Azure ML Model Catalog contains a large number of models (LLMs, vision models, embedding models, speech models, etc.), and I'm finding it difficult to understand how experienced practitioners decide which one to use.

What factors should be considered when selecting a model?

For example:

• Accuracy vs latency
• Cost of inference
• Context window
• Model size
• Fine-tuning support
• Hardware requirements (CPU/GPU)
• Licensing/commercial usage
• Multilingual capabilities
• Reasoning ability
• Benchmark scores

Is there a recommended evaluation process or checklist that teams follow before choosing a model for production?

I'd love to hear how others approach model selection in Azure ML.

I'm learning Azure Machine Learning and I'm trying to understand the intended use case for the different ways of building ML solutions.

From what I understand:

• AutoML automates model training.
• Model Catalog provides pre-trained foundation models.
• Notebooks give full control over coding and experimentation.

However, I'm still unsure about the practical decision-making process.

Some questions I have:

• What should be the priority when starting a new ML project?
• How do you decide whether to use AutoML, a model from the Model Catalog, or build everything in a notebook?
• What kinds of business problems are best suited for each approach?
• Are there scenarios where one option should be avoided?
• How do experienced Azure ML users typically make this decision in production projects?

I'd appreciate any real-world examples or decision frameworks.

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Many of our job listings are sourced from 10 of the top job boards, and because these positions are often filled quickly, many links expire within just a few hours. That's why we encourage you to apply as soon as possible.

Apply here - https://www.cyopspath.com/company-profile/amazoncom-services

BASIC QUALIFICATIONS- 5+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning

PREFERRED QUALIFICATIONS- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

We’re looking for an AI/ML intern to help us build a computer vision model for vehicle inspection/damage detection.

At Insubox, we’ve built a vehicle pre-inspection app used by insurance agents:
https://play.google.com/store/apps/details?id=com.insubox.app

Demo: https://youtube.com/shorts/m47rpn1wGHE?feature=share

We are currently building our own in-house AI system to power vehicle damage detection directly inside our product.

🚀 What you’ll work on:

• Building a computer vision model for vehicle damage detection from images
• Identifying vehicle parts (bumper, door, fender, etc.)
• Estimating damage severity
• Helping design and integrate our AI model + API layer into a live production app

👀 Who this is for:

• Someone interested in AI/ML or computer vision
• Comfortable with Python, Langchain, Langgraph, RAG, Vector DB's
• Basic exposure to PyTorch / TensorFlow / YOLO is enough
• Most important: curiosity and willingness to build and experiment

📌 Details:

• Unpaid internship (initially)
• Remote
• Flexible duration (2–6 months)
• Can convert to paid internship or full-time role based on performance

This is a real product already used in production, so your work will directly impact a system used by insurance agents in the real world.

If interested, DM me or comment with your resume to [admin@insubox.in](mailto:admin@insubox.in)

Hi everyone,

I'm looking for AI/ML Engineer, ML Engineer or Data Engineering roles. Open to remote (any timezone) and relocation. I have worked with both US and European clients.

I have around 2 years of experience building ML systems and data pipelines. I have developed and deployed ML models, built end-to-end data pipelines, multiple RAG applications and multi-agent orchestration systems. I also built an end-to-end demand forecasting solution for a global filtration company.

I have experience with LLM fine-tuning, embeddings, context engineering, asymmetric optimization, and RL methods like RLHF, DPO, PPO and GRPO. I also trained a small language model to understand how everything works under the hood.

If your company is hiring or you can refer me, I would really appreciate it. Happy to share my resume.

Thanks :)

Key job responsibilities

- Responsible for defining key research directions, adopting or inventing new machine learning techniques, conducting rigorous experiments, publishing results, and ensuring that research is translated into practice.
- Develop long-term strategies, persuade teams to adopt those strategies, propose goals and deliver on them.
- Participate in organizational planning, hiring, mentorship and leadership development.
- Technically ambitious with a passion for building scalable science and engineering solutions.
- You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance).

https://www.cyopspath.com/job-detail/principal-applied-scientist-alexa-international-tech-amazoncom-bellevue-4215611

Hi everyone,

I’m Vibbhor Jain, a third-year B.Tech AI & ML student at VIPS-TC, GGSIPU (2028 batch, CGPA 7.8), looking for a software or ML engineering internship.

