The hottest Big Data Substack posts right now

And their main takeaways
Category
Top Technology Topics

GroupBy #17: Pinterest’s new wide column database using RocksDB, Fault tolerance Kafka on Kubernetes at Grab

VuTrinh. • 19 implied HN points • 09 Jan 24
🕹 Technology Big Data
  1. Pinterest has developed a new wide column database using RocksDB for better data handling. This helps them manage large amounts of data more efficiently.
  2. Grab improved Kafka's fault tolerance on Kubernetes, ensuring their real-time data streaming service runs smoothly even when problems occur.
  3. The newsletter will evolve, offering more content types like curated resources on data engineering and personal insights every week.

GroupBy #14: What it takes to be a Senior IC at Meta, Netflix Data Engineering Summit

VuTrinh. • 19 implied HN points • 19 Dec 23
🕹 Technology Big Data
  1. To be a Senior Individual Contributor at Meta, focus on quickly adding value and aligning with the organization's goals. It's about making an impact and building good relationships within the team.
  2. Data modeling involves creating a shared understanding between business and data teams. It's essential for delivering valuable insights and ensuring everyone is on the same page.
  3. Job hopping in data engineering can be successful with the right approach. Make sure to deliver value early on and always be ready for new opportunities while enjoying your work-life balance.

Log Transformations for efficient multiplication between lots of positive numbers [Technique Tuesdays]

Technology Made Simple • 39 implied HN points • 02 Nov 22
🔬 Science Big Data
  1. Log transformations can be used for efficient multiplication between large numbers by converting the problem into addition of logs, making it more manageable.
  2. Logs have interesting properties that make them useful for handling computations with very large or very small numbers.
  3. Using log transformations is a clever math technique that is commonly used in fields like AI, Big Data, and Machine Learning to handle large computations.
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The unreasonable effectiveness of LLMs

John’s Contemplations • 19 implied HN points • 08 Mar 23
🕹 Technology Big Data
  1. LLMs have displayed surprising reasoning abilities like solving math problems using words.
  2. LLMs can be trained to use tools to address their weaknesses and improve tasks like code generation.
  3. LLMs work well due to the general nature of language, the breakdown of complex tasks into simpler steps, and the efficiency of neural networks like Transformers.

Data Science Weekly - Issue 463

Data Science Weekly Newsletter • 19 implied HN points • 06 Oct 22
🕹 Technology Big Data
  1. When you get a big CSV file, it's important to choose the right tools to explore and understand the data quickly.
  2. Using AI, like GPT-3, can help turn messy text into organized data, saving a lot of manual work.
  3. There's growing interest in using collective intelligence ideas to improve deep learning and AI research.

Data Science Weekly - Issue 454

Data Science Weekly Newsletter • 19 implied HN points • 04 Aug 22
🕹 Technology Big Data
  1. NASA is using machine learning to organize millions of astronaut photos of Earth. This technology helps scientists access and study these images more effectively.
  2. Data-driven companies can have a competitive edge in the market. The right expertise and data strategy can influence investors' decisions.
  3. There are many resources and discussions available online about using machine learning and data science effectively. Engaging with these can help keep skills and knowledge up to date.

Data Science Weekly - Issue 451

Data Science Weekly Newsletter • 19 implied HN points • 14 Jul 22
🕹 Technology Big Data
  1. Many people believe that data scientists today often do tasks very similar to data analysts. They're not just creating charts; there's a concern that their work lacks deeper statistical analysis.
  2. There's a lively debate about what it means to be a data scientist. While some argue the role has become too diluted, others believe that practical application in companies differs from academic definitions.
  3. Data science is evolving, with new techniques and applications emerging, like the importance of understanding datasets and using principles from various fields to improve intelligence in AI.

AIM turns 10!

Sector 6 | The Newsletter of AIM • 19 implied HN points • 23 May 22
🕹 Technology Big Data
  1. AIM has been around for ten years, showing significant growth in analytics and technology. It's impressive how much the industry has evolved in that time.
  2. The rise of data science and AI/ML has changed the business landscape. People are now recognizing the importance of these fields more than ever.
  3. One major success of AIM is its role in establishing analytics as a key tech stack in the industry. They have helped people understand the value of data in decision-making.

Meta's New AI model, Generating websites and 3D textures using natural language, economic potential of Generative AI and more

AI Brews • 20 implied HN points • 16 Jun 23
🕹 Technology Big Data
  1. Meta AI introduces a new Image Joint Embedding Predictive Architecture model that excels in computer vision tasks and is open-sourced.
  2. McKinsey's report highlights the economic potential of generative AI, estimating it could add trillions annually across various use cases.
  3. EU lawmakers pass regulations for AI systems, requiring review of generative AI like ChatGPT before commercial release and banning real-time facial recognition.

