AWS Machine Learning Certification Specialty Exam Prep

AWS Machine Learning Specialty Certification Prep (Android)

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The AWS Certified Machine Learning Specialty validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.

Use this App to learn about Machine Learning on AWS and prepare for the AWS Machine Learning Specialty Certification MLS-C01.

Download AWS machine Learning Specialty Exam Prep App on iOs

Download AWS Machine Learning Specialty Exam Prep App on Android/Web/Amazon

AWS MLS-C01 Machine Learning Specialty Exam Prep PRO

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AWS machine learning certification prep
AWS machine learning certification prep

Download AWS machine Learning Specialty Exam Prep App on iOs

Download AWS Machine Learning Specialty Exam Prep App on Android/Web/Amazon

The App provides hundreds of quizzes and practice exam about:

– Machine Learning Operation on AWS

– Modelling

– Data Engineering

– Computer Vision,

– Exploratory Data Analysis,

– ML implementation & Operations

– Machine Learning Basics Questions and Answers

– Machine Learning Advanced Questions and Answers

– Scorecard

– Countdown timer

– Machine Learning Cheat Sheets

– Machine Learning Interview Questions and Answers

– Machine Learning Latest News

The App covers Machine Learning Basics and Advanced topics including: NLP, Computer Vision, Python, linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, etc.

Domain 1: Data Engineering

Create data repositories for machine learning.

Identify data sources (e.g., content and location, primary sources such as user data)

Determine storage mediums (e.g., DB, Data Lake, S3, EFS, EBS)

Identify and implement a data ingestion solution.

Data job styles/types (batch load, streaming)

Data ingestion pipelines (Batch-based ML workloads and streaming-based ML workloads), etc.

Domain 2: Exploratory Data Analysis

Sanitize and prepare data for modeling.

Perform feature engineering.

Analyze and visualize data for machine learning.

Domain 3: Modeling

Frame business problems as machine learning problems.

Select the appropriate model(s) for a given machine learning problem.

Train machine learning models.

Perform hyperparameter optimization.

Evaluate machine learning models.

Domain 4: Machine Learning Implementation and Operations

Build machine learning solutions for performance, availability, scalability, resiliency, and fault

tolerance.

Recommend and implement the appropriate machine learning services and features for a given

problem.

Apply basic AWS security practices to machine learning solutions.

Deploy and operationalize machine learning solutions.

Machine Learning Services covered:

Amazon Comprehend

AWS Deep Learning AMIs (DLAMI)

AWS DeepLens

Amazon Forecast

Amazon Fraud Detector

Amazon Lex

Amazon Polly

Amazon Rekognition

Amazon SageMaker

Amazon Textract

Amazon Transcribe

Amazon Translate

Other Services and topics covered are:

Ingestion/Collection

Processing/ETL

Data analysis/visualization

Model training

Model deployment/inference

Operational

AWS ML application services

Language relevant to ML (for example, Python, Java, Scala, R, SQL)

Notebooks and integrated development environments (IDEs),

S3, SageMaker, Kinesis, Lake Formation, Athena, Kibana, Redshift, Textract, EMR, Glue, SageMaker, CSV, JSON, IMG, parquet or databases, Amazon Athena

Amazon EC2, Amazon Elastic Container Registry (Amazon ECR), Amazon Elastic Container Service, Amazon Elastic Kubernetes Service , Amazon Redshift

Important: To succeed with the real exam, do not memorize the answers in this app. It is very important that you understand why a question is right or wrong and the concepts behind it by carefully reading the reference documents in the answers.

Note and disclaimer: We are not affiliated with Microsoft or Azure or Google or Amazon. The questions are put together based on the certification study guide and materials available online. The questions in this app should help you pass the exam but it is not guaranteed. We are not responsible for any exam you did not pass.

Download AWS machine Learning Specialty Exam Prep App on iOs

Download AWS Machine Learning Specialty Exam Prep App on Android/Web/Amazon

  • Adaptive RAG: A retrieval technique to reduce LLM token cost for top-k Vector Index retrieval [R]
    by /u/dxtros (Machine Learning) on March 28, 2024 at 6:55 pm

    Abstract: We demonstrate a technique which allows to dynamically adapt the number of documents in a top-k retriever RAG prompt using feedback from the LLM. This allows a 4x cost reduction of RAG LLM question answering while maintaining the same level of accuracy. We also show that the method helps explain the lineage of LLM outputs. The reference implementation works with most models (GPT4, many local models, older GPT-3.5 turbo) and can be used with most vector databases exposing a top-k retrieval primitive. Blog paper: https://pathway.com/developers/showcases/adaptive-rag Reference implementation: https://github.com/pathwaycom/pathway/blob/main/python/pathway/xpacks/llm/question_answering.py submitted by /u/dxtros [link] [comments]