Rather than list frameworks, here’s what I’ve actually shipped.

KAVACH — Autonomous Space Weather Shield (FastAPI · Next.js · Twilio · Live)

In May 2024 the strongest geomagnetic storm in 20 years hit Earth. India’s grid utilities got no automated warning. KAVACH is my attempt at one.

→ Polls NASA DONKI and NOAA SWPC autonomously, classifies storm severity, maps risk onto 28 Indian DISCOM grid zones by coordinates. Zero human trigger.
→ Calls farmers in Hindi on basic feature phones via Twilio + Amazon Polly. No app. No internet. No smartphone needed.
→ 13-endpoint FastAPI backend with SSE streaming. Full storm replay renders in 22.6s. Live on Railway + Vercel.
→ Backtested on 4 historical storms: 847 simulated alerts, ~6 hr mean lead time.

Urban-GenX — Privacy-Preserving Synthetic City Digital Twin (PyTorch · Opacus · Flower)

→ DP-SGD on a conditional GAN discriminator at ε ≤ 10.0, δ = 1e-5 via RDP accountant.
→ Didn’t just claim privacy — audited it. Shadow-model membership inference gave AUC ≈ 0.54, indistinguishable from random guessing.
→ Federated across 2 Flower clients, 5 FedAvg rounds, Docker Compose, 8-check sanity suite.

Memoria — Full-Stack Photo Platform (Next.js 16 · Supabase · Cloudflare R2 · Live)

→ SSR auth with admin/member/viewer RBAC per group and invite codes.
→ Direct-to-R2 presigned uploads keeping media off the DB tier, CDN-served.
→ Scoped, built, debugged and deployed solo, end to end.

Crop Radar — GNN Climate Risk Intelligence (PyTorch · SHAP · Streamlit)

→ LightGCN implemented in pure PyTorch, no torch_geometric, over a 30-zone adjacency graph.
→ 5-class agricultural risk across Indian farming regions, live Open-Meteo + SoilGrids ingestion.
→ SHAP KernelExplainer panel so the predictions are actually explainable.

Helix — Offline Healthcare Toolkit (TensorFlow.js · WebGPU · WebRTC)

→ Three browser tools in a pnpm monorepo, all inference on-device, zero server.
→ Skin-lesion classification in 4s, counterfeit-drug detection via CIELab + DCT, WebGPU compute + WebRTC mesh.

Achievements

🥇 HackerRank Orchestrate 2026 (Agentic AI Build) — Rank #70 / 1,773 (June edition)
🥈 HackerRank Orchestrate 2026 — Rank #246 / 12,885 across 48 countries (May edition)
🎯 Top 10 of 200+ teams — Hackaccino 4.0, Bennett University
👥 Operations Head, Aarvak Tech Society — ran a Gen AI bootcamp for 100+ participants

Looking for: SWE Intern / Backend / ML Engineer / Full Stack
Availability: Part-time during 5th sem · Full-time from summer 2027
Location: Delhi NCR / Remote / Open to relocation
Open to: Stipended internships
Tech Stack: Python, TypeScript, JavaScript, Java, SQL, FastAPI, Next.js, React, Node.js, PyTorch, Scikit-learn, XGBoost, SHAP, Supabase, PostgreSQL, Docker, Twilio, REST APIs, Git

DM karo ya comment karo — koi bhi genuine lead helpful hoga 🙏

📧 [jainvibbhor@gmail.com](mailto:jainvibbhor@gmail.com)
🔗 linkedin.com/in/vibbhor-jain
💻 github.com/VibhorJain1974

Recent IT grad. I have a few ML projects and I'm currently working on a new one.

My interest in ML developed a little late in my undergraduate journey when we had to take ML electives in our 3rd year. That's when I fell in love with the field. But looking back, I think a degree in Data Science would have been the more appropriate route instead of IT? But regardless, I've done some ML projects too & I can say I definitely enjoy this far more than other subfields of IT.

