James Courtney

Just watched the new Twister movie, and it got me thinking about the incredible strides we've made in tornado prediction thanks to AI and advanced data analytics. Today, we’re harnessing the power of AI to analyze massive datasets, enabling us to predict tornadoes with unprecedented accuracy. Machine learning algorithms, such as neural networks and random forests, sift through vast amounts of meteorological data, identifying patterns and anomalies humans might miss. These models are trained on historical weather data, improving their ability to forecast tornado occurrences and enhance early warning systems. This precision not only bolsters early warning systems but also has the potential to save lives and mitigate damage. These groundbreaking advancements are revolutionizing our approach to natural disasters, marking a new era in predictive technologies. AI and Big Data Analytics are transforming meteorological science, enhancing our ability to anticipate and respond to tornadoes more effectively than ever before. The future of disaster preparedness is here, driven by data and innovation. #MeteorologyAdvancements #DisasterPreparedness #AI #BigData #InnovationInScience

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I worked again with the Basel Infra team to dive into the Data Engineering and AI infra's performance, explore new features, and analyze costs. Over the past three weeks, I've spent a total of €473.06, but I could have saved around €100 by optimizing pod resource allocation. Placeholder pods alone cost approximately €70. - Data scientists are now using multiple Jupyterhub Notebook kernels for parallel tasks. To save resources, I might deploy various JupyterHub configurations with specific kernel categories. - The number of Metabase dashboards has grown significantly, so I'm integrating a way to interact with the API directly from notebooks. - Switched from PySpark to DuckDB, with multiple instances tailored to different resource needs. - Integrating Debezium to test database replication use cases; Kafka is already up and running. - Deploying Bytewax for both streaming (Python) and database synchronization (if it proves effective). - Building a platform agent using Lobechat. - Integrating @grafana specifically for cost alerts! To reduce startup times, I initially added eight placeholder pods for developers, anticipating node boot-up lag. However, after monitoring, it turned out the cost of placeholders far exceeded the actual boot-up time savings, so I removed them. Next phase priorities will focus on streaming technologies and experimenting with additional options. Finally, we'll work on building and integrating an AI agent across the platform. Thanks to the Basel Infra team—you all rock! 🙏 📷🚀

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𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲: 𝗧𝗵𝗲 𝗛𝗶𝗱𝗱𝗲𝗻 𝗖𝗵𝗮𝗺𝗽𝗶𝗼𝗻 𝗼𝗳 𝘁𝗵𝗲 𝟮𝟬𝟮𝟰 𝗣𝗮𝗿𝗶𝘀 𝗢𝗹𝘆𝗺𝗽𝗶𝗰𝘀 As the world watches the thrilling performances at the Paris 2024 Summer Olympics, I'm fascinated by how data science plays a crucial role behind the scenes. Here's a look at how big data and AI are shaping the Olympic experience right now: 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: Athletes and coaches leverage advanced sensors and ML algorithms for instant feedback and fine-tuning strategies between events. 𝗦𝗺𝗮𝗿𝘁 𝗩𝗲𝗻𝘂𝗲𝘀 𝗶𝗻 𝗔𝗰𝘁𝗶𝗼𝗻: IoT devices and data analytics optimize everything from crowd flow to energy usage in Olympic venues, enhancing sustainability and the spectator experience. 𝗟𝗶𝘃𝗲 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: Data scientists are updating sophisticated models in real-time, predicting medal outcomes as the games progress, captivating viewers, and informing team strategies. 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗩𝗶𝗲𝘄𝗶𝗻𝗴 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: AI-driven content curation allows broadcasters to deliver tailored Olympic coverage, keeping viewers engaged across different time zones and demographics. 𝗢𝗻𝗴𝗼𝗶𝗻𝗴 𝗔𝗻𝘁𝗶-𝗗𝗼𝗽𝗶𝗻𝗴 𝗘𝗳𝗳𝗼𝗿𝘁𝘀: Machine learning algorithms actively support fair play, helping officials detect and prevent the use of performance-enhancing substances. 𝗟𝗶𝘃𝗲 𝗚𝗹𝗼𝗯𝗮𝗹 𝗜𝗺𝗽𝗮𝗰𝘁 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴: Big data is helping quantify the real-time economic and social impacts of the Olympics on Paris, providing valuable insights for future host cities. As data professionals, we're not just spectators – we're part of the technology driving this global event. The ongoing Olympics showcase how our field enhances human performance, engages global audiences, and drives innovation on a world stage. I love working in a field that can significantly impact many aspects of life. #𝗗𝗮𝘁𝗮𝗦𝗰𝗶𝗲𝗻𝗰𝗲 #𝗢𝗹𝘆𝗺𝗽𝗶𝗰𝘀 #𝗣𝗮𝗿𝗶𝘀𝟮𝟬𝟮𝟰 #𝗦𝗽𝗼𝗿𝘁𝘀𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 #𝗔𝗜 #𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 #𝗢𝗹𝘆𝗺𝗽𝗶𝗰𝘀𝗟𝗶𝘃𝗲