Data Science Weekly - Issue 403

Data Science Weekly Newsletter • 19 implied HN points • 12 Aug 21
🕹 Technology Big Data
  1. Be careful with machine learning! There are common mistakes that researchers make. It's important to build models carefully and evaluate them properly.
  2. A court in Australia has decided that AI can be considered an inventor. This is a big change in how we think about inventions and who gets credit for them.
  3. Natural Language Understanding (NLU) with just big data might not work as well as we think. It's time to rethink how we approach this challenge.

Data Week: How to manage your data

Joshua Gans' Newsletter • 19 implied HN points • 12 Oct 20
🕹 Technology Big Data
  1. Management of mission-critical data should ensure robust systems to avoid errors like the UK Excel scandal.
  2. Having a unified data infrastructure for COVID-19 reporting across various testing venues is crucial for accurate data collection.
  3. Lessons from data management failures, such as the UK Excel error, underline the importance of investing in advanced data systems for efficient pandemic handling.

The Belamy is the weekly newsletter of Analytics India Magazine. We share a curated list of best stories on AI & machine learning

Sector 6 | The Newsletter of AIM • 19 implied HN points • 07 Feb 21
🕹 Technology Big Data
  1. The Belamy newsletter shares top stories about AI and machine learning each week. It's a great way to stay updated in these fast-changing fields.
  2. Analytics India Magazine also highlights important technological advancements in analytics, data science, and big data. This helps readers understand new trends and innovations.
  3. You can sign up for a free trial to explore the newsletter's archives. This is a good chance to see if the content is a good fit for you.

Data Science Weekly - Issue 355

Data Science Weekly Newsletter • 19 implied HN points • 10 Sep 20
🕹 Technology Big Data
  1. DeepMind and Google Maps are using advanced Graph Neural Networks to improve the accuracy of travel time predictions, making them even more reliable in cities around the world.
  2. AI is now being used to detect deepfake videos by identifying unique signals from the videos, which can help spot how they were made.
  3. There are resources available to help people get started in data science, build their portfolios, and improve their resumes to land jobs in this field.

Data Science Weekly - Issue 333

Data Science Weekly Newsletter • 19 implied HN points • 09 Apr 20
🕹 Technology Big Data
  1. Data science roles often don't meet expectations due to issues like unclear job roles and lack of leadership.
  2. Monitoring machine learning models in production is complex and requires careful strategies to ensure effectiveness.
  3. Best practices in time series forecasting help improve the accuracy of predictions by utilizing advanced algorithms and example-driven approaches.

Data Science Weekly - Issue 300

Data Science Weekly Newsletter • 19 implied HN points • 22 Aug 19
🕹 Technology Big Data
  1. Adversarial Fashion aims to confuse surveillance cameras by using items like license plates. This shows how fashion can be used to challenge technology.
  2. A new AI optimizer called RAdam can improve accuracy for various AI models. It's a helpful update for anyone working with machine learning.
  3. Deep learning is making waves in genetics, showing that it can help explore DNA. This opens new possibilities for understanding and working with genetic data.

Data Science Weekly - Issue 266

Data Science Weekly Newsletter • 19 implied HN points • 27 Dec 18
🕹 Technology Big Data
  1. Netflix's data team often clashes with the content team, highlighting the importance of balancing data insights with creative decisions.
  2. Teaching AI to write generates funny results, showcasing the difficulties of making machines understand human language.
  3. Data is not just raw information; it is influenced by human judgment and context, making it essential to analyze it carefully.

Data Science Weekly - Issue 257

Data Science Weekly Newsletter • 19 implied HN points • 25 Oct 18
🕹 Technology Big Data
  1. Neural networks can help create fun and unique Halloween costumes. Using AI for creative tasks can lead to new ideas we might not think of ourselves.
  2. Uber processes massive amounts of data very quickly, showing how big data can improve services and make operations smoother. Their platform manages over 100 petabytes of information.
  3. Learning data science can be made easier with mentorship and flexible payment options. Programs like Springboard's help you get job-ready skills while supporting your career journey.

Data Science Weekly - Issue 249

Data Science Weekly Newsletter • 19 implied HN points • 30 Aug 18
🕹 Technology Big Data
  1. Netflix is using notebooks for development and collaboration, helping manage many scheduled jobs more effectively.
  2. Understanding the world in 3D is challenging, especially for extending successful technologies like convolutional networks.
  3. There's a creative idea to enhance shopping experiences for color-blind clients by pairing their selections with personalized music.