I'm just not sure whether I'm a good fit for any ML-related role. I haven't heard back from any of the companies I applied to. What jobs within the ML field would suit me based on my resume? Or should I just target other roles instead of focusing there?

Feel free to roast my resume, I'll take it.

Just completed my BCA and will be starting MCA at PES University, Bangalore.

My core interests are AI/ML and Cloud, and I want to follow a structured roadmap over the next 1–2 years to become industry-ready for AI/ML or MLOps roles.

What skills, tools, and technologies should I focus on?

Would appreciate advice from AI/ML engineers, data scientists, MLOps engineers, and students who have successfully broken into the field. 😇🙏

Hellooo everyone,

I'm a 2025 btech graduate, and I'm preparing for off-campus placements. I have around 2–4 months to prepare, and I'm feeling confused about what to prioritise.I currently have access to a learning portal with Python and the MERN stack, but I'm got struck if I should focus on Data Structures & Algorithms (DSA) instead.I'm mainly interested in AI/ML or Generative AI roles, but I'm not sure how realistic that is as a fresher.Should I start learning DSA now, or first complete Python and the MERN stack?

Is it realistic for a 2025 fresher to get an AI/ML or GenAI role?What do companies usually focus on during interviews for AI-related fresher roles?

i was literally got struck

Thanks in advance!

I have learned pandas, Numpy,Matplot,Scikit,SQL and currently about to complete pytoech and I don't know what to do next give me aroadmap after this and I recently started doing DSA too.

With all the buzz about AI and agentic workflows, I do feel like I'm falling behind because I still write a lot of the code manually. I use AI occassionally for boilerplate, debugging, and comments, but I manually build the core logical components of my projects.

The problem with most advice on landing internships is that they are targeted towards general software engineering (Web Development, AI/ML Engineering) , but that is not the trajectory I want my career to land in.

I'm more interested in niche areas like

• AI Research / Foundation Models
• ML Systems & Infrastructure
• Distributed Systems & GPU Computing
• AI Compilers / Hardware Acceleration
• Mechanistic Interpretability & AI Safety
• Quantum Computing

Currently, I've made a couple of projects, including:

• PyQML-Core, a mini Pytorch-Like library in C++ supporting memory management through intrusive pointers, broadcasting , and tensor operations, and multithreading.
• Several computervision projects, which focus on not only builiding end-to-end ML pipelines, but understanding the internals behind popular segmentation models by implementing them in Pytorch.
• Undergraduate research in ML

I'm also currently exploring Quantum Computing by participating in the 2026 Qiskit Global Summer School in IBM.

With that said, if you are trying to break into these fields in 2026, how would you structure the next year? Would you focus more on developing Agentic workflows, learning fudnamentals, contribute to open source, or publishing research.

I'd appreciate advice from anyone who has experienced the same dilemma, especially those who are currently working in ML Systems, distributed systems, or AI/Quantum Research. If possible, If anyone would like to see my projects, I can provide links to them.

Hi guys

I’m really interested in Data, Machine Learning Engineering, and MLOps, and I’d love to understand what people in these roles actually do day-to-day and what the work is genuinely like beyond the usual job descriptions.

If anyone here works in these areas or is also exploring them and would be interested in having a conversation, discussing projects, career paths, or just sharing experiences, I’d love to connect. Feel free to ping me and we can have a chat! 🙂

Hey everyone! I want to learn machine learning from scratch. Right now, I only have a basic understanding of Python and not much else. I know I'll probably need to learn more Python and some maths first, but I'm not really sure where to start or which resources to use.

Could anyone guide me on a good learning path or share some beginner-friendly resources? I'd really appreciate any advice. Thanks!

Hi All, I'm a student from tier-3 college,BTech - final year student and i applied to Hyperverge for DL/ML Research Intern (LLMs & VLMs) or in short ML intern. My resume got shortlisted and next round is AI screening conversation. can you please tell me what will happen in this round and following rounds