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🌟 𝗨𝗻𝗶𝘁 𝟮, 𝗗𝗮𝘆 𝟰 𝗮𝘁 𝗠𝗮𝘀𝗮𝗶 𝗦𝗰𝗵𝗼𝗼𝗹: 𝗔 𝗥𝗼𝗹𝗹𝗲𝗿 𝗖𝗼𝗮𝘀𝘁𝗲𝗿 𝗼𝗳 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗚𝗿𝗼𝘄𝘁𝗵! 🌟 As usual, the day started with a SCRUM and ended with a DBMS class, but every moment is a valuable experience, making the journey so enjoyable! Today was packed with diverse and engaging learning experiences: 🕚 𝟭𝟭:𝟬𝟬 𝗔𝗠 – 𝟭𝟭:𝟯𝟬 𝗔𝗠: 𝗦𝗰𝗿𝘂𝗺 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 - Kicked off with our daily Scrum to align on goals and updates. 🔢 𝟭𝟭:𝟯𝟬 𝗔𝗠 – 𝟭:𝟬𝟬 𝗣𝗠: 𝗗𝗦𝗔 𝗖𝗹𝗮𝘀𝘀 - Dived into Multidimensional Arrays with advanced-level questions, tackling complex problems and enhancing our skills. 🕐 𝟭:𝟬𝟬 𝗣𝗠 – 𝟮:𝟯𝟬 𝗣𝗠: 𝗗𝗼𝘂𝗯𝘁 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 - Spent 1.5 hours resolving doubts and solidifying our understanding of the day’s topics. ✉️ 𝟮:45 𝗣𝗠 – 𝟰:𝟬𝟬 𝗣𝗠: 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗹𝗮𝘀𝘀 - Learned to craft Emails that Get Noticed, focusing on impactful and effective messaging. 🕕 𝟲:𝟬𝟬 𝗣𝗠 – 𝟴:𝟬𝟬 𝗣𝗠: 𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝗗𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻 - Engaged in a discussion about Assignments and Assessments, sharing insights and strategies. 💾 𝟴:𝟬𝟬 𝗣𝗠 – 𝟭𝟬:𝟬𝟬 𝗣𝗠: 𝗗𝗕𝗠𝗦 𝗖𝗹𝗮𝘀𝘀 - Took a deep dive into SQL CLAUSES: > FROM > WHERE > GROUP BY > HAVING > SELECT > ORDER BY > LIMIT and their execution order. Gained skills to write efficient SQL queries quickly. Days at Masai are a bit hectic but incredibly interesting—like a roller coaster ride! Enjoying every moment of this journey. #MasaiSchool #DSA #ProfessionalCommunication #DBMS #SQL #TechLearning #CodingJourney #GrowthMindset #RollerCoasterLearning

🚀Here's how I streamline my 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 with 𝐌𝐋𝐎𝐩𝐬 🔬 𝐚𝐧𝐝 𝐂𝐈/𝐂𝐃... I recently completed an exciting project using the 𝐇𝐢𝐠𝐠𝐬 𝐁𝐨𝐬𝐨𝐧⚛ 𝐏𝐚𝐫𝐭𝐢𝐜𝐥𝐞 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐨𝐫 dataset. Here's a glimpse of what I've been working on. 🧰 Tools I used -> • Weights & Biases for Experiment Tracking and Artifact Registry • GitHub Actions and CML (Continuous Machine Learning) for automating workflows • DVC.ai (Data Version Control) for Dataset Versioning 🤖 Workflows I integrated -> • 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰: Ensures the project's normal functioning by verifying dependencies and printing the version of Weights & Biases used. • 𝐓𝐫𝐚𝐢𝐧 𝐚𝐧𝐝 𝐋𝐨𝐠 𝐨𝐧 𝐃𝐚𝐭𝐚 𝐌𝐨𝐝𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Automatically trains two baseline models (one with all variables, one with the important ones) and creates a data artifact stored in Weights & Biases whenever the dataset is modified. • 𝐓𝐫𝐚𝐢𝐧 𝐚𝐧𝐝 𝐋𝐨𝐠 𝐨𝐧 𝐌𝐨𝐝𝐞𝐥 𝐌𝐨𝐝𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Tunes model hyperparameters, creates a configuration file, trains the model, and registers the tuned model artifact along with metrics to Weights & Biases. • 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜 𝐂𝐨𝐦𝐦𝐞𝐧𝐭𝐢𝐧𝐠: Uses Ghapi to generate and print the workflow payload, make comments, and manage PRs and issues. • 𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥 𝐦𝐞𝐭𝐫𝐢𝐜𝐬 𝐚𝐜𝐫𝐨𝐬𝐬 𝐕𝐞𝐫𝐬𝐢𝐨𝐧𝐬: Listens for comments with a run ID, compares the specified run with a baseline run, and comments the comparison report link. Despite some challenges with the dependency on the Weights & Biases ecosystem and the use of Ghapi, I found DVC.ai's CML to be a more user-friendly solution for commenting metrics and integrating with DVC. This project was inspired by Hamel H.'s course, CI/CD for Machine Learning (GitOps). Let's connect and discuss the endless possibilities of CI/CD for ML! How have you used MLOps & CI/CD in your projects? #MLOps #MachineLearning #DataScience #CI_CD #WeightsAndBiases #GitHubActions #CML #DVC #Streamlit #Automation #TechInnovation #ProjectManagement

