Naga Manohar Yelubandi’s Post

#data #engineering vs #feature Engineering What is the difference between these 2? Nowadays, this question occurs a lot during conversations - Data Engineers and data engineering, are now terms well known and pivotal in overall data architectures. With a growing inclination towards data science, AI use cases the term of Feature Engineering is also gaining momentum. #dataengineering is a process of designing and building pipelines that transforms and processes data into a format wherein, by the time it reaches the end users (like data analysts or data scientists), it is in usable state. These pipelines may take data from many disparate sources, collect them into warehouse representing single source of truth. Responsibilities- a data engineer might implement several transformations, business rules and modelling techniques to present the raw data into consumer-specific data format. #featureengineering is a very specific data engineering activity that is targeted towards preparation of data in a specific format that is suitable for data science or training Machine Learning models. Some common feature engineering methods which could be employed are -correlation analysis, imputation, binning ,encoding, log transform etc. Responsibilities - In feature engineering process, a data scientist uses domain knowledge to extract features (characteristics, properties, attributes) from raw data.  A #feature is an individual, measurable property or characteristic of an entity. Choosing informative, discriminating, and independent features is a crucial element of effective algorithms in pattern recognition, classification, and regression. #snowflake #Snowpark's #ml capabilities provide feature engineering capabilities with additional advantages of working on - 1) large volumes(PB -scale) of data with pushdown processing and lazy evaluation 2) scaling 3) reducing network traffic and 4) leveraging security and RBAC and #fidelity uses #snowpark for some feature engineering use cases. https://lnkd.in/ggrR6Zsh If you would like to know more, connect with us. Sumeet Tandure Murad Wagh Navneet Srivastava Arjun Rajagopal Tushar Wadhwani Ritesh Shah Sachin Gangwar Manisha Jaiswal Pawan Mall Nikhil Bhatnagar Ashit Bali

🌟 Empowering Solutions with Data Engineering, Data Science, and MLOps 🌟 The convergence of data engineering, data science, and MLOps is transforming the way businesses tackle complex challenges. This combined expertise enables sustainable, profitable solutions and significant value creation. Here's how their collective proficiency drives impact: -Optimized Data Infrastructure: Data engineers architect and sustain high-performance data pipelines, ensuring the seamless flow of high-quality data for analytical processes. -Sophisticated Insights: Data scientists apply advanced analytical methods and machine learning models to reveal critical trends and patterns, informing strategic decision-making. -Streamlined ML Lifecycle Management: MLOps specialists enhance the deployment and oversight of machine learning models, ensuring they remain efficient, reliable, and continuously refined. -Long-Term Sustainability: Their integrated approach leads to solutions designed for lasting resilience and sustainable business growth. -Responsive Adaptation: This cross-disciplinary collaboration rapidly adapts to changing business demands, keeping solutions current and highly effective. By leveraging the synergy between data engineer, data science, and MLOps, organizations drive transformative change and realize unparalleled success and enduring value creation. #DataEngineering #DataScience #MLOps #ValueCreation #SustainableSolutions #BusinessGrowth #Innovation

Very useful information!

Bittersweet truth! Tens of thousands of ML engineers /data-scientists merely adjusting prompts & temperatures is no data science or research, Its just a glorified rest API client development! Its more exciting & meaningful to experiment with novel ideas & architectures! Objective driven ML >> LLMs

My views on Data Engineers leading the way in the world on Gen AI. As I have started working closely with my Engineering at Mathco leaders @Varun and Himanshu, we are starting to think quite differently to ensure we are ready for the future.

𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐟𝐨𝐮𝐫 𝐰𝐚𝐲𝐬 𝐭𝐨 𝐝𝐞𝐩𝐥𝐨𝐲 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥𝐬 𝐢𝐧 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬? When it comes to deploying Machine Learning models in real-world scenarios, there are four simple ways to deploy them. Let's take a look: 1⃣ 𝗕𝗮𝘁𝗰𝗵 - You apply your trained models as a part of ETL/ELT Process on a given schedule. - You load the required Features from a batch storage, apply inference and save the results to a batch storage. - It is sometimes falsely thought that you can’t use this method for Real Time Predictions. - Inference results can be loaded into a real time storage and used for real time applications. 2⃣ 𝗘𝗺𝗯𝗲𝗱𝗱𝗲𝗱 𝗶𝗻 𝗮 𝗦𝘁𝗿𝗲𝗮𝗺 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 - You apply your trained models as a part of Stream Processing Pipeline. - While Data is continuously piped through your Streaming Data Pipelines, an application with a loaded model continuously applies inference on the data and returns it to the system - most likely another Streaming Storage. - This deployment type is likely to involve a real time Feature Store Serving API to retrieve additional Static Features for inference purposes. - Predictions can be consumed by multiple applications subscribing to the Inference Stream. 3⃣ 𝗥𝗲𝗮𝗹 𝗧𝗶𝗺𝗲 - You expose your model as a Backend Service (REST or gRPC). - This ML Service retrieves Features needed for inference from a Real Time Feature Store Serving API. - Inference can be requested by any application in real time as long as it is able to form a correct request that conforms API Contract. 4⃣ 𝗘𝗱𝗴𝗲 - You embed your trained model directly into the application that runs on a user device. - This method provides the lowest latency and improves privacy. - Data in most cases is generated and lives inside of device significantly improving the security. Afterall, Each method is suited for different use cases, from handling large data sets in batches to delivering immediate predictions or running models directly on devices for offline access. [ Explore more in the post ] <<<>>> What types of deployments are you mostly working on? share your thoughts in the comment..⬇ If you liked this content, ♻ repost this and follow Piku Maity PC - Respective Owner #machinelearning #mlops #dataengineering #datascience #aiml

🚀 Embracing the Power of Automation in Data Science! 🚀 Today, I explored the magic of automation in machine learning, where a few lines of code empowered me to analyze models and pinpoint the best one for my loan application status project. The result? The LGBM Classifier emerged as the top performer, achieving an impressive 93% accuracy! 📈 Automation is not just about speed; it's about enabling data scientists like myself to focus on insights and strategy. As I continue upskilling in the data science field, it's exciting to see how these advancements make complex analyses more accessible and efficient. Let’s leverage automation to transform data-driven decision-making! 💼 #MachineLearning #DataScience #Automation #Upskilling #BusinessIntelligence #Recruiting #HiringDataScientists #DataDriven #BusinessOwners #TechInnovation

🚀 Unlocking the Power of Data Structures! 🚀 In the world of computer science, data structures are the backbone of efficient algorithm design and software development. Here’s why each one matters: 🔗 Linked List: Ideal for dynamic memory allocation, making insertion and deletion a breeze! 📚 Stack: The go-to for implementing undo features, parsing expressions, and managing function calls (LIFO FTW!). 🔄 Queue: Perfect for scheduling tasks, handling resources in order, and ensuring first-come, first-served service. 🏔️ Heap: Essential for priority-based tasks like scheduling and resource management, ensuring you always find the max or min efficiently. 🌳 Tree: Critical for hierarchical data representation, efficient searching, and sorting with various types like binary, AVL, and red-black trees. 🔗 Suffix Tree: The key to fast substring searches and pattern matching, powering text processing and DNA sequencing. 🌐 Graph: The best choice for modeling relationships in social networks, routing algorithms, and dependency analysis. 🌍 R-Tree: Optimized for spatial data indexing, making it a game-changer in geographic information systems (GIS) and location-based services. 🎯 Vertex Buffer: Boosts performance in rendering by storing vertex data in graphics memory, crucial for real-time 3D graphics. 📏 Skiplist: Combines linked list simplicity with binary search speed—perfect for ordered lists that support fast searches. 🔒 Hash Index: Provides O(1) average-time complexity for lookups, making it invaluable for databases and caches. 📦 SSTable: Powers NoSQL databases by enabling efficient sequential writes and reads, ensuring data integrity. 🌲 LSM Tree: Optimizes writes in high-throughput databases, reducing write amplification in storage engines. 📚 B-Tree: The foundation of many databases and file systems, allowing efficient storage and retrieval of large datasets. 🔍 Inverted Index: Drives fast full-text searches by mapping content to locations, powering search engines. 🎯 Priority Queue: Ensures critical tasks are handled first, making it indispensable in CPU scheduling and network routing. Master these, and you’ll unlock the true potential of data structures! 💻🔓 💡 Want more insights on data structures, algorithms, and tech trends? Follow Het Thakkar for expert tips, deep dives, and industry updates! 💡 Let’s connect and grow together in this exciting tech journey! 🚀 #DataStructures #TechInsights #FollowForMore