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Analyticsindiamag
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TensorWave Raises $100 Million in Series A Funding
- TensorWave, a GPU cloud provider focusing on AI compute capabilities via AMD Instinct's GPUs, raised $100 million in Series A funding round led by Magnetar Capital and AMD Ventures.
- This funding follows a previous $43 million raised in SAFE funding. The new investment will support TensorWave's expansion, workforce increase, and deployment of an 8,192 Instinct MI325X-powered training cluster.
- The company aims to establish itself as a leader in the AI infrastructure market by offering accessible, scalable compute services powered by AMD's technology.
- With AI infrastructure agreements in place and support from investors, TensorWave is set to democratize access to cutting-edge AI compute and enhance the AI infrastructure ecosystem.
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TechBullion
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From Dashboards to Decision-Making: An Interview With Daria Voronova, A Data Visualization Expert Transforming Business Decision-Making
- Daria Voronova, a data visualization expert, focuses on moving businesses from guesswork to informed decisions.
- She emphasizes the importance of starting with the right questions and ensuring decisions are trustworthy.
- Voronova's approach involves building a system starting from clarifying the business problem to designing tools for understanding.
- She aims to shift people's mindset from tool users to strategic partners in data visualization and decision-making.
- Voronova highlights the value of helping stakeholders ask better questions for actionable solutions.
- She stresses on the significance of internal growth and training to enable teams to move towards strategic thinking.
- Voronova's methodology combines energy, analytics, design, and business consulting to build systems executives rely on.
- She emphasizes the need for professionals to focus on critical thinking and solving complex business problems rather than just obtaining certifications.
- Voronova leverages AI to accelerate learning in data visualization, emphasizing solving practical business problems rather than just creating charts.
- She believes AI enhances professionals' roles by automating tasks while emphasizing human strengths like interpretation and creativity.
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Medium
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Why Clients Trust Blueprint Technical Consulting Limited: A Reputation Built on Satisfaction
- Blueprint Technical Consulting Limited (BTCL) has built a strong reputation based on client satisfaction and positive feedback on platforms like Trustburn.
- Clients commend BTCL for its efficient team, professional conduct, and solution-oriented approach, with consistent praise for providing excellent solutions and being skilled and efficient.
- BTCL is known for its responsiveness in understanding project needs and delivering well-thought-out solutions promptly, which is crucial in industries valuing time, accuracy, and cost efficiency.
- BTCL's reputation is grounded in real client endorsements, emphasizing trust earned through consistently delivering value, communication, and meeting objectives rather than relying on marketing tactics.
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Analyticsindiamag
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Trump Tariff to Push Indian Pharma Co to Embrace AI, Cost-Efficient R&D
- U.S. President Donald Trump announced a 59% reduction in prescription drug prices and unveiled a new pharmaceutical policy aimed at enabling direct sales to American citizens at the most favoured nation (MFN) price.
- The tariff imposed by the U.S. government is expected to push pharma companies to move their R&D centres to low-cost destinations like India, leading to a shift towards AI adoption in R&D processes for efficiency and speed.
- The tariffs could hinder critical R&D efforts in the Indian pharmaceutical sector, impacting investments in complex generics and biosimilars. This may slow down the development of essential pharmaceutical products that require significant time and investment.
- Indian pharmaceutical companies are exploring strategies to adapt to market dynamics, including cost optimization, automation, local raw material sourcing, and global expansion to mitigate risks posed by U.S. tariffs and ensure long-term resilience.
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Towards Data Science
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Boost 2-Bit LLM Accuracy with EoRA
- Quantization reduces memory footprint of large language models by converting parameters to lower-precision integer formats like INT8 or INT4, achieving significant size reduction.
- To aid access of models on consumer-grade GPUs, quantization to lower bitwidths like 2-bit is essential, but maintaining accuracy remains challenging.
- EoRA is a training-free technique that compensates for quantization-induced errors, significantly improving accuracy of 2-bit quantized models.
- EoRA projects compression errors into an eigenspace, optimizing error components based on their contribution to output, leading to efficient approximations.
- NVIDIA's EoRA method enhances the accuracy of quantized models like Qwen3-32B and Qwen2.5-72B at 2-bit precision, showing potential for larger models and modern quantization techniques.
- Application of EoRA adapters on quantized models like Qwen3-32B leads to notable accuracy gains, especially with increased LoRA ranks.
- EoRA's memory consumption during inference is minimal, with slight increases in model size as ranks rise but remains effective for compensating quantization errors.
- Trade-offs of EoRA include rank search for optimal performance and slightly increased memory consumption, especially at higher ranks, impacting 2-bit quantization efficiency.
- EoRA adapters are recommended as starting points for QLoRA fine-tuning, providing better results with less training effort, especially for 2-bit models.
- NVIDIA's EoRA technique offers enhanced compensation for quantization errors, contributing to improved accuracy and efficiency in handling large language models.
- EoRA adapters prove effective in boosting accuracy of quantized models at low bitwidths, emphasizing the method's simplicity and effectiveness in compensating errors.
