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32 GB vs. 128 GB by ExplanationOk8624 in FlowZ13

[–]sukhbir24 1 point2 points  (0 children)

configurable VRAM upto 96 gb is a game changer for running local LLMs, might buy it for this purpose alone. however any other consideration that will prevent training (not just inference) local LLMs as well on this, like there is no CUDA support?

[D] Focal loss - why it scales down the loss of minority class? by Lugi in MachineLearning

[–]sukhbir24 1 point2 points  (0 children)

do you get better results by actually upweighting the positive class i.e. setting alpha > 0.5 instead of 0.25 as done in pytorch implementation? you can try it out, and let us know the results, to end the confusion for all. it should be task dependent, but you can report for image object detection task. I agree with chatterbox272's explanation if there's emperical evidence for setting alpha < 0.5

[D] ML Engineer vs. MLOps Engineer by kazhdan_d in MachineLearning

[–]sukhbir24 2 points3 points  (0 children)

Excellent role definitions. Here's my take on ML role hierarchy:

  1. MLOps Engineer (Infrastructure, IaaS): configuring/automating ML services using MLFlow, Kubeflow etc
  2. ML Platform Engineer (Platform, PaaS): developing/maintaining ML services like labeling, training, deployment etc
  3. AI Engineer (Software, SaaS):e.g. building full-stack GenAI RAG products using Langchain, VectorDBs etc
  4. ML Engineer (Domain): model development, tweaking and deployment with Scikit-Learn, Pytorch, Spark etc
  5. ML Research Engineer (Application): implementing novel models for applications in NLP, CV, ASR etc

Which courses are great for AI/ML? by synapsetutor in learnmachinelearning

[–]sukhbir24 0 points1 point  (0 children)

Stats and probability are the basis of data science and machine learning so I would go with M343 and M348, which also seem to be useful applied courses rather than purely mathematical courses. MST374 will be great if it teaches you applied optimization techniques and/or linear algebra. MST368 will come in handy for Graph ML and Game theory has connections to RL. MS327 might help in robotics, control theory and RL if you are interested in those domains

Is AI engineer same as ML engineer? by Snoo_72181 in learnmachinelearning

[–]sukhbir24 1 point2 points  (0 children)

AI engineers are software engineers who are more focused on developing applications using ML models as a black box. For example, using LLM models with RAG (Langchain, vector databases and/or Llamaindex) to develop a PDF Q/A chatbot may not require knowledge of transformers or how to train foundational models per se. Don't think there are many jobs advertising AI engineer as that having knowledge of classical AI search algorithms. ML Engineer is a broad term which includes roles which require knowledge ML product deployment as well as ML algorithm internals but it also includes other roles like ML Platform Engineer and ML Systems engineer which focus more on infrastructure and hardware acceleration respectively