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Founder & CEO - ThinkAI - A Machine Learning Community I Owner @CSE Pathshala by Nirmal Gaud I Kaggle Expert I Assistant Professor , Deptt. of C.S.E , SATI (D) , Vidisha , M.P , India I Former Tutor @ Unacademy
You are a great machine learning engineer if you: 1. Focus on the problems first, then tools. 2. Have both creative and critical thinking skills. 3. Know how to drive progress between cross-functional teams. 4. Understand how to deploy models at scale. 5. Can discern between noise and value in the industry. 6. Know what reproducibility and observability mean. 7. Be a team player with excellent communication skills. 8. Stay updated with trends and research. 9. Have strong programming skills. 10. Possess a solid mathematical foundation. 11. Have data wrangling skills. 12. Be good at model evaluation and validation. 13. Be experienced with big data technologies. 14. Understand software engineering principles. 15. Have domain knowledge. 16. Maintain a problem-solving mindset. 17. Be aware of ethics and fairness. 18. Be adaptable. 19. Have effective debugging skills. 20. Show curiosity and passion. #machinelearning follow Nirmal Gaud for ML updates !!!
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Having worked with many teams over time, I have realized that building a successful AI product requires a combination of ML Engineers and Software Engineers. They both bring a wealth of complementary skills to the table. While software developers excel in coding, debugging, and building applications, ML engineers focus on creating models, understanding algorithms, and managing large datasets. The overlap? Both need strong programming skills, but their approaches differ. What do you think is the most crucial skill for each role? #TechSkills #MLEngineering #SoftwareDevelopment
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Leading Data Teams | Public Speaker | Author of "Thriving in a Data World" Book | Healthcare and CPG Data Strategies | Transforming Numbers into Compelling Stories
People pursuing software engineering roles and people studying computer science With newly introduced Devin - first AI Software Engineer ❓ How will this reshape the future of software engineering? Some things to explore: ✅ Are we transitioning from low-code/no-code to high-level automation? ✅ Software maintenance is more expensive than creation. Is AI assisting with maintenance a good use case? ✅ How will the DevOps pipeline incorporate AI tools? ✅ Will there be an AI human collaborator job role? What's your take on the future of software engineering in an AI-driven world? #data #powerbi #jobs
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Title: "Decoding the Differences: Software Engineer vs AI Software Engineer" 👋 Ever wondered about the differences between being a Software Engineer and an AI Software Engineer? 🤔 Let's break it down: 🖥️ Software Engineer: Masters the art of designing, building, and maintaining software applications and systems. They're the architects behind your favorite apps and programs, ensuring they run smoothly and efficiently. 🤖 AI Software Engineer: Dives deep into the realm of artificial intelligence, developing cutting-edge algorithms, models, and systems that power intelligent applications. They leverage machine learning and deep learning techniques to create solutions that learn and adapt over time. Both roles require strong technical skills and a passion for innovation, but they each have their own unique focus areas. Whether you're passionate about crafting user-friendly interfaces or pushing the boundaries of AI technology, there's a path for you in the world of software engineering. Which role resonates with you more? Share your thoughts below! 👇 #SoftwareEngineer #AISoftwareEngineer #TechTalk #LinkedInDiscussion
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Software Engineer vs ML Engineer: A Career Guide for 2024 https://lnkd.in/gvysN6T9 Both Software Engineering and Machine Learning Engineering offer exciting career prospects, each with its unique challenges and rewards. Aspiring professionals should assess their interests, skills, and long-term goals to make an informed decision. #SoftwareEngineer #MachineLearningEngineer #TechIndustry #ProgrammingLanguages #MachineLearning #AI #AINews #AnalyticsInsight #AnalyticsInsightMagazine
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What to Look for When Hiring a Machine Learning Engineer - When hiring an ML Engineer, recognizing specific attributes will ensure your new hire is capable and adds value to your team. Here are some skills and qualities to look for when hiring Machine Learning Engineers: https://hubs.li/Q02DLts30
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Attn: Machine Learning Engineer!
I am currently seeking a Machine Learning Engineer for a contract position in Washington D.C. This role offers a hybrid work schedule. Please click on the job title below to review the Job Description and apply. *No 3rd Party Recruiters.
Machine Learning Engineer
aplitrak.com
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Senior Cloud Principal Infrastructure Architect, Multi-Cloud strategies and Solutions, Budding AI and ML enthusiast
Nice article on the role of an ML Engineer. Is the term Data Scientist synonymous to ML Engineer? Maybe not. ML Engineer knows how to put various algorithms to use to achieve a specific objective. Data Scientist on the other hand knows how to create specific algorithms to specific domain problems that ML Engineers can use. Am I wrong?
Learn AI Together - I share my learning journey into AI and Data Science here, 90% buzzword-free. Follow me and let's grow together!
Machine Learning Engineer vs. Software Engineer ◾A software engineering role is meant to develop applications and keep them operational, with a focus on tasks such as building new features and ensuring a robust back-end infrastructure that sustains the application. ML engineering, on the other hand, centers around ML infrastructure and maintaining ML models. Most ML engineers work on three main components: infrastructure for inference, infrastructure for training, and infrastructure for annotation and labeling. It's a rewarding position for building interesting models and shaping the user experience, but it may not provide many opportunities to work on fancy UI elements that customers or clients directly interact with. ◾The image shows how Machine Learning algorithms can be used in the biomedical field. While it's not directly related to the topic, I found it interesting to share. :) It's from the paper 'Incorporating Machine Learning into Established Bioinformatics Frameworks'. ◾For learning materials/ resources in ML, AI, and Data Science, please check my previous posts. I share my learning journey into Machine Learning and Data Science with my amazing LinkedIn friends, join me and let's grow together!
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✨ I'm a huge fan of visuals ✨ Quite often, especially when we are starting in the world of data analysis/science, we struggle to understand the meaning of the different models. That's why having a nice visual summary, like the one published in https://lnkd.in/eFJrba7M, can be extremely useful. Thanks to Alex Wang for the very insightful post. #datascience #machinelearning #deeplearning #clustering #nicepost #haveanicemonday
Learn AI Together - I share my learning journey into AI and Data Science here, 90% buzzword-free. Follow me and let's grow together!
Machine Learning Engineer vs. Software Engineer ◾A software engineering role is meant to develop applications and keep them operational, with a focus on tasks such as building new features and ensuring a robust back-end infrastructure that sustains the application. ML engineering, on the other hand, centers around ML infrastructure and maintaining ML models. Most ML engineers work on three main components: infrastructure for inference, infrastructure for training, and infrastructure for annotation and labeling. It's a rewarding position for building interesting models and shaping the user experience, but it may not provide many opportunities to work on fancy UI elements that customers or clients directly interact with. ◾The image shows how Machine Learning algorithms can be used in the biomedical field. While it's not directly related to the topic, I found it interesting to share. :) It's from the paper 'Incorporating Machine Learning into Established Bioinformatics Frameworks'. ◾For learning materials/ resources in ML, AI, and Data Science, please check my previous posts. I share my learning journey into Machine Learning and Data Science with my amazing LinkedIn friends, join me and let's grow together!
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