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Data Science News
Medium
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Hinge Loss: Understanding and Implementing it from Scratch
- Support Vector Machines (SVMs) use hinge loss to confidently separate data points into distinct classes.
- SVMs aim to find the best line with the widest margin between classes, even allowing for some errors with soft margins.
- Hinge loss measures the model's confidence in its decisions, encouraging a clear margin of difference between classes.
- While logistic regression uses log loss for smooth and probabilistic decisions, SVMs use hinge loss for sharp decision boundaries.
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Analyticsindiamag
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Why INDMoney is Using Golang to Power Real-time Data Streaming
- INDMoney is using Go programming language, the messaging system NATS, and a WebSocket engine to deliver high-frequency market data at extraordinary speeds.
- The company's objective is to keep traders updated in real-time with latencies below 200 milliseconds as milliseconds matter in trading opportunities.
- INDMoney's system employs microservices powered by Go programming language for efficient and minimal resource consumption infrastructure.
- Key components include multicast streaming, NATS for reliable message delivery, protocol optimization using Protobuf, and client-side sampling for precise measurements.
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Analyticsindiamag
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What Skills Do You Need to Be a Data Engineer in 2025?
- Data engineering professionals are facing challenges in adapting to rapid technological changes due to the increasing demand for advanced skills, especially in the era of AI.
- Prakash Rajagopalan highlighted the impact of LLMs and generative AI on the industry, emphasizing the reshaping of the data engineering lifecycle and processes.
- Key areas for professionals include rethinking the data engineering lifecycle, adapting to new demand patterns, and leveraging AI tools effectively for tasks such as data modelling and code generation.
- Organisations and individuals need to focus on upskilling and adapting to new ways of working, embracing an AI-first approach to augment work processes with AI tools in the coming years.
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Medium
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What is Machine Learning? A Simple Explanation with Real-Life Examples.
- Machine Learning is a part of computer science where computers learn from data like humans learn from experience.
- Arthur Samuel coined the term Machine Learning in 1959, defining it as the ability for computers to learn without explicit programming.
- Machine Learning helps businesses and systems become smarter and faster by analyzing vast amounts of data.
- Examples of Machine Learning in everyday life include facial recognition for phone unlocking and personalized video suggestions on platforms like YouTube.
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VentureBeat
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You.com’s ARI Enterprise crushes OpenAI in head-to-head tests, aims at deep research market
- You.com has launched ARI Enterprise, claiming it outperforms OpenAI in head-to-head tests and excels in accuracy on independent benchmarks.
- ARI achieved 80% accuracy on the FRAMES benchmark, surpassing major competitors.
- This advanced research platform offers 4x greater depth and breadth and connects to internal corporate data sources.
- ARI Enterprise provides 35% more insights and facts per research project, with enhanced integration capabilities.
- Early adopters include venture capital firms, consulting agencies, and research institutions.
- The platform's interactive approach involves collaboration with users to refine research plans and guide analysis.
- Founder Richard Socher emphasizes ARI's role in augmenting analysts' efficiency, not replacing them.
- You.com believes ARI Enterprise democratizes access to high-quality research and transforms professional roles.
- The company raised $99 million to challenge Google's search dominance and has reported significant revenue growth.
- ARI Enterprise changes how businesses process information, offering comprehensive, verified analysis in minutes.
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Medium
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The Simplest Way to Understand Ocean Protocol’s Compute-to-Data
- Private data is valuable for research and innovation in industries like healthcare and finance, but using it while maintaining privacy is crucial.
- Ocean Protocol's Compute-to-Data allows for the value of data to be unlocked without giving it away, enhancing collaboration, innovation, and earning opportunities.
- Compute-to-Data (C2D) enables algorithms to access and process data without exposing the raw information, promoting data privacy in applications like healthcare, finance, and AI development.
- Key components of Compute-to-Data include data providers, data consumers, and compute providers, offering benefits like privacy preservation, monetization opportunities, and enhanced compliance with data protection regulations.
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Analyticsindiamag
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Using Huawei AI Chips Could Lead to Criminal Charges, US Warns
- The US warns against the use of Huawei AI chips, stating it could lead to criminal charges under US export control laws.
- The Bureau of Industry and Security rescinded the AI Diffusion Rule issued by former US President Biden, aiming to strengthen export controls on semiconductors globally.
- Huawei's Ascend processors face stringent export restrictions due to suspicions of utilizing US technology, complicating the company's efforts to create advanced AI chips and smartphones.
- The Trump administration previously imposed restrictions on Huawei in 2019, impacting the company's chip market, but it has since recovered with Chinese government support.
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Analyticsindiamag
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Twilio Partners With Microsoft To Advance Conversational AI
- Twilio and Microsoft announced a strategic partnership to boost conversational AI solutions globally.
- The collaboration aims to empower Twilio developers and Microsoft-managed customers to create AI-driven customer interactions.
- Twilio's communication and AI expertise, combined with Microsoft Azure AI's infrastructure, will enhance conversational AI adoption.
- New innovations include AI-powered agents, tools for customer service agents, and multi-modal solutions to improve digital customer interactions.
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Hackernoon
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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
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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
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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
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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
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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
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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
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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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