AWS for Healthcare & Life Sciences’ Post

The #AWSSummit New York keynote is in the books. ☁️ 🗽 📕 We caught up with William McQueen, VP, Head of Data Assets & Analytics, Bayer | Crop Science, fresh off the #AWS Summit stage to discuss his post-keynote emotions, what excites him the most about #generativeAI, & how #AmazonQ is impacting his workloads.

The classification of tomato leaf diseases has long been a significant challenge in the agricultural sector. To address this issue, I embarked on a comprehensive project where I meticulously crafted an end-to-end MLOps pipeline, encompassing every critical stage from data ingestion to production deployment. This innovative solution was meticulously designed to operate seamlessly on Microsoft Azure's robust infrastructure, ensuring scalability, reliability, and accessibility. The objective was not just to classify diseases but to democratize access to this valuable tool. To make it user-friendly, I developed a Rest API that simplifies interaction, eliminating the need for clients to write complex code. Additionally, I incorporated a user-friendly interface, enabling clients to effortlessly harness the system's power with minimal effort. This endeavor represents a significant step towards addressing the persistent challenges faced by agricultural professionals, offering them a powerful tool for disease identification and ultimately contributing to improved crop yields and food security. Ayush Singh Dragos Stan Adebolajo Sunday Nabafat.AI Azure DevOps Consulting Microsoft Azure #machinelearning #deeplearning #production #computer_vision #Mlops #cicd #mlfow

Its easy to get frustrated with how things may be going and the speed you want to see. I find it helpful to look backwards occasionally to see how far we have come. Inari recently received our Databricks physical award and it is amazing to see how far Rania Khalaf and our team have come in the last almost two years. Exciting to see how much progress was made to build the data and ML platform, enabling unlocking the true potential of seeds. #databricks #dataForGood

I'm at the Databricks AI and Data Summit in San Francisco this week. The question I am seeking to answer is "How can we make great decision-making more affordable and accessible to everyone?" Currently, it's really hard and expensive to produce great analytic insights. And those insights are super valuable. Let me explain with an illustration. How mass production has elevated humanity: ---------------------------------------------------- Over the past 300 years, the masses of humanity have elevated their quality of living through mass production. Take the example of the Green Revolution in agriculture: The introduction of high-yield crop varieties and advanced farming techniques dramatically increased food production. This helped reduce hunger and improve nutrition worldwide, showcasing the transformative power of mass production. By lowering the cost of goods and services, we’ve made them more affordable and available to more people. Why can't we translate this idea to the realm of insights?

Excited to announce that I’ll be speaking about end-to-end MLOps at INVISTA using Snowflake. This is my first on an external platform, and I’m eager to share insights and learnings. Join us to find out on how Snowflake can streamline your MLOps processes and best practices.Looking forward to seeing you there! Shoutout to these pros for all the help. Tyler White, Kirk Mason , Lucy Z. #MachineLearning #MLOps #Snowflake

Chicken health classification using CNN 🚀 helpful for POULTRY FARMING (With MLOps Concepts) 1. Data: chicken health Dataset, 2 classes, and 2100 images. 2. Model Training: The model was trained using a Convolutional Neural Network (CNN), specifically ResNet50V2, with the application of fine-tuning techniques to enhance accuracy. 3. MLOps: MLOps tools including CI/CD Pipelines, Data Version Control (DVC), DagsHub, MLflow, and Git Actions, along with deployment capabilities on Microsoft Azure cloud platforms. 4. Deployment: The application was successfully deployed and is operational on Azure Cloud environments. Key Features: - Model Training: Accessible through a dedicated URL for training purposes (URL/train). - Continuous Integration: Code is automatically pushed to GitHub, subsequently triggering seamless deployments to Azure Cloud for production use. I hope you find this information valuable. P.S. If you're interested in delving deeper into the world of MLOps and deploying models in the cloud, please feel free to connect with me on GitHub, LinkedIn, and Twitter (Links in my bio). #machinelearning #deeplearning #computervision #imageclassification #azurecloud #mlops" #github #githubactions

I am excited to share : I have successfully completed my real time data science project - mushroom classification. I have done EDA, Data preprocessing, feature scaling and machine learning to complete the project. This project is all about predicting mushroom is poisonous or edible based on different features using Machine learning models. I am thankful to Learnbay that they supported me in this journey. #datascience #learnbay #machinelearning

🌟 Thrilled to share insights from the recent Snowflake Data Summit! 🌟 The data catalog landscape is heating up. Snowflake had its sights set on the startup Tabular, which built a powerful catalog on the Iceberg format, competing with Databricks' Delta Lake. In a strategic move, Databricks acquired Tabular to strengthen its position. Not to be outdone, Snowflake announced Polaris, an open-source catalog on Iceberg. These industry moves underscore a unified shift towards open table formats, diminishing the traditional reliance on a warehouses and accelerating data value generation by reducing data movement. #DataSummit #Snowflake #Databricks #DataStrategy #AI #DataCatalog #OpenTableFormat

🍷 "Harnessing Data Science to Predict Wine Quality" 📊✨ Exciting news from the world of data science! 🚀 I am thrilled to share insights from a recent project where I explored the fascinating intersection of Wine and Technology. Using advanced data science and machine learning techniques, I developed a model to predict wine quality with remarkable accuracy. 🔍 Project Highlights: Data Exploration: Analyzed a diverse dataset of wine attributes including acidity, sugar content, and pH levels to understand the key factors affecting wine quality. Feature Engineering: Created and refined features to enhance model performance, transforming raw data into actionable insights. Model Development: Applied various machine learning algorithms, from regression to classification, to build robust prediction models. Validation & Testing: Employed rigorous validation techniques to ensure the reliability and accuracy of our predictions. The results? I achieved significant improvements in predicting wine quality, offering valuable insights for both producers and consumers. This project underscores the power of data science in transforming industries and making data-driven decisions. Looking forward to exploring more ways to leverage data science in innovative fields! Cheers to data-driven success! 🍷📈 #DataScience #MachineLearning #WineQuality #PredictiveAnalytics #DataDriven #Innovation #WineLovers

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