Data Science Weekly - Issue 242

Data Science Weekly Newsletter • 19 implied HN points • 12 Jul 18
🕹 Technology Big Data
  1. There's a big focus on how artificial intelligence has evolved in the past year, with many players in the market and new trends shaping its future.
  2. Understanding the difference in approaches to machine learning is crucial for businesses, as many struggle when they don't recognize the distinctions.
  3. New methods in machine learning, like generating detailed ground views from satellite images, show how technology can create innovative solutions to complex problems.

Data Science Weekly - Issue 237

Data Science Weekly Newsletter • 19 implied HN points • 07 Jun 18
🕹 Technology Big Data
  1. Understanding how the human brain works can improve our grasp of complex environments. This knowledge helps in both neuroscience and technology applications.
  2. The future job landscape will involve more collaboration between humans and machines. Companies need to prepare for a mix of human and automated roles.
  3. Deep learning techniques are evolving, especially in object detection. Innovations in this field show how minor adjustments can lead to significant improvements in performance.

Data Science Weekly - Issue 236

Data Science Weekly Newsletter • 19 implied HN points • 31 May 18
🕹 Technology Big Data
  1. Natural disasters like Hurricane Maria can have serious health impacts, and it's hard to get an accurate death count afterward.
  2. Improving training data is key to making better machine learning models, and there are practical ways to enhance that data.
  3. Reproducibility in machine learning is important, but it can be tough to achieve and often requires careful planning and work.

Data Science Weekly - Issue 226

Data Science Weekly Newsletter • 19 implied HN points • 22 Mar 18
🕹 Technology Big Data
  1. A Senior Data Scientist's role is often unclear and expectations can vary widely. It can be helpful to define what skills and responsibilities are actually needed.
  2. Digital evolution in AI can show surprising creativity that doesn't always match our expectations. This means evolution can create new ideas in unexpected ways.
  3. There's a big conversation about AI and responsibility. When AI causes harm, it's tough to figure out who should be accountable for it.

Data Science Weekly - Issue 219

Data Science Weekly Newsletter • 19 implied HN points • 01 Feb 18
🕹 Technology Big Data
  1. Deep learning education needs a common way to explain why different layers exist. Right now, it’s taught differently than other technical fields.
  2. You can create autonomous driving models using simulation environments like AirSim. This lets you train a model to steer a car just with camera input.
  3. Learning matrix calculus helps in understanding deep learning better. This knowledge is crucial for mastering the training of deep neural networks.

Data Science Weekly - Issue 206

Data Science Weekly Newsletter • 19 implied HN points • 02 Nov 17
🕹 Technology Big Data
  1. A Fortune 50 company is looking to build a strong data science team in NYC. They want to hire both senior and junior data scientists.
  2. There's an interesting article about how humans are currently better than AI at playing StarCraft. A human gamer won a contest against AI with a score of 4-0.
  3. A new tool called Bounter can quickly count item frequencies in large datasets. It uses little memory and is designed for speed.

Data Science Weekly - Issue 206

Data Science Weekly Newsletter • 19 implied HN points • 02 Nov 17
🕹 Technology Big Data
  1. A big company is looking to hire a skilled data science team in NYC, including both senior and junior positions. If you're interested, reach out with your details.
  2. There are various articles about interesting projects in data science, like using machine learning for costume recommendations and understanding what causes wildfires. These kinds of studies show the diverse applications of data science.
  3. New tools and resources are being developed to make data science easier, like TensorFlow's eager execution. These advancements help data scientists to work more effectively with large datasets.

Data Science Weekly - Issue 202

Data Science Weekly Newsletter • 19 implied HN points • 05 Oct 17
🕹 Technology Big Data
  1. Algorithms can be used in designing unique structures, like concert halls, by creating specific shapes for materials based on calculations.
  2. Understanding bias in AI is crucial because it can lead to intelligent systems that reflect human prejudices rather than being fair.
  3. New York City is seen as a top place for data scientists to grow their careers and for companies to build strong data teams.

Data Science Weekly - Issue 2

Data Science Weekly Newsletter • 39 implied HN points • 05 Dec 13
🕹 Technology Big Data
  1. Visual image extraction can enhance social image searches using big data techniques. This can help businesses understand how their images are perceived online.
  2. Probabilistic programming can model complex, unseen factors in finance that influence market behavior, like investor fear. This approach provides better tools for understanding market trends.
  3. Big data technologies can analyze social media pictures to find popular locations, helping businesses discover the best spots to attract customers.

