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Hackernoon
2w
200
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Seamlessly Sync API Data to Your Data Lake Using DolphinScheduler and SeaTunnel
- SeaTunnel along with DolphinScheduler is being used for CDC data lake ingestion.
- Integration via APIs and the scheduling of data synchronization is essential for scenarios where direct database access is not possible.
- Example scenario includes synchronizing inventory data from ERP (SAP) into a data lake for analysis.
- Steps involve configuring SeaTunnel connectors, running SeaTunnel jobs for data transformation, and integrating SeaTunnel with DolphinScheduler for automated workflow execution.
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Analyticsindiamag
2w
139
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AI is Changing Healthcare, But Can India Protect Patient Privacy?
- AI is revolutionizing healthcare in India through advancements like AI-driven drug discovery and digitizing patient records for improved diagnostic accuracy and privacy protection.
- Government initiatives, such as increasing medical seats and developing low-cost MRI machines, contribute to healthcare hardware development.
- Initiatives like the Genome India project and Ayushman Bharat Digital Mission are enhancing healthcare technology and clinical understanding in India.
- Startups like BrainSightAI and Niramai Health Analytics are leveraging AI for innovative diagnostic solutions in neurology and breast cancer screening.
- Pharmaceutical companies like Biocon are utilizing AI for biosimilar development and enhancing patient access to healthcare.
- While AI innovation in healthcare is promising, concerns arise regarding data security and patient privacy due to the risk of data breaches and sensitive information exposure.
- Strict adherence to data protection regulations and protocols is crucial to mitigate risks associated with AI platforms and patient data handling.
- Healthcare organizations need to deploy approved AI applications centrally to ensure data security and reduce reliance on personal accounts.
- India's approach to data protection is evolving organically, emphasizing the need for interdisciplinary med-tech programs integrating AI with medical education.
- Measures such as deploying data loss prevention policies and prioritizing med-tech programs can help India safeguard patient privacy amid AI-driven healthcare transformation.
- Ensuring comprehensive data protection frameworks and countering misinformation are vital to supporting healthcare innovation and protecting patient data in India.
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Analyticsindiamag
2w
119
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India’s Defence Drone Industry Gains Momentum, But Regulatory Challenges Persist
- India's defence drone sector has gained momentum following tensions with Pakistan, showcasing the strategic importance of indigenous drones during 'Operation Sindoor'.
- However, challenges persist due to regulatory ambiguity, import restrictions, and technology transfer limits, according to Major General Mandip Singh.
- Government initiatives like Make in India and Atmanirbhar Bharat, along with regulatory frameworks, are creating a better environment for indigenous drone manufacturers.
- India's military drone market is projected to reach $4,082 million by 2030, with a CAGR of 17.9% from 2025 to 2030, showcasing substantial growth opportunities.
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Analyticsindiamag
2w
17
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How Snowflake is Simplifying Data Engineering in 2025
- Sumeet Tandure, senior manager of sales engineering at Snowflake, discussed data engineering disruptions and principles at AIM’s DES 2025 event.
- Transitioning from experimentation to production requires a strong data engineering foundation to achieve ROI.
- LLMs are simplifying value extraction from unstructured data types like PDFs, audios, and videos.
- Snowflake's Document AI automates extraction of specific fields from documents, enhancing data engineers' capabilities.
- Data interoperability, exemplified by Iceberg, enables multiple engines to communicate regardless of origin.
- Iceberg's ecosystem is expanding to open-source catalogues, reducing vendor lock-in and enhancing interoperability.
- Snowflake's Cortex Search simplifies pipelines for building search engines and chatbots, boosting developer productivity.
- Snowflake provides governance tools like automatic classification of Indian identifiers and attribute-based access controls.
- Snowflake integrates DevOps principles into data operations with Python APIs, Snowflake CLI, and CI/CD workflows.
- Snowflake offers end-to-end data engineering capabilities with support for various integration options, transformations, and delivery methods.
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Medium
2w
1.5k
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Major Difference Between Ai Code And Human Written Code
- AI is great for coding small fragments of code or writing short functions in various programming languages like Bash, C++, Python, etc.
- It can quickly generate examples of code using features of different languages, along with wrappers that serve as mini-unit tests.
- AI excels as a shortcut reference tool by providing fast and accurate information if the required documentation is available and the query is well-defined.
- However, AI may struggle with writing complex logic that requires human reasoning, as seen in the example of creating a complicated regular expression.
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Analyticsindiamag
2w
327
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NVIDIA-backed CoreWeave to Invest $23 billion on AI infrastructure in 2025
- NVIDIA-backed CoreWeave plans to invest $20 to $23 billion in 2025 on AI infrastructure and data center capacity.
- In its first quarter of 2025 results, CoreWeave highlighted rapid scaling of AI Infrastructure, with a total of approximately 420 MW of active power and 1.6 GW of contracted power.
- The company's revenue increased by 420% year-over-year, driven by demand for the CoreWeave Cloud Platform, with projected capital expenditure for the second quarter exceeding revenue expectations.
- CoreWeave has raised $1.4 billion in net proceeds through its IPO and is ramping up investments in data center and server infrastructure to meet customer demand.
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Analyticsindiamag
2w
392
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How AI is Giving Patients Perfect Vision
- Artificial intelligence (AI) is revolutionizing ophthalmology by optimizing the design and implantation of AI-designed intraocular lenses (IOLs) for perfect vision.
- AI-designed IOLs like Rayner’s RayOne Galaxy and Galaxy Toric IOLs offer enhanced rotational stability and minimal visual disturbances, improving patient satisfaction and visual clarity.
- Challenges of integrating AI in clinical practice include the need for rigorous validation, training surgeons, and educating patients through counseling and visual simulation sessions.
- AI is also being used for early disease detection, predictive analytics, and surgical guidance in ophthalmology, promising to transform vision restoration and patient outcomes.
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Analyticsindiamag
2w
292
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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
2w
209
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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
2w
292
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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
2w
100
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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
2w
225
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
2w
192
Image Credit: Medium
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
2w
117
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
2w
410
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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