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🚀 Choosing the Right Data Visualization Tool: Julia, R, Python, or SAS? 📊 As data scientists, we’re spoiled for choice when it comes to selecting the best tool for data visualization. Let’s break down the contenders: Julia: Speedster: Julia’s exceptional speed makes it a favorite for large datasets and complex computations. User-Friendly Syntax: Julia’s straightforward syntax is a breath of fresh air. Plots.jl: Julia’s visualization package, Plots.jl, offers a wide range of options. Beyond Data Science: Julia isn’t just for data science—it’s also a powerful scientific programming language. Pros: Speed, ease of use, and flexibility. Cons: Smaller community compared to R and Python. R: Statistical Powerhouse: R specializes in statistical analysis, data science, and manipulation. ggplot2: R’s ggplot2 package is like a Picasso for data visualization—expressive and customizable. Reproducibility Champion: R emphasizes reproducibility, crucial for research. Pros: Powerful tools, extensive packages, and an active community. Cons: Steeper learning curve for beginners. Python: Jack of All Trades: Python’s versatility extends from data science to machine learning and beyond. Libraries Galore: NumPy, Pandas, and Matplotlib simplify data manipulation and analysis. Industry Darling: Python’s industry adoption is off the charts. Pros: Simplicity, versatility, and a massive community. Cons: May lag behind R and SAS for specific statistical tasks. SAS: Business Savvy: SAS rules the business and finance world for data management and analysis. User-Friendly Interface: SAS holds your hand through statistical procedures. Proprietary Enigma: It’s proprietary, which means limited access to source code. Pros: Beginner-friendly and comprehensive. Cons: Expensive and lacks the openness of open-source languages. In Summary: Julia: Speed and flexibility. R: Statistical wizardry and expressive visuals. Python: Swiss Army knife with a bustling community. SAS: Beginner-friendly but locked in a vault. Choose wisely based on your project needs and personal flair! 🌟🔍💡

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Balancing Data Science and Vacation: A Mindful Approach As data scientists, we are often deeply immersed in the world of analytics, algorithms, and model building. However, it's crucial to maintain a healthy work-life balance, which includes taking well-deserved vacations. In this post, I'll share a mindful approach to help us stay productive while enjoying some much-needed time off. First, let's acknowledge the challenge. It's easy to feel the pull of our data science projects, even when we're supposed to be relaxing. The fear of falling behind or missing critical insights can be overwhelming. But the truth is, a well-rested and rejuvenated data scientist is often more productive and creative than one who is constantly burning the midnight oil. Here's how we can strike a balance: 1. Automate your workflows: Leverage tools and technologies to create automated data pipelines and model training processes. This way, your work can continue to run in the background while you're away. 2. Establish monitoring and alerting: Set up a system that can monitor the health of your data science workflows and notify you if any issues arise during your vacation. This will give you peace of mind knowing that you can address any problems quickly. 3. Delegate tasks to your team: If you have the privilege of working with a team of data scientists, ensure that critical tasks are assigned to capable team members who can handle them in your absence. 4. Embrace the vacation mindset: When you're on vacation, truly disconnect from work. Resist the urge to constantly check your email or fiddle with your laptop. Immerse yourself in the present moment and allow your mind recharge. I am guilty of forgetting to take time to recharge and every time I take the time to get away from work I realize how important it is for your mental state of mind. #DataScience #DataScientist #Vacation #WorkLifeBalance #Automation #Airflow #ProductiveVacation #DataPipelines