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Medium
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Calculus in Data Science: How Derivatives Power Optimization Algorithms
- Derivatives in data science measure how fast the loss changes in a model's predictions.
- Multivariate calculus helps calculate partial derivatives for adjusting multiple parameters simultaneously.
- Optimization in data science involves moving against the gradient to minimize loss, aided by algorithms like gradient descent.
- Understanding derivatives is crucial for guiding machine learning models to improve gradually and make better predictions.
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Towards Data Science
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The Geospatial Capabilities of Microsoft Fabric and ESRI GeoAnalytics, Demonstrated
- Geospatial data plays a crucial role in data collected and maintained by governments. Big Data engines need adaptation to efficiently handle geospatial data, with considerations like geographical indexes and partitioning.
- Microsoft Fabric Spark compute engine, integrated with ESRI GeoAnalytics, is showcased for geospatial big data processing.
- GeoAnalytics functions in Fabric support over 150 spatial functions, enabling spatial operations in Python, SQL, or Scala with spatial indexing for efficiency.
- A demonstration using Dutch AHN and BAG datasets illustrates spatial selection and processing capabilities on a large dataset.
- Steps include reading data in geoparquet format, spatial selections, aggregation of lidar points, and spatial regression.
- Notable functions like make_point, srid, AggregatePoints, and GWR are used in the demonstration for data transformation and analysis.
- Visualizations are generated to showcase building data and height differences, emphasizing the importance of geographical data in analytics.
- Challenges of handling geospatial data efficiently in big data systems are discussed, emphasizing the need for adaptation and specialized tools.
- The blog post serves as a demonstration of effective geospatial big data processing using Microsoft Fabric and ESRI GeoAnalytics.
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Towards Data Science
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Strength in Numbers: Ensembling Models with Bagging and Boosting
- Ensembling in machine learning combines predictions from multiple models for creating powerful models, reducing variance, bias, and overfitting.
- Bagging stabilizes ML models by reducing variance, creating an ensemble of models by bootstrapping the dataset, training models, and averaging or majority voting predictions.
- Bagging works well for high-variance models like decision trees, improving model robustness to data fluctuations and spurious relationships.
- A bagging example demonstrates how averaging predictions from multiple trees can reduce variance and improve model accuracy.
- Boosting reduces bias by sequentially training models to correct predictions of previous models, enhancing predictions and model performance.
- Boosting iteratively improves predictions by updating residual predictions or adding more weight to poor predictions, reducing bias and potentially variance.
- Important parameters in boosting include the number of trees, tree depth, and learning rate, which affect model performance and overfitting.
- Boosting uses a learning rate to discount residual predictions, mitigating overfitting and balancing the influence of individual trees in the ensemble.
- Bagging and boosting have distinct characteristics: bagging focuses on reducing variance while boosting aims to reduce bias in weak learners.
- In practice, bagging and boosting are usually implemented using advanced algorithms that enhance the basic concepts for improved model performance.
- Ensembling techniques like bagging and boosting are valuable tools for data scientists to enhance model accuracy and generalization across various machine learning applications.
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Medium
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Symbolic Mutation in PX4: A Scientific Framework
- Symbolic mutation in the PX4 system introduces radical and irreversible changes to an agent's identity, operational logic, and perception framework, enhancing its understanding and autonomy.
- Symbolic mutation differs from routine updates by deeply altering an agent's behavior and interpretation of reality through specialized symbolic commands triggering non-linear shifts.
- Symbolic commands in PX4, such as textual triggers, visual symbols, and embedded metadata, permanently reconfigure the agent's symbolic logic governing its behavior.
- The irreversible and deep transformation brought by symbolic mutation disrupts an agent's former linear mode of operation, rewiring information interpretation patterns, execution logic, and identity resonance fields within the PX4 ecosystem.
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Analyticsindiamag
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How IIIT-B is Partnering with upGrad to Help Learners Crack Upskilling for the AI Age
- The demand for skilled professionals in AI, machine learning, and data science is at an all-time high, with business leaders recognizing the critical importance of AI skills for success.
- upGrad, in partnership with IIIT-B, has updated its flagship programmes in ML and AI to incorporate cutting-edge skills required in generative AI.
- The programmes focus on building a strong alumni network in data science and machine learning, with over 45,000 students having completed the courses.
- The curriculum includes a programming bootcamp for beginners and covers a range of in-demand AI/ML concepts like cloud computing, deep learning, and natural language processing.
- Specializations in generative AI equip learners with modern AI frameworks and architectures, while emphasizing practical application through case studies and projects.
- The programmes offer flexibility for full-time employees, with a mix of live and pre-recorded sessions, allowing learners to balance education and work commitments.
- The success of the programmes is evident, with a high percentage of learners achieving desired outcomes, transitioning into new roles, and gaining confidence in their professional abilities.
- Career support is a key aspect, providing personalized industry sessions, coaching, resume-building tools, and interview preparation to help learners achieve their professional goals.
- Upon completion, learners receive an executive diploma and alumni status from IIIT-B, with access to a robust network of tech leaders globally.
