Odinaka Chinweze’s Post

The journey from "this prompt works great" to "we have a reliable ML classifier in production" has always been fascinating to me. With DecisionBox v0.1.2, we're making that journey smoother than ever. What excites me most about this release is how it bridges the gap between prompt engineering and traditional ML. Your zero-shot prompts now become the foundation for classifiers that actually learn from your data - giving you the best of both worlds. The new help system is particularly interesting, as it guides users through optimizing their classifiers based on real-world performance data. It's like having a ML expert looking over your shoulder! Kudos to the entire team at Blueteam AI for pushing the boundaries of what's possible with modern ML tooling. Can't wait to see what people build with this! What are your thoughts on the convergence of prompt engineering and traditional ML? Have you faced challenges moving prompts to production? #MachineLearning #PromptEngineering #AI #TechInnovation

🌳 Day 32/100: Flattening the Tree! 🌳 Today’s challenge was an exciting dive into tree structures with the Flatten Binary Tree to Linked List problem. Converting a binary tree into a linked list is no small feat, but this problem beautifully highlights the power of recursion and iterative problem-solving. 🧠💻 🔍 Today's Challenge: 114. Flatten Binary Tree to Linked List The task was to take a binary tree and flatten it into a linked list, where the "right" pointer of each node points to the next node in a preorder traversal. It’s like reshaping a dynamic tree structure into a single linear path—a satisfying transformation! Key Insights: 1️⃣ Preorder Traversal: The problem hinges on understanding the preorder traversal of a binary tree and how to maintain that structure in a flattened list. 2️⃣ Recursive Elegance: Recursion once again proves its value in tree problems. Breaking down the tree into smaller parts made it easier to visualize and implement the solution. 3️⃣ Tree to List Transformation: The challenge was to efficiently rearrange nodes, ensuring no data is lost in the process. It’s all about understanding structure and flow. 💡 Pro Tip: When transforming data structures like trees into lists, always think about traversal order. Preorder, inorder, or postorder? Each traversal reveals a different aspect of the structure and helps shape your approach. Excited by how much I’m learning every day through these challenges! Have you ever had to convert complex structures into something simpler? Let’s chat about those experiences in the comments! 🚀 #100DaysOfCode #LeetCode #CodingChallenge #ProblemSolving #BinaryTrees #LinkedList #Recursion #GrowthMindset #SoftwareDevelopment #TechCommunity

🚀 Announcing DecisionBox v0.1.2! Transform your zero-shot prompts into production-ready ML classifiers that keep getting smarter. Our latest release makes the journey from prompt to learned classifier even smoother: ✨ What's New: - Interactive guidance system for optimizing classifier performance - Streamlined interface for managing your prompt-to-classifier pipeline - Enhanced monitoring of how your classifiers learn from data - Improved error handling for production reliability - Expanded test coverage across decision workflows These updates make it even easier to turn your domain expertise into scalable ML classifiers that improve with real-world data. 🤝 Special thanks to our user community for the valuable feedback, and to Niole Nelson from Domino Data Lab and Muhammad Burhan from VirtualForce for their contributions in this release. We're committed to making DecisionBox the bridge between prompt engineering and production ML. Ready to transform your prompts into learning classifiers? Visit https://lnkd.in/eEF7zEFA to get started! #MLOps #ArtificialIntelligence #PromptEngineering #MachineLearning #BlueTeamAI

🔢 Day 51/100: Finding the K-th Largest Element in a Stream! 🔠 Today’s challenge, K-th Largest Element in a Stream, brought an interesting twist to sorting and maintaining large datasets in real time. The problem focused on dynamically finding the k-th largest element in a stream of numbers, which required efficient data structures to handle new updates as they come. 🧠💻 🔍 Today's Challenge: 703. Kth Largest Element in a Stream The goal was to design a class that processes a stream of integers and returns the k-th largest element at any given point in time. This involved efficiently keeping track of the top k elements as the stream updates. Key Insights: 1️⃣ Min-Heap Data Structure: I leveraged a min-heap to maintain the k largest elements. A min-heap ensures that the smallest element among the top k elements is always at the root, which makes it easy to replace and manage as new elements are added. 2️⃣ Efficient Updates: With each new number added to the stream, the heap dynamically adjusts, allowing for quick access to the k-th largest element. This approach ensures the solution scales well, even as the number of elements grows. 3️⃣ Real-Time Processing: This problem emphasizes the importance of real-time data processing, which is a common requirement in areas like live data analytics, financial systems, and real-time monitoring applications. 💡 Pro Tip: When dealing with problems where you need to maintain a dynamically changing set of elements, consider data structures like heaps to keep things efficient and manageable. Each challenge brings new perspectives, and this one highlighted the power of smart data structures in making complex tasks more efficient. Have you worked with heaps or other advanced data structures in your coding projects? Let’s chat about the best use cases for them! 🚀 #100DaysOfCode #LeetCode #CodingChallenge #ProblemSolving #Heaps #DataStructures #KthLargestElement #RealTimeProcessing #SoftwareDevelopment #GrowthMindset #TechCommunity