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In 2024, the landscape of Python packages in Data Science continues to evolve with remarkable advancements. The ever-growing list of tools makes it easier for data scientists to extract valuable insights from complex datasets. One such tool is Pandas, a stalwart package that remains an essential part of any data scientist's toolkit due to its robust data manipulation capabilities. NumPy and SciPy continue to be popular for their high-level mathematical functions. Matplotlib and Seaborn remain favorites on the visualization front, while Plotly has gained traction for its interactive plotting features. Machine Learning libraries like Scikit-Learn and TensorFlow have advanced significantly, offering comprehensive algorithms and models for various applications. There are many more on the list you can see below that will enhance your options for analysis. Lastly, let us not forget Jupyter Notebooks—a vital environment that provides an interface for combining code, data analysis, and reports. Stay curious and keep learning! #PythonPackages #DataScience2024

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Continuing **Interview ProTip** Unlike software development, the technical process in data science is not standardized across companies. Therefore, it is important for candidates to connect the dots in terms of data capture, processing, feature engineering, and deployment/retraining. When interviewing candidates, I always ask them to explain their technical process once I have understood the problem statement. I want to know how they collect data, what tools they use for processing, how they engineer features, and how they deploy and retrain their models. However, many candidates struggle to explain this process clearly. They may jump from one step to another without providing a clear explanation of how they got from point A to point B. This can be frustrating for interviewers, as it makes it difficult to assess the candidate's technical skills. To avoid this issue, I recommend that candidates take the time to clearly map out their technical process before the interview. This will help them to connect the dots and provide a clear explanation of their work. One approach I use is to create data flow and architecture diagrams to explain the chain of processes. In conclusion, candidates should focus on connecting the dots in their technical process during the interview process. By mapping out their process and providing a clear explanation, they can demonstrate their technical skills and stand out in the interview process. #interviewtips #datascience #machinelearning

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Machine Learning is redefining the landscape of Data Science—and opening doors like never before! Gone are the days when diving into Data Science meant grappling exclusively with hardcore statistics and complex mathematical formulas. Thanks to advancements in Machine Learning, the emphasis has shifted. Now, it's more about how we organize data and apply powerful models to extract meaningful insights. This evolution is making Data Science more accessible and fun for everyone, even those who might not have a traditional background in statistics or math. It's encouraging to see people from diverse fields step in, bring fresh perspectives, and make significant impacts within their organizations. If you've ever been curious about data science but felt daunted by the technicalities, now is the time to take the plunge. With the right tools and a thirst for knowledge, you can be part of groundbreaking projects and drive real change. The post below can help you understand some of the basic concepts and help to understand some of the aspects of Machine Learning. #MachineLearning #DataScience #CareerGrowth #Innovation #DataAnalytics

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Just wrapped up an intensive 3-day Snowflake for Data Science Training. We delved into a myriad of topics including: ►Query performance enhancements such as Clustering keys, Query Acceleration Service, and Search Optimization ►Handling semi-structured and unstructured data ►Data acquisition methods, Snowpipe, and Auto Ingest ►Utilizing Table Streams & Tasks ►Exploring Snowpark for enhanced data processing ►Harnessing the power of Vectorized UDFs and Stored Procedures ►Diving into ML Modeling API and MLOps API for advanced analytics and operationalization A huge shoutout to Tycho Juta for delivering such an engaging and insightful material & presentation! Your expertise truly made the learning experience exceptional. Excited to apply these newfound skills and knowledge to drive impactful insights at Sanofi! #Snowflake #DataScience #ML #Analytics #Certification #ContinuousLearning

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Generative Transformer model sampling in the inference process consists of two phases, prefill and autoregressive sampling. In the prefill phase, the model ingests the prompt tokens in parallel, populating the so-called key-value (KV) cache. It represents the state of a model, and it is nestled within the attention operation. No token generation happens at prefill time, while in the autoregressive sampling phase, a model leverages the current state stored in the KV cache to sample and decode the next token. A small price is paid then in terms of storage (GPU VRAM, or RAM when no GPU is available) to achieve a significant gain in terms of speed. Unfortunately, latest #LLM architectures having a longer context length could turn this mechanism into a speed bottleneck, because of the increased size of used memory for the KV cache. Would any post-training workaround be possible to mitigate this side effect? #quantization could be a straightforward and effective solution to reduce KV cache size, as it reduces the total number of bytes taken by it. One of very few papers addressing this matter lately is #KIVI: in this work the authors suggest a tuning-free asymmetric 2-bit quantization for the KV cache. It has been tested for the #Llama, #Falcon and #Mistral model families only, but the #opensource nature of the paper code gives room for implementation of the same strategy towards other LLM architectures. On average the generated content quality of the models post KIVI quantization matches the original without KV cache quantization, while speed increases because less 2.6X less peak memory is used (it results in larger batch sizes and a higher throughput at inference time). All the relevant links in the comments. #AI #GenAI #Python #MLOps