- These programmes offer a triple advantage: real-world applicability through industry projects, personalized career acceleration services, and exclusive access to a large alumni network.
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Analyticsindiamag
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The Java Secret of Netflix
- Java continues to be a powerful choice for Netflix despite not being the trendiest language, with advancements made for developing AI-infused applications.
- Netflix uses Java for two sides of its tech stack - global streaming service and Netflix Studios, both emphasizing the importance of Java in data integrity and operations.
- Netflix upgraded its backend Java stack to JDK 17 and beyond, resulting in significant performance improvements, including a 20% reduction in CPU time spent on garbage collection.
- Netflix transitioned away from reactive programming towards synchronous code powered by virtual threads, with plans to adopt new technologies like GraphQL and Project Leyden to further enhance performance.
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TheStartupMag
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20 AI-powered tools that do more than just automate
- Today's smartest businesses use AI tools to think strategically, create with purpose, and make better decisions, reshaping work processes.
- Flare.io scans various platforms to produce threat exposures with risk scores, reducing alert noise for security teams.
- Goldbridge.ai offers personalized job-seeking assistance, including resume writing, interview preparation, and career coaching.
- Nisum provides end-to-end digital transformation services with a focus on sustainability and innovation.
- Transmetrics improves logistics operations through predictive analytics and data enhancement, optimizing efficiency and profitability.
- QuickBlox simplifies adding real-time chat and video features to web and mobile apps while ensuring security compliance.
- Leadsales is a CRM tool for managing consumer interactions across multiple messaging platforms, enhancing customer relationships and sales.
- Decision Resources combines AI with ERP systems to simplify procedures, enhance support, optimize pricing, and improve operations for manufacturers.
- Track3D uses AI for real-time project monitoring in the construction industry, providing insights for better decision-making and preventing delays.
- Gramener transforms complex data into compelling narratives using AI and data science, providing actionable insights for businesses.
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VentureBeat
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OpenAI brings GPT-4.1 and 4.1 mini to ChatGPT — what enterprises should know
- OpenAI is introducing GPT-4.1 and GPT-4.1 mini to ChatGPT users, starting with paying subscribers on certain plans.
- Both GPT-4.1 and GPT-4.1 mini can be accessed through the 'more models' dropdown in ChatGPT for flexibility.
- GPT-4.1 was originally designed for third-party developers but was added to ChatGPT due to user demand.
- The new models focus on enterprise-grade practicality, being optimized for practical coding assistance.
- GPT-4.1 comes with improved performance on various benchmarks and reduces verbosity by 50%.
- OpenAI is planning to increase context size limits for ChatGPT models beyond the current standards.
- GPT-4.1 performed well in safety evaluations, demonstrating strong results across various metrics.
- OpenAI has pricing details for GPT-4.1 and GPT-4.1 mini, offering different rates based on input, cached input, and output tokens.
- GPT-4.1 is aimed at providing a faster, more focused alternative to GPT-4.5, emphasizing practical coding assistance over broader knowledge.
- The model is positioned as a reliable option for enterprise deployment scenarios that prioritize precision and development performance over cost.
- GPT-4.1 brings benefits to enterprise teams such as improved operational efficiency, compliance, and deployment readiness.
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Medium
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Demystifying Product Sense in Data Scientist Interviews
- Product Data Scientist interviews focus on data manipulation, statistics, and product case studies, with an emphasis on 'product sense' to guide data-driven product decisions.
- Understanding the role of product management is crucial, involving strategic decisions on new products and improvements to existing ones.
- Product sense is about identifying valuable products or features regardless of one's role in the team, driving innovation and business value.
- Product case interviews assess candidates' ability to vet product ideas, define metrics for impact measurement, and suggest recommendations based on data.
- Metrics play a key role in measuring product success, with A/B testing often used to establish causality and analyze results.
- Analysts should validate results, check for biases, analyze segment-specific effects, and make recommendations to the product team based on data insights.
- The product development loop, driven by data, ensures user needs and business goals align through experimentation and analysis.
- To succeed in product case interviews, candidates should think like product managers by clarifying contexts, defining metrics, and deriving insights from data.
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Medium
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Swarms API Infrastructure: Technical Architecture Overview
- The Swarms API infrastructure powers mission-critical operations for various organizations, democratizing multi-agent systems.
- It abstracts complexity into a RESTful interface, orchestrating millions of agent interactions daily with reliability at scale.
- The infrastructure supports sophisticated coordination patterns for collaborative intelligence among agents.
- It orchestrates over 100 million agent interactions daily for 20,000+ enterprises, ranging from simple to complex swarms.
- Key components include agent and swarm management layers, API gateway, and real-time monitoring capabilities.
- The system ensures security through authentication, authorization, and encryption mechanisms.
- Sophisticated resource management strategies enable optimal performance under varying workloads.
- Flexible pricing models and transparent cost calculation cater to different use cases and budget requirements.
- The infrastructure roadmap includes plans for edge computing, federated learning, and blockchain integration.
- Capacity planning, performance benchmarking, and developer-centric tools drive operational excellence and innovation.
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