic accidents, transportation costs, and user mobility.

Retailers Forecast Demand Better

Data processing and analysis are essential for real-time inventory adjustments, customer personalisation, and price strategy optimisation in retail. Larger retailers with more products have more sophisticated and compute-intensive data processes.

Walmart, the world’s largest retailer, used accelerated computing to increase forecasting accuracy for 500 million item-by-store combinations across 4,500 shops.

Walmart’s data science team constructed stronger machine learning algorithms to tackle this massive forecasting task, but the computing environment started to fail and produce erroneous findings. Data scientists had to delete characteristics from algorithms to finish them, the company found.

Walmart used NVIDIA GPUs and RAPIDs to improve forecasting. A forecasting algorithm with 350 data variables predicts sales across all product categories for the company. These include sales statistics, promotional activities, and external factors like weather and the Super Bowl that affect demand.

Walmart improved prediction accuracy from 94% to 97%, eliminated $100 million in fresh produce waste, and reduced stockout and markdown scenarios with advanced algorithms. GPUs ran models 100x faster, finishing projects in four hours that would have taken weeks on a CPU.

Retailers can reduce costs and carbon emissions while offering shoppers the finest options and lower prices by moving data-intensive operations to GPUs and accelerated computing.

Public Sector Prepares for Disasters

Public and corporate organisations use immense aerial image data from drones and satellites to predict weather, follow animal movements, and monitor environmental changes. This data helps researchers and planners make better decisions in agriculture, disaster management, and climate change. If it lacks location metadata, this imagery is less useful.

A federal agency collaborating with NVIDIA sought a solution to automatically locate photos without geolocation metadata for search and rescue, natural disaster response, and environmental monitoring. Like finding a needle in a haystack, pinpointing a small location in a bigger aerial photograph without information is difficult. Geolocation algorithms must account for image lighting and time, date, and angle variances.

NVIDIA, Booz Allen, and the government agency used computer vision algorithms to scale the picture similarity search challenge to find non-geotagged aerial photographs.

A Python-based programme was utilised by an NVIDIA solutions architect to overcome this challenge. CPU processing took over 24 hours initially. GPUs parallelized hundreds of data processes in minutes, compared to a CPU’s few. The application was 1.8-million-x faster after switching to CuPy, an open-source GPU-accelerated library, producing results in 67 microseconds.

A technology that can process photographs and data of enormous land masses in minutes can help organisations respond faster to emergencies and plan ahead, saving lives and protecting the environment.

Accelerate AI and Business Results

Accelerated computing for data processing promotes AI initiatives and positions companies to innovate and outperform.

Accelerated processing speeds up model training and algorithm selection, improves live AI solution accuracy, and handles larger datasets.

It can achieve better price-performance ratios than CPU-based systems and help companies deliver better results and experiences to consumers, employees, and partners.

Read more on Govindhtech.com

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Top 10 DevOps Trends in 2024

In the ever-evolving landscape of DevOps, staying ahead of the curve is essential for organizations aiming to enhance collaboration, accelerate development, and ensure continuous delivery. As we look forward to 2024, several trends are shaping the future of DevOps. Let’s explore the top 10 DevOps trends that are expected to dominate the scene in the coming year.

AI and Machine Learning Integration

DevOps is set to embrace AI and machine learning to optimize processes further. Expect intelligent automation, predictive analytics, and anomaly detection to become integral parts of the DevOps toolchain, enhancing efficiency and decision-making.

GitOps Adoption

GitOps, an operational model that leverages Git for declarative infrastructure and application management, is gaining traction. This trend is expected to continue in 2024, providing teams with a unified approach to version control and infrastructure as code.

Security-Driven DevOps (DevSecOps)

With the increasing frequency and sophistication of cyber threats, security is becoming an integral part of DevOps. DevSecOps practices will be a focal point, integrating security measures at every stage of the development pipeline.

Serverless Computing in DevOps

Serverless architecture is set to revolutionize how applications are developed and deployed. In 2024, DevOps teams will explore serverless computing to enhance scalability, reduce infrastructure management overhead, and optimize resource utilization.

Kubernetes as a Standard

Kubernetes has become the de facto standard for container orchestration. In 2024, its role in DevOps will continue to expand, with more organizations adopting Kubernetes for seamless container deployment, scaling, and management.

Multi-Cloud Strategies

Embracing a multi-cloud approach will be crucial for DevOps teams seeking flexibility and resilience. The trend in 2024 will involve optimizing workflows for multiple cloud providers, ensuring efficient resource utilization and avoiding vendor lock-in.

Edge Computing Integration

As edge computing gains prominence, DevOps practices will adapt to support applications deployed at the edge. Expect to see the incorporation of edge computing considerations into CI/CD pipelines and infrastructure management strategies.

Enhanced Observability

Comprehensive observability across the entire software delivery pipeline will be a priority. DevOps teams will invest in advanced monitoring, logging, and tracing solutions to gain insights into application performance, user experience, and infrastructure health.

ChatOps for Collaboration

ChatOps, which integrates chat platforms with DevOps tools, will continue to facilitate real-time collaboration among team members. In 2024, expect increased adoption of ChatOps for streamlined communication, issue resolution, and knowledge sharing.

No-Code/Low-Code DevOps

 The rise of no-code/low-code platforms is extending into the DevOps domain. In 2024, these platforms will empower non-developers to participate in the DevOps process, accelerating the delivery of applications and reducing the development bottleneck.

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