In-depth analysis of MLflow versus SageMaker for machine learning project lifecycle management https://lnkd.in/g5U9zJrQ

𝐇𝐨𝐰 𝐈 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐌𝐋 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 ? In my recent project, an A̳I̳-̳p̳o̳w̳e̳r̳e̳d̳ ̳w̳a̳s̳t̳e̳ ̳s̳o̳r̳t̳i̳n̳g̳ ̳s̳y̳s̳t̳e̳m̳ ̳f̳o̳r̳ ̳s̳m̳a̳r̳t̳ ̳r̳e̳c̳y̳c̳l̳i̳n̳g̳, I applied a structured approach to ensure efficiency and scalability. Here’s how I did it: 🔴 𝐂𝐨𝐫𝐞 𝐌𝐋 𝐅𝐨𝐜𝐮𝐬:   For the waste sorting system, I started by clearly defining the problem—sorting waste into recyclable categories using computer vision. I focused on selecting relevant data, keeping the model simple, and refining it with each iteration. 🔴 𝐃𝐞𝐬𝐢𝐠𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬:   To make the system scalable, I used design patterns like data pipelines and model retraining. This allowed the system to continuously improve and adapt to new waste categories as it processed more data. 🔴 𝐂𝐥𝐞𝐚𝐧 𝐂𝐨𝐝𝐞:   I wrote clean, modular, and testable code, ensuring that each component (data processing, model training, evaluation) could be easily maintained. Best practices like PEP8 and Git version control kept the project organized. 🔴 𝐌𝐋𝐎𝐩𝐬 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧:   Using MLOps practices, I automated the deployment pipeline and monitored model performance in real-time. Tools like MLflow and Docker made it easier to track versions and manage models as the system scaled. 🔴 𝐏𝐫𝐨 𝐓𝐢𝐩:   If you want to make your ML projects scalable, production-ready, and adaptable, this structured approach will help you achieve those goals! #MachineLearning #MLOps #AI #SmartRecycling #CleanCode #MLDesignPatterns #BestPractices #DataScience

🚀 Exciting Milestone Achieved: Mastering Iterative Tools for Data Science & Analytics! 🎓 As I continue my journey through my Master's degree in Artificial Intelligence, I'm thrilled to share that I've successfully completed the course "Iterative Tools for Data Scientists & Analysts" from DVC. This course has been pivotal in equipping me with the skills needed for a collaborative, experiment-driven approach to machine learning projects. Here are some key takeaways from my learning experience: 🔹 Coding Best Practices & Project Structuring: Emphasized the importance of maintaining a clean project repository structure, leveraging Git for version control, and working within virtual environments. These practices ensure our projects are organized, reproducible, and easily shareable. 🔹 Efficient Experimentation & Automation: Learned to build and automate experiment pipelines using DVC, enhancing productivity and reliability in running experiments. 🔹 Data & Model Versioning: Gained insights into the criticality of versioning, managing multiple versions of data and models efficiently using DVC and remote storage solutions. 🔹 Metrics Tracking & Visualization: Mastered setting up metrics tracking and visualizations with DVC and Iterative Studio. These tools are invaluable for comparing experiment results and deriving meaningful insights. 🔹 Collaboration: The course emphasized the collaborative aspect of data science projects, encouraging seamless teamwork and knowledge sharing. #DataScience #MachineLearning #ArtificialIntelligence #DVC #IterativeTools #ContinuousLearning #AI #VersionControl #MetricsTracking #DataVersioning #Experimentation