🔗 Day 35/100: Splitting a Linked List—Divide and Conquer! 🧩 🔗 Today’s challenge had me thinking about how to split things up efficiently with the Split Linked List in Parts problem. It’s not just about dividing a linked list—it’s about ensuring that each part is balanced and well-distributed. This one tested my understanding of linked lists and how to approach division with precision. 🧠💻 🔍 Today's Challenge: 725. Split Linked List in Parts The task was to split a linked list into k parts, with the goal of keeping the parts as evenly sized as possible. If the linked list couldn’t be split evenly, the first parts would take an extra node. This problem made me appreciate the beauty of balancing algorithms. Key Insights: 1️⃣ Node Counting: The first step was to determine the length of the linked list—a crucial step before splitting, ensuring the parts were divided properly. 2️⃣ Balancing Act: Splitting uneven data required careful thought about how to distribute nodes, making sure that the larger parts came first. 3️⃣ Pointer Management: Managing pointers to break the list at the correct places without losing nodes or corrupting the structure is key in linked list operations. 💡 Pro Tip: When splitting linked lists or other data structures, always calculate the total size first to plan out the division. Balancing uneven divisions can be tricky, but starting with an accurate count makes everything smoother! Another day, another new skill! Have you ever worked on breaking down complex data into simpler, more manageable parts? Let’s chat about how you approached it! 🚀 #100DaysOfCode #LeetCode #CodingChallenge #ProblemSolving #LinkedLists #DataDivision #BalancingAlgorithms #GrowthMindset #SoftwareDevelopment #TechCommunity

🎯 Day 59/100 of #100DaysOfCode Challenge Today, I explored a fascinating problem: Reversing the elements of a queue in groups of size k using recursion and stacks. This exercise not only deepened my understanding of queues but also highlighted the beauty of combining data structures to solve complex problems elegantly. 🔑 Key Highlights of the Solution: Stacks were used to reverse elements within each group. Recursion handled group-by-group processing efficiently. Edge cases like incomplete groups and single elements were tackled seamlessly. For instance, given a queue: 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110 and k=3k = 3k=3, the output is: 30, 20, 10, 60, 50, 40, 90, 80, 70, 100, 110. 🎯 This problem reinforced my belief that problem-solving is an art, and every challenge sharpens our skills for building scalable and efficient systems. Let’s continue this journey together! Have you come across any interesting problems recently? Share them in the comments—I’d love to learn more! 😊 #ProblemSolving #DataStructuresAndAlgorithms #CodingJourney #SoftwareEngineering #LearningNeverStops

Hello LinkedIn family! Day (116/150) - Today's Highlights: Today, I attempted to tackle a easy level problem using stack data structure.I encourage all to take part in the challenge and give it a shot! Share your solutions, and let's grow together. Here's a detailed breakdown of the problem statement. Problem discription is as follows: Make the array beautiful: Given an array of negative and non-negative integers. You have to make the array beautiful. An array is beautiful if two adjacent integers, arr[i] and arr[i+1] are either negative or non-negative. And you can do the following operation any number of times until the array becomes beautiful. If two adjacent integers are different i.e. one of them is negative and other is non-negative, remove them. Return the beautiful array after performing the above operation. Example 1: Input: 4 2 -2 1 Output: 4 1 Example 2: Input: 2 -2 1 -1 Output: [] Expected Time Complexity: O(N) Expected Space Complexity: 0(N) Today's key takeaways! Stay tuned for tomorrow's updates, and let the learning journey continue! #LeetCodeChallenge #CodingJourney #DailyCoding #AlgorithmLearning #ProblemSolving #ProgrammingLogic #coderarmy

The latest update for #Last9 includes "PromQL for Beginners: Getting Started with #Prometheus Query Language" and "#Python #Logging Best Practices: The Ultimate Guide". #Monitoring #Observability #TimeSeries #SRE #DevOps https://lnkd.in/eV2ztwiV

Diving into the world of data structures! 📊 From arrays to trees, stacks to queues, and everything in between, each structure offers unique ways to organize and manipulate data. Let's explore the diverse landscape of data structures together! 💡 #devopsifi #CrossPlatform #TechFuture #developers #development #devops #languages #react #betterfuture #whatyouthink #fullstack #DataStructures #TechExploration

🔥 I’m excited to share that I’ve successfully developed and deployed my first data science web application to the cloud. As a professional who has primarily worked with analytics in environments like Jupyter notebooks and local IDEs, this opens new opportunities for me to develop, package, and distribute data science solutions to the public. The application is hosted on the Azure cloud within a Docker container, a common method for deploying data science solutions nowadays. The application’s purpose was to showcase an end-to-end data science process, from exploratory data analysis (EDA) and feature engineering to building a predictive model (predicting property value based on parameters provided by user for select county in Washington state) and deploying the application to the cloud within a Docker container. 😤 The deployment process was challenging, and I learned a lot along the way. You can access the application at this URL: https://lnkd.in/gvYjT8_H ⚠️ I will be taking it offline in about a week, as I have exhausted all my free Azure credits and am now self-funding the application. ✅In terms of results, I have detailed the feature selection and engineering process in the “Exploratory Analysis” tab where constructed several Linear Regression models to gain a better understanding of the data and engineer features. After numerous iterations of feature engineering, I managed to significantly enhance the linear models’ performance by excluding outliers and adding interaction terms between the predictor variables. As a result, even the linear regression model demonstrated impressive performance. Notably, linear regression models provide inherent explainability, and their coefficients offer valuable business insights. ✅After engineering the key features (predictors), I built three predictive models in PyCaret that expectedly outperformed the Linear regression model in terms of predictive power, even without any tuning. A robust feature engineering process may be time-consuming but can significantly enhance model performance. ✅Deploying data science applications is no easy task, but I am improving as I gain more experience. ⏩Next steps: I plan to focus on a more practical data science solution next, possibly within the finance domain. I am currently searching for suitable datasets related to financial fraud, money laundering, etc. Understandably, finding good real-world datasets can be challenging, so if you have any suggestions, please let me know in the comments below. 👀 Stay tuned, and don’t hesitate to reach out if you have any questions!