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Rahul Agarwal

Founder | Agentic AI... • 5d

Most ML models never make it past the notebook. Production is a different game entirely. I chose to focus on MLOps years ago. It changed my career trajectory. If you're starting out, build real systems early. Here are six beginner-friendly projects that build practical deployment skills. Start with workflow orchestration using tools like Prefect. Automate preprocessing, training runs, evaluations, and cloud deployments. Add logging and failure alerts. Make pipelines observable. Next, implement CI/CD for machine learning systems. Use GitHub Actions and CML to automate retraining. Trigger tests and deployments on every commit. Treat models like software. Then, combine DVC with GitHub Actions. Version datasets and models properly. Automate training workflows and push artifacts to AWS. Reproducibility becomes non-negotiable. Explore a complete MLOps walkthrough through structured, hands-on coursework. Learn orchestration, continuous delivery, API deployment, and monitoring. Ship a working REST service. Monitor it in production. Deploy a containerized model to Azure cloud infrastructure. Wrap your model inside a Flask application. Package it with Docker. Push it to Azure Container Registry. Finally, master experiment tracking and pipeline reusability. Log metrics and parameters using MLflow. Standardize repeatable training workflows. Track performance after deployment. The real opportunity lives between experimentation and production. That gap defines modern ML careers. Build these projects. Document everything. Show end-to-end ownership. That’s how beginners stand out in MLOps.

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Gourav Singh

Student of Computer ... • 10m

I recently tested my skills by building and deploying a full-stack monorepo(using bun as package manager) project using Next.js, backend with typescript and WebSockets. The project includes a backend API, database integration with Prisma, and a Next.

Kunal Gavhane

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Nikhil Raj Singh

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Hiring AI/ML Engineer 🚀 Join us to shape the future of AI. Work hands-on with LLMs, transformers, and cutting-edge architectures. Drive breakthroughs in model training, fine-tuning, and deployment that directly influence product and research outcom

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Mohammed Zaid

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DeepCode, an open-source “agentic coding” platform by the University of Hong Kong. 🧑‍💻 Feeds on research papers, technical docs, specs & more, and automatically crafts full-stack, production-ready code (backend, frontend, tests included). 🚀 Multi-

Deepak

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🔍 Ever wondered what sets successful AI projects apart from the unsuccessful ones? In my experience, it comes down to a few key factors: Integrated MLOps: Ensuring seamless deployment and monitoring of models can make or break AI projects. Deep Le

Mr BIZENIUS

Mr. Bizenius • 1y

OpenAI has for months been working with Broadcom to create an AI chip for running models, which could arrive as soon as 2026, reports Reuters. Meanwhile, OpenAI plans to use AMD chips through Microsoft's Azure cloud platform for model training.

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Shuvodip Ray
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YouTube • 1y

AI relies on robust data management across 7 key components to build effective AI models: 1. sources, 2. ingestion, 3. storage, 4. transformation, 5. analytics, 6. governance and security, and 7. orchestration.

Pranjal Pandey
•

CompSci • 1y

Here are some AI tools which are useful for the startups: 1. TensorFlow and PyTorch: Open-source frameworks for building machine learning models. 2. IBM Watson: Offers AI tools and services like natural language understanding and computer vision.

1 Reply

Aditi Wagh

Passionate about lea... • 1m

Coding 💻✨ is the art of turning ideas 💡 into reality 🌍, and deployment 🚀🌉 is a bridge 🌉 which provides you the facility to take your code 👨‍💻📦 out of your computer system 🖥️ and make it usable for the real world 🌐🚀. 1. Local Deployment (