🌟Remember our last Tech Playground? From AI basics to expert discussions, we created a space where tech curiosity thrived. Next edition is in the works - but until then, applications are open for our Data Science & AI Program and our AI Software Engineering. Ready to dive deeper?
AI just jumped another level; Cognition have announced a major leap forward in using AI for software engineering: Devin. Devin can: 1. Work across very large code bases - this jumps us past the current context limits and let's code changes affect multiple files and complex interactions. 2. It can learn new tech by researching API documentation on the web (what?!). 3. It can undertake technical architecture and implementation as one sequence (whilst maintaining context). 4. It can find it's own bugs. 5. It can execute code, reviews results and then improve upon itself. Super keen to check this out and test it. Can't wait to deploy across all our teams! Check it out: https://lnkd.in/gEUdCSGC
Should we organize codebases differently now given the prevalence of AI usage in software engineering? Hear a controversial opinion on this question from our very own YK Sugi shared during the Virtual Code AI Summit! Check out the full version of his talk and a lot more: https://lnkd.in/etNz8QXa
AI-driven Data Analysis and Predictive Modeling Proficient in Python, Machine Learning, and Neural Networks, I harness the power of AI to drive business success
Every day as a machine learning engineer, I’m reminded that our work goes beyond models and algorithms. It’s about solving real-world problems, understanding user needs, and sometimes, facing the challenge of turning messy, unstructured data into meaningful insights. The journey is filled with constant learning, experiments that don't always go as planned, and the thrill of seeing a model make a tangible impact. Every challenge is an opportunity to grow, and every breakthrough feels like a win—not just for the project but for the team and the end-users who benefit. If you're on this journey too, keep going. Every line of code, every failed model, and every breakthrough is a step forward. Here’s to the journey, the learning, and the impact we’re all working to create! ---
Devin AI: The Future of Software Engineering? Artificial intelligence has been making waves in various fields, and now it’s eyeing the world of software engineering. Meet Devin AI, the brainchild of brilliant minds and lines of code. #aighana #machinelearning #datascience
💾 The AI Software Engineer Part 2 is out! What can you expect? 👩💻 We will use four simple examples to display the current limitations of LLMs, preventing them from generating software from a prompt. 🚧 We’ll discuss three main obstacles in the path toward a model-centric approach 🏔 We'll look at the two ways people are tackling this challenge (full mega model and hybrid) 🏅 We'll have a look at the current contenders 🔗 Link: https://lnkd.in/egRxu3A3 This is a super interesting space on the edge of research, if you too are researching it, let me know, and let's exchange!
Through our workshops you get the most in-demand skills. This is how we build them 👇
I get a lot of DMs from people asking me how to become a ML engineer. From my experience it's essential to build tiny versions of the model architectures and experiment with them rather than using already-existing libraries and pretrained models that abstract away all the details of the architecture. With this in mind we created a computer vision workshop at Break Into Data. Today is the last day and I'm proud to share what we created during the 7 days we had: - designed and trained a YOLO model from scratch using Pytorch - we created a great dataset for detecting socker ball - trained a production ready model using Ultralytics that we will deploy at HuggingFace today Dataset Link: https://lnkd.in/dvbZeTkH If you participated in the workshop - let us know about your experience in the comments below. If you missed the workshop but want to learn ML - we will host another one soon, so stay tuned!
How do you choose to spend your weekdays? Working on one algorithm until it’s as perfect as it could be? Taking algorithms that others have built and deploying them at scale? There is nothing wrong with either, but if you like spending your time on the second option you should definitely drop me a DM. Principal ML Engineer.
I get a lot of DMs from people asking me how to become a ML engineer. From my experience it's essential to build tiny versions of the model architectures and experiment with them rather than using already-existing libraries and pretrained models that abstract away all the details of the architecture. With this in mind we created a computer vision workshop at Break Into Data. Today is the last day and I'm proud to share what we created during the 7 days we had: - designed and trained a YOLO model from scratch using Pytorch - we created a great dataset for detecting socker ball - trained a production ready model using Ultralytics that we will deploy at HuggingFace today Dataset Link: https://lnkd.in/dvbZeTkH If you participated in the workshop - let us know about your experience in the comments below. If you missed the workshop but want to learn ML - we will host another one soon, so stay tuned!
Today is our last day of the ML live workshop. And I am proud to see what we were able to build with our students in 1 week! Follow Kostya Numan to learn more about Machine Learning fundamentals. 💪🏼
I get a lot of DMs from people asking me how to become a ML engineer. From my experience it's essential to build tiny versions of the model architectures and experiment with them rather than using already-existing libraries and pretrained models that abstract away all the details of the architecture. With this in mind we created a computer vision workshop at Break Into Data. Today is the last day and I'm proud to share what we created during the 7 days we had: - designed and trained a YOLO model from scratch using Pytorch - we created a great dataset for detecting socker ball - trained a production ready model using Ultralytics that we will deploy at HuggingFace today Dataset Link: https://lnkd.in/dvbZeTkH If you participated in the workshop - let us know about your experience in the comments below. If you missed the workshop but want to learn ML - we will host another one soon, so stay tuned!
Cofounder & CEO at DAIR.AI | Ph.D. | Prev: Meta AI, Galactica LLM, Elastic | Prompting Guide (6M+ learners) | I teach how to build with AI ⬇️
Agents in Software Engineering Provides a comprehensive overview of frameworks of LLM-based agents in software engineering. https://lnkd.in/gqKePA5Z ↓ Join 85K+ AI researchers and devs so you don’t miss my weekly summary of the top AI and LLM papers: https://lnkd.in/e6ajg945