Data Science Weekly - Issue 1

Data Science Weekly Newsletter • 39 implied HN points • 28 Nov 13
🕹 Technology Big Data
  1. To make big data useful, it needs to be connected to insights and actions that help decision makers. Without this connection, data can just confuse rather than clarify.
  2. Big data is being applied in many ways that can create real benefits in different areas. These applications can have a major positive impact on various industries and society.
  3. There are powerful tools like Python that data scientists use for analysis and visualization, which help in working with data effectively. It's becoming a popular choice due to its versatility and ease of use.

Data Science Weekly - Issue 186

Data Science Weekly Newsletter • 19 implied HN points • 15 Jun 17
🕹 Technology Big Data
  1. Data science is key in optimizing services like Netflix, helping to deliver content efficiently worldwide.
  2. New algorithms can summarize long texts well, which can help in areas like medicine and law by making information easier to understand.
  3. Building visual maps and understanding neural networks are important steps in advancing data science and machine learning fields.

Data Science Weekly - Issue 185

Data Science Weekly Newsletter • 19 implied HN points • 08 Jun 17
🕹 Technology Big Data
  1. The Google Brain Residency Program allows people to work with top scientists in machine learning and deep learning for a year. It's a great opportunity to learn and network in a cutting-edge field.
  2. Natural language processing can help analyze products like wine by using descriptive language instead of traditional data. This approach can uncover unique insights about different wines.
  3. New AI features in tools like Google Sheets aim to automate tasks and improve office efficiency. These smart tools can eventually help companies work faster and smarter.

Data Science Weekly - Issue 181

Data Science Weekly Newsletter • 19 implied HN points • 11 May 17
🕹 Technology Big Data
  1. Using deep learning can significantly improve how algorithms rank content, like Twitter does with its timelines.
  2. Companies like Airbnb use A/B testing to experiment and understand how changes to their platform affect users.
  3. New technologies in AI are being developed, such as visual attribute transfer and mind-reading algorithms, which could change how machines understand and interact with the world.

Data Science Weekly - Issue 175

Data Science Weekly Newsletter • 19 implied HN points • 30 Mar 17
🕹 Technology Big Data
  1. Deep learning is becoming important for various parts of companies like Facebook. It's not just a special skill; it's useful everywhere from messaging to ads.
  2. Nvidia is focusing on making chips that can help improve healthcare through AI. They see medicine as a big chance to apply their technology.
  3. Data visualization is crucial for understanding information. Tools like Pandas and Seaborn help people make sense of data easily.

Data Science Weekly - Issue 171

Data Science Weekly Newsletter • 19 implied HN points • 02 Mar 17
🕹 Technology Big Data
  1. Deep learning has evolved from basic neural networks to advanced models. This includes popular types like convolutional and recurrent neural networks.
  2. Mathematicians looking at data science should consider what aspects of the job they enjoy. Knowing your interests can help in applying to the right roles.
  3. Time series modeling is tricky because past data points can influence each other. New strategies are needed for better accuracy in this kind of data.

Data Science Weekly - Issue 170

Data Science Weekly Newsletter • 19 implied HN points • 23 Feb 17
🕹 Technology Big Data
  1. You can use data and APIs to analyze music, like finding the saddest Radiohead song. This shows how data science can be fun and creative.
  2. Neural networks can change images, like making faces look older or younger. This technology is evolving and has cool applications in photography.
  3. Different approaches to statistics, like frequentist and Bayesian, can shape how we think about data. It's important to understand these methods to analyze problems better.

Data Science Weekly - Issue 168

Data Science Weekly Newsletter • 19 implied HN points • 09 Feb 17
🕹 Technology Big Data
  1. P-values and null hypothesis testing are often seen as problematic in scientific research, with many issues arising from their use.
  2. Joining a social network app can encourage people to exercise more, showcasing the impact of social interactions on personal habits.
  3. Machine learning is being used to predict parking difficulties, helping drivers find parking spots more efficiently.

Data Science Weekly - Issue 161

Data Science Weekly Newsletter • 19 implied HN points • 22 Dec 16
🕹 Technology Big Data
  1. Machine learning can solve big social problems, but it's important to be careful about potential misuse. We should focus on using it wisely to get the best results.
  2. There is a free resource for learning deep learning that makes advanced concepts accessible to everyone. It’s great for beginners who want to get into AI without too much complexity.
  3. XGBoost is a popular tool because it is very effective for classification problems in data science. People should consider using it in their projects for better accuracy.