Maintaining a high velocity of generating data-driven insights to improve ML model performance is critical to my work. However, over the past few weeks, I've noticed a few issues that have been holding me back: 1. Starting vs. Building: It was easy to start new analyses but challenging to build on top of them. 2. Inconsistent Naming Conventions: I was unsure whether to use names like make_figures_working.py or new_make_figures01.py. 3. Replicability Issues: Since others couldn’t easily replicate the analysis, feedback was often superficial. That's when I discovered Cookiecutter Data Science, a standardized yet flexible project structure for conducting and sharing data science work. Its project structure is designed to enforce several key principles: 1. Data Analysis as a Directed Acyclic Graph (DAG): Treating the data analysis pipeline as a DAG ensures reproducibility. 2. Immutable Raw Data: It's acceptable to read and copy raw data for manipulation into new outputs, but it's never okay to change it in place. 3. Exploration vs. Repetition: Use notebooks for exploration and communication, and source files for repetitive tasks. It’s fairly easy to get started. I highly recommend giving it a try. Link to the website: https://lnkd.in/gBupevGu

Completed the 'Planning a Machine Learning Project' course, gaining invaluable insights into the intricacies of ML project management! 📊 #MachineLearning #ProjectManagement #ContinuousLearning"

📢 For data scientists and ML engineers! DagsHub - Your all-in-one platform for managing and collaborating on data science projects! 🚀 🔗 Tired of juggling multiple tools and platforms to organize your data and collaborate with your team? Look no further! DagsHub is here to simplify your data science workflows. 🌟 DagsHub provides a unified solution for version control, data management, and Collaboration, all in one place. Say goodbye to scattered code, data, and documentation and hello to streamlined project management. 📚 With DagsHub, you can easily version control your code, notebooks, and datasets, allowing you to track changes, collaborate seamlessly, and ensure project reproducibility. No more confusion or lost work - DagsHub keeps everything organized and accessible. 📊 Need to manage large datasets? DagsHub has got you covered! Its robust data versioning capabilities enable you to keep track of dataset changes and manage different versions effortlessly. You can easily explore and share datasets with your team, making data collaboration a breeze. 🤝 Collaboration made easy! DagsHub provides built-in features for easy team collaboration. You can invite team members, assign tasks, and share insights within the platform. Collaborate on projects, exchange feedback, and accelerate your data science progress together. 🔐 Worried about data privacy and security? DagsHub offers robust access control and permissions, protecting your data and intellectual property. You have complete control over who can access and modify your projects, giving you peace of mind. 🌐 Join the growing DagsHub community and unlock the power of collaborative data science! With a vibrant ecosystem, DagsHub offers a range of integrations and tools to enhance your productivity and accelerate your data-driven projects. 🚀 Ready to take your data science projects to the next level? Start using DagsHub today! Visit dagshub.com to explore its features and revolutionize how you manage and collaborate on your data science endeavours. 💡 #DagsHub #DataScience #MachineLearning #Collaboration #VersionControl #DataManagement #Productivity

#NavigatingtheDataTechLearningCurve: From Novice to Knowledgeable 📊🚀 Ever stared at a screen full of code or a complex algorithm and felt completely lost? You're not alone in this data-driven world! 🌐 The journey from data novice to proficient practitioner is filled with challenges, but it's also incredibly rewarding. Let's explore this path together. 🧭 The Data Tech Landscape - Machine Learning algorithms that seem to have a mind of their own 🤖 - Data pipelines that look more like labyrinths 🌀 - Visualization tools that initially create more confusion than clarity 📈 🔍 Common Challenges: 1. Information Overload: The field evolves faster than we can learn! 2. Imposter Syndrome: "Everyone else seems to get this. Why don't I?" 3. Tool Fatigue: Just when you've mastered one tool, a new "must-learn" emerges 💡 Strategies for Success: 1. Start Small: Begin with foundational concepts before tackling advanced topics 2. Practice Regularly: Consistency trumps intensity in the long run 3. Join Communities: Learn from peers and mentors in the field 4. Embrace Errors: Each bug is a learning opportunity in disguise 5. Stay Curious: The "why" is often more important than the "how" 🚀 The Breakthrough Moment Remember, every expert was once a beginner. That moment when a complex concept finally clicks? It's worth every struggle leading up to it. 📈 Continuous Growth In data tech, learning never stops. But with each challenge you overcome, your problem-solving toolkit expands, making the next hurdle easier to tackle. 🌟 Your Turn What's been your biggest challenge in learning data technologies? How did you overcome it? Share your experiences and let's learn from each other! #DataScience #TechLearning #ProfessionalDevelopment #ContinuousLearning #DataTech