Nabla’s Post

𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝘁𝗼 𝗲𝗻𝗵𝗮𝗻𝗰𝗲 𝘆𝗼𝘂𝗿 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗼𝗳 𝘁𝗵𝗲 𝗠𝗟 𝗺𝗼𝗱𝗲𝗹 𝗹𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁? Join us for an insightful seminar on June 13, 2024, titled 𝗠𝗮𝗻𝗮𝗴𝗶𝗻𝗴 𝘁𝗵𝗲 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 𝗼𝗳 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀. 🖥 With the emergence of low-code ML frameworks and AutoML platforms, building ML models has become more accessible than ever. However, integrating these models into production systems is a complex endeavor that requires meeting several non-functional requirements, especially in safety-critical domains like medicine and security. This seminar will provide an overview of the best practices and tools for managing the lifecycle of ML-based components, ensuring their quality and trustworthiness. Gain insights into the engineering challenges involved and learn how to bridge the cultural gap between data scientists and software engineers. Presented by Luigi Quaranta from Università degli Studi di Bari, this session promises to be a valuable resource for anyone involved in the field of machine learning. For more information and registration, write to: projectdare.education@unibo.it. #DARESeminars #DARE #DAREDigitalLifelongPrevention #DAREproject #DAREFoundation #DigitalPrevention #DigitalHealth #DigitalTransformation #DiseasePrevention

I just finished this course on the Machine Learning Data Lifecycle. Looks like the AI tools have come along way since I started my Machine Learning journey 5 years ago. My impression is that the AI community is starting to incorporate software engineering best practices. Also, the Tensorflow ecosystem has decomposed the various disciplines (data collection, cleaning, analysis, feature engineering, training, serving, etc.) so to simplify and streamline the process; this will enable a diverse set of engineers and non-engineers alike to contribute to this next phase of human development. I am excited for the future and how #collinsaerospace will incorporate this in our next generation products. #machinelearning #technology #diversity

🚀 Excited to share that I've successfully completed and received certification for an end-to-end machine learning project! 🎓 🔍 In this project, I delved into the entire ML pipeline from data collection and preprocessing to model training, evaluation, and deployment. I learned valuable skills in data wrangling, feature engineering, model selection, hyperparameter tuning, and more. 🌟 Grateful for the support of Krish Naik sir and Boktiar Ahmed Bappy sir, and looking forward to applying these skills to real-world challenges! #MachineLearning #DataScience #Certification #EndToEndML #DataAnalytics

Happy to have completed the Machine Learning Engineer Career Track on DataCamp - I can now not only build, train, and test ML models, but also employ new skills and strategies to their deployment, operation, monitoring, and maintenance. Having gained experience with key concepts in MLOps and useful tools like MLFlow, GHA, DVC, and Docker, I am ready to apply these new skills as an ML Engineer!

🎉 Excited to share that I have just completed a course on Machine Learning promoted by Standanford University ! 🚀 This marks a new chapter in my journey as a Data Scientist, and I'm thrilled about the possibilities ahead. Eager to apply what I've learned and continue growing in this dynamic field. #MachineLearning #NeuralNetworks #DeepLearning #DataScience

Let's work together! --- My expertise is in building and leading teams of Data Scientists, Machine Learning Engineers, Data Engineers, and Data Product experts of diverse experience levels to drive innovation, product development, and cutting edge, actionable research. I have a proven track record of mentoring, growing, executing, and building strategy around ambitious goals across the Data Science technical platform to provide business value. I have spent much of my career in biomedical research with a focus on therapeutic intervention strategies. My work spanned the space of foundational mechanisms of cell division for targeting cancer cell checkpoints to implementing novel machine learning algorithms for live cell imaging data  to understand environmental signals during disease progression with a focus on patient-to-patient therapeutic response variability. My mission is ambitious; identify and execute on the intersection of medical science and technology to provide curative solutions to the world.

"Excited to embark on the journey to mastery in machine learning! 🚀 Thrilled to be participating in the Roadmap to Becoming an ML Engineer Masterclass by Scalar Academy . Ready to level up my skills and dive deep into the world of AI. Let's learn, grow, and innovate together! #ML #MachineLearning #ScalarAcademy #Masterclass"

Happy to share that I've successfully completed the NIVIDIA Certified Associate - "𝗔𝗜 𝗶𝗻 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮 𝗖𝗲𝗻𝘁𝗲𝗿" certification course by 𝗡𝗜𝗩𝗜𝗗𝗜𝗔 𝘁𝗵𝗼𝗿𝗼𝘂𝗴𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝗿𝗮. This course explores an introduction to 𝗔𝗜, 𝗚𝗣𝗨 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴, 𝗡𝗩𝗜𝗗𝗜𝗔 𝗔𝗜 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲, 𝗱𝗲𝗲𝗽 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀, and how to implement and scale AI workloads in the data canter. Wrapping up the year with new learnings , especially in the field of AI. #aiforall #AIinDataCenter #learning

Hello LinkedIn fam! 👋 I'm thrilled to share that I've successfully completed the Supervised Machine Learning: Regression and Classification certification! 🎉🤖 This journey has been a rewarding exploration into the world of algorithms, data modeling, and predictive analysis. 📈📊 🔍 What I've learned: Throughout this certification, I dived deep into the foundations of supervised learning, mastering essential concepts like classification, regression, feature engineering, and model evaluation. 📚 The ability to harness the power of labeled data to make accurate predictions and decisions is truly remarkable

Behind every successful man there's a woman and in the world of AI, 'Behind every successful machine learning model, there's a rock-solid data platform working its magic!' 💼🤖 In today's rapidly evolving landscape, the success of AI and ML initiatives hinges on a robust data platform. 📊🔗 From data collection and storage to preprocessing and management, the data platform lays the foundation for accurate and impactful models. 📌 Key Responsibilities of a Data Platform for Machine Learning: 1. Data Collection: Gathering relevant and diverse datasets to fuel model development. 2. Data Storage: Providing a scalable and efficient storage solution to house vast amounts of data. 3. Data Preprocessing: Ensuring data quality and consistency through cleaning, transformation, and normalization. 4. Data Management: Enabling version control, sharing, and collaboration on datasets. 5. Security & Privacy: Implementing stringent measures to safeguard sensitive data. 6. Model Training Support: Offering access to high-quality, labeled data for effective model training. 7. Model Monitoring: Facilitating real-time monitoring and performance assessment of deployed models. 8. Debugging & Optimization: Enabling quick identification and resolution of model issues. 9. Regular Maintenance: Sustaining platform functionality and adapting to evolving project needs. 🔑 A successful data platform empowers teams to innovate and iterate, ultimately driving impactful AI solutions. As the technology landscape evolves, so do the demands on data platforms. Staying current and adaptable is key to thriving in this space. What other responsibilities would you add to this list? Let's discuss in the comments! #datascience #mlops #ai #artificialintelligence #dataplatform #dataengineering #machinelearning #innovation #technology #management #datacitizen #upskilling #futureofwork #continuousimprovement #career #jobs #datasciencejobs #aijobs