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𝐇𝐨𝐰 𝐝𝐨 𝐰𝐞 𝐮𝐬𝐞 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 (𝐑𝐋) 𝐭𝐨 𝐩𝐮𝐬𝐡 𝐩𝐞𝐩𝐭𝐢𝐝𝐞 𝐝𝐫𝐮𝐠 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 𝐛𝐨𝐮𝐧𝐝𝐚𝐫𝐢𝐞𝐬? First let me explain what RL is. RL trains Machine Learning (ML) systems to learn from their actions, using rewards to shape behavior. Each decision leads to feedback, encouraging the ML to refine its choices and adapt over time. This process is particularly powerful for tackling complex, unknown challenges—like discovering new peptides. However, RL isn’t without pitfalls. If your data is limited to a closed dataset (e.g., only using canonical, naturally occurring peptides), the ML will remain confined to the existing knowledge, much like teaching basketball using only NBA footage. The chance of learning new techniques is slim. Read my previous posts on this topic > https://lnkd.in/dVUknMCs Now, imagine applying that same National Basketball Association (NBA) footage to a different scenario—teaching elderly individuals basketball. Their handshakes, muscle memory, and techniques won’t match the NBA model. Here, traditional training fails, and the system needs to adapt, redefining the model to reward behaviors that lead to success, even if they deviate from what was once considered ideal. At Pepticom Ltd., we use this approach to push the boundaries. By rethinking how we select and train data, we create models that explore uncharted chemical spaces and reward innovative, non-canonical peptide behaviors. This combination of carefully selecting the data and strategically rewarding novel outcomes sets our AI peptide discovery platform apart from others. In peptide drug discovery, innovation means breaking free from bias and exploring beyond existing knowledge—and that’s what we aim to do with reinforcement learning. #AI #ReinforcementLearning #DrugDiscovery #Peptides

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Microsoft PowerPoint is more crucial than a Python or R in the realm of Data science & Machine learning A correlation score of 0.7 or a model accuracy of 75% or that beautiful elbow curve in scree plot, which appear like jewels on your output window can be lost on a Power point slide if your insight fails to land on the stakeholder. Powerpoint and narration are extremely crucial to Data science and PowerPoint is here to stay :)

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𝗧𝗵𝗲 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲: 𝗙𝗿𝗼𝗺 𝗢𝗯𝘀𝗰𝘂𝗿𝗲 𝗧𝗲𝗿𝗺 𝘁𝗼 𝗗𝗿𝗶𝘃𝗶𝗻𝗴 𝗙𝗼𝗿𝗰𝗲 𝗼𝗳 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 Have you ever wondered how "Data Science" became the buzzword it is today? Let's take a quick journey through its fascinating evolution: 1️⃣ Origins (1960s-1970s): 1962: John Tukey coined "data analysis," laying the groundwork 1974: Peter Naur first used "data science," emphasizing computer science in data processing 2️⃣ Growth Period (1990s-2000s): Rise of computational statistics and data mining Increase in data generation and storage capabilities 3️⃣ Big Data Era (2000s-2010s): 2008: "Big Data" enters the mainstream 2012: Harvard Business Review dubs Data Scientist "The Sexiest Job of the 21st Century" 4️⃣ Modern Data Science (2010s-Present): Integration with AI and Machine Learning Focus on ethical considerations and data privacy Widespread adoption across industries 5️⃣ The Future: Emphasis on interpretable AI and explainable models Growing importance of data literacy in all professions Data Science has come a long way from a niche term to a driving force of innovation. As we continue to generate vast amounts of data, the role of Data Scientists becomes ever more crucial in shaping our world. #DataScience #BigData #Innovation #TechEvolution #LinkedInLearning

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How to build learning dashboards from research papers - Very Easy - Ask Claude! Credits: Superhuman AI Use Claude 3.5 Sonnet as your model on Claude.ai Upload your research paper and use the prompt: Create an interactive and detailed dashboard to present the learnings from this research paper. Make it look good and professional. Refine your prompt if needed. Wait for a few seconds and you are done. Voilà! It is done. Have a nice ice cream with the time your saved.

