Inside Machine Learning PoCs: Planning & Execution Explained
Explore how Machine Learning Proof-of-Concepts are built from idea to validation. Understand the methods used to test feasibility, reduce risks, and ensure ML models meet real-world needs.
https://www.amplework.com/blog/machine-learning-poc-explained/
#MachineLearning #MLPoC #AI #ProofOfConcept #MLEngineering #DataScience #AIProjects #TechStrategy
Structured data drives AI. But messy inputs? They stall everything.
We’ve listed six parsing issues you should be watching for.
👉 Read the blog to know more: https://shorturl.at/vuJjw
#AIanalytics #MLengineering #DataWrangling #ParsingProblems #TechStrategy #BigData
Join us for hands-on Machine Learning deployment training! You'll analyse errors, tweak models, and push to production using real-world engineering patterns—way beyond the "drop your model on S3 and call it a day" approach. Gain practical experience with sophisticated ML engineering techniques that you can immediately apply on the job. #MLEngineering #AITraining
👉 https://ddd.academy/put-an-ml-model-in-production/
Training a model is easy. Reproducing it? 🤔 That’s where the real game begins.
No CI/CD ⚙️ No versioning 🕵️ No logs
Just vibes ✨ and an old dataset no one remembers.
That’s why ML needs DevOps 💥
Core Principles of MLOps
MLOps: Machine Learning Operations
#MLEngineering #MLOps #MachineLearning
http://sodakai.com/2025/03/02/mlops-machine-learning-operations/
Early in your ML career, every decision feels irreversible. But the best engineers don’t aim for perfection—they build with reversibility in mind.
Understanding the difference between one-way and two-way doors will help you iterate faster and build better.
A few tips for optimizing Pytorch model training time from a Yandex ML engineer.
https://alexdremov.me/simple-ways-to-speedup-your-pytorch-model-training/
#ml #mlengineering #modeltraining #pytorch #modeloptimization
Here's a more clearly visible demonstration of the problem I described previously: https://sigmoid.social/@chrisoffner3d/111591367887994819
On the left we see the progression of cross-attention maps extracted via the CPU, on the right we see the same cross-attention maps extracted via the GPU.
This is using the #Keras implementation of #StableDiffusion on an M3 Max.
#TensorFlow #StableDiffusion #Diffusion #Python #MLEngineering #MachineLearning #DeepLearning #GPU #M3Max
For example, check the second row, fifth column and how it changes between t = 600 and t = 700.
Is this some bug specific to Apple GPUs or does this also happen with CUDA?
For t = 0, the CPU and GPU images look identical. For higher t, the GPU run produces *very* different results even when re-running with the exact same model inputs, i.e. also for the same time step t.
Any idea why that is?
#MLEngineering #GPU #DeepLearning #Diffusion #CUDA #AppleSilicon #TensorFlow #Keras
I'm running into some unexpected and significant non-determinism when running a #Keras diffusion model on my Apple GPU.
On the left we see the progression of cross-attention maps for time steps from t = 0 to t = 900 when running the model via the CPU.
We see that each cross-attention map undergoes some "refinement" progression as we go from t = 0 to t= 900.
On the right we see the same but on the GPU.
It's a much more erratic and discontinuous progression.
🔖 The Top 5 Papers About #mlops You Should Know (Part 1)
I've seen a ton of lists about the most important papers in #ml, #datascience, #deeplearning, #mlengineering.
But I've either seen not that many #mlops reading lists or when I do run across them they tend to be focused a bit too deeply on specific ML systems or domains or algorithms.
👉🏻 If you only read 5 papers to understand why ML is hard (and how big the problem space of MLOps is) it should be these papers.
[To Be Continued]
Does anyone here have experience with #Prefect? What's the best way to automate blocks? can you do it via #terraform? #ml #mlengineering
The tools we have today are better than the ones we had before and this is especially true in the #mlops world. We have more options than ever before (cc: MAD Turck Landscape) but confusion is just as high as it ever was.
Having #DataScientists Build Infrastructure & Developing Models At The Same Time Is A Terrible Anti-Pattern We’re Addicted To.
Esp at comps that aren’t early stage -- correlated w/ a lack of technical DS leadership, poor infra design, and lack of organizational alignment.
Really shows how the difference between success & failure isn’t technology choices but good project management & strategic leadership around platforms.
👉🏻 Online Inference =/= Streaming
We're all aware of this right? That they're not the same thing?
#mlops #mlengineering #datascience #dataengineering #productionml #mlsystems #systemdesign
🤔 Rather than trying to get rid of the #datascientist title, maybe we just treat it as an abstract class and continue on our merry ways?
#datascience #dataengineering #mlops #mlengineering #ai #career #data
🤔To bootcamp or not to bootcamp?
Like all annoying senior devs, my answer is going to be: "It depends".
I breakdown what consider when choosing the #bootcamp route for #datascience (but advice good for other bootcamps like #dataengineering #mlengineering, etc)
If the answer is similar to:
1️⃣ ASAP
2️⃣ Minimal
3️⃣ Divorced
4️⃣ We can't
5️⃣ Less than 5
Then your first step shouldn't be building an ML platform, it should be developing models or ML-drive product features using the simplest, tried & true patterns possible.
#mlops #mlplatform #datascience #mlengineering #platformengineering #dataengineering #ai #mlinproduction