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The Music of Data Science As I listen to the Black Keys' new album "Ohio Player," I'm struck by the fascinating parallels between music and data science. Having been a fan of the Black Keys since their early days, I've witnessed their evolution and the consistent quality of their work. This latest album is no exception – it's a masterpiece of rhythm, melody, and harmony working together seamlessly. This got me thinking: isn't data science similar in many ways? In music, you need the right rhythms and beats. Every element has to come together perfectly to create a compelling song. Similarly, in data science: 1. Rhythm = Data Patterns   Just as a good rhythm forms the backbone of a song, identifying patterns in data is fundamental to any data science project. 2. Melody = Algorithms   The melody carries the main idea of a song. In data science, our algorithms are the melodies that carry our analysis forward. 3. Harmony = Integration of Multiple Data Sources   Like harmonizing instruments, bringing together diverse data sources creates a richer, more comprehensive analysis. 4. Composition = Problem-Solving Approach   The overall composition of a song is analogous to our approach to solving complex data problems. It requires creativity, structure, and an understanding of how all parts work together. 5. Performance = Visualization and Presentation   Finally, just as musicians perform for an audience, data scientists must present their findings effectively through compelling visualizations and clear communication. In music and data science, success comes from finding the right elements and ensuring they work symbiotically. It's about creating something that resonates, whether it's with listeners or stakeholders. #DataScience #Music #Analytics #Creativity

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Writing Python code isn’t just about doing the job. It’s about code being cleaner, faster & easier to maintain. In my experience, automating repetitive tasks using Python’s built-in functions has drastically improved my workflow and boosted productivity. Here’s what I’ve learned: → map(), filter(), and lambda streamline data transformations. → List comprehensions and zip() simplify loops and enhance readability. These tools become essential in data-heavy environments where performance and clarity are critical for scaling operations. In this guide, I’ll walk you through 10 powerful Python functions that I use regularly to automate manual tasks and optimize my scripts. Expect to learn: → Apply map() & filter() for faster data processing. → Use list comprehensions and enumerate() for concise, readable code. → Simplify complexity & boost performance in Python scripts. Swipe through and start coding smarter. ----------------------------- Found this helpful ? Like & Share. Follow me - I'm Varun Sagar Theegala and you'll find me posting my learnings, experiences & conversation with industry professionals daily Tags : Shashank Singh 🇮🇳 | Munna Das | Mandar Patil | Vaibhav Lambat | Ayan Khan | Srinath Ankolla | Thodupunuri Bharath | Venkata Naga Sai Kumar Bysani | AMAN KUMAR | Adapala Naveen Kumar | Simala Om Prakash | Neil B. | Preeti Moolani | Neha Panjabi | Bigyandutt P. #dataanalytics #python #functions #comprehensions #dataanalysis

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🔍 Selecting the Right Machine Learning Model: A Blend of Art and Science (Why it’s more than just picking a popular algorithm) In the constantly evolving field of machine learning, selecting the right model involves more than following the latest trends—it requires a deep understanding of the problem, the data, and the nuances of each model. Here’s why: 1. No single model is a silver bullet. What works wonders for one dataset might falter with another. This is why model selection is often an iterative process—testing and validating multiple rounds to get it right. 2. Consider all the factors. Model interpretability, computational efficiency, and the nature of your data (structured vs. unstructured, balanced vs. imbalanced) significantly impact the best model choice. 3. Test multiple models. Compare them all, from linear models and decision trees to neural networks and ensemble methods. Through this rigorous experimentation, we can move beyond assumptions to data-driven decisions. Finding a suitable machine learning model is both a science and an art. It requires a deep understanding of algorithms, attention to detail, and a commitment to ongoing experimentation. 🚀 Takeaway: Never settle for the first model that works. Challenge yourself to test, iterate, and discover the best possible solution. Your data—and your project—deserve nothing less. #MachineLearning #DataScience #AI #ModelSelection #DataDriven

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For any data science and AI-related use case, having high-quality data is an integral component. Data with missing values can significantly impact any data-related project. Data imputation is a process used to fill in missing values in a dataset. Imagine you have a table with some empty cells; data imputation helps you guess what those missing values might be based on the other information you have. This way, your data becomes complete and more useful for analysis.   This article highlights various techniques, with examples, related to data imputation. #datascience #AI #dataquality #dataimputation #takeda

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