  • [D] Suggested readings on distributed inference
    by /u/Shintuku1 (Machine Learning) on March 28, 2024 at 6:37 pm

    I'm looking for readings on distributed inference: is it at all possible? Is there any system architecture that makes this feasible, or at all worthwhile? What approaches are there to distributed inference? I'm getting a number of hits on Google Scholar; anything you personally consider worthwhile digging into? submitted by /u/Shintuku1 [link] [comments]

  • [D] Advice needed!!
    by /u/ray_ashh (Machine Learning) on March 28, 2024 at 5:44 pm

    I am currently a sophomore studying computer science. In this era of AI, is it necessary for me to learn the inner workings of AI like the math and other stuff or should I directly dive into the top level stuff and create projects based on models made by others. What would be better for me to break into jobs in AI startups or MNC's. submitted by /u/ray_ashh [link] [comments]

  • [D] Stanford's BioMedLM Paper reported accuracy vs Evaluated accuracy: Doesn't make sense
    by /u/aadityaura (Machine Learning) on March 28, 2024 at 5:32 pm

    Stanford releases #BioMedLM, a 2.7B parameter language model trained on biomedical data. However, the results do not seem to make sense. Here is the evaluation report using the LM Evaluation Harness framework on MultiMedQA (MedMCQA, MedQA, MMLU, PubMed). https://preview.redd.it/vd21crtn14rc1.png?width=1442&format=png&auto=webp&s=ee905e8277006e40c37b7e5b87003165bd0de4b5 https://preview.redd.it/6ot7mibo14rc1.png?width=1164&format=png&auto=webp&s=5d76fcce909fb07d5404e148b0cdc2fbc6dae43c ​ submitted by /u/aadityaura [link] [comments]

  • [D] What skills should I have to make the transition from Physics/EE/SWE to ML professionally
    by /u/Brilliant-Donkey-320 (Machine Learning) on March 28, 2024 at 5:24 pm

    I am looking to transition to a ML engineer (or DS possibly) in the future(1-3yrs) and (I will continue to work as a SWE, but possibly with a job in Python in the meantime, TBD. I have my education and work background below. What skills and knowledge should I gain/brush up on? Any thing I should add to my rough plan I am doing below for the next year-ish. Courses: - CS50 AI with Python - Andrew Ng ML Specialization - Andrew Ng DL Specialization These courses seem to give a good base (but not too deep) (These teach with TF instead of PyTorch. Side question, is it easy to transition from TF to PyTorch?) Also, I will be reading Introduction to Statistical Learning in Python. This book seems to have some good depth to it at a glance. Also also, just review my linear algebra, probability and statistics, multivariable calculus. During this process, I thought it would be good get some Kaggle datasets and do some projects with them. Any suggestions or thoughts on what I am maybe missing or have overlooked? Thanks! Education Background: BSc - Physics MSc- Electronics and ICT Work Background: Hardware engineer: 1.5yrs Software engineer: 1.5yrs. C# .NET, desktop application submitted by /u/Brilliant-Donkey-320 [link] [comments]

  • [D] Suggestions on organizing and monitoring multi-model training
    by /u/pwinggles (Machine Learning) on March 28, 2024 at 5:05 pm

    Hey all, I have a project that, for me, is a bit complicated and so I'm trying to scheme out the best structure for it prior to getting things running, and I'm looking for some advice. The situation: I have 4 tabular predictor datasets, each of which has 31 response variables (RV) for which I need to train regression models (using XGBoost). By the end, I will have 124 (4 * 31) trained models. Ideally, for each RV I'd like to perform some form of K-fold cross-validated hyperparam optimization, and final model analysis will also be based on K-fold CV. The challenge: I'm trying to figure out the best way to organize all of this in such a way that it isn't a complete mess when it comes to reproducibility and analysis as well as having the potential to add new predictor data and/or new RVs. I've done this once before and I opted for just writing data out to a CSV, but that quickly became unwieldy and ended up requiring a lot of extra code just to handle and parse the results sanely. I'd really like to be able to visualize the training and performance for each of the models, but most of the examples of popular tools in this space seem to focus training a single model, with "experiments" generally referring to different hyperparams or feature modifications. DVC, Aim, WandB all look appealing, but I'm not quite sure how to conceptualize my particular workflow, and I'd like to avoid any eventual limiting pitfalls in the future by making sure my initial seutp is sound. I'd love to hear how others have organized such multi-model/ensemble training projects! submitted by /u/pwinggles [link] [comments]

  • [D] A Little guide to building Large Language Models in 2024 – 75min lecture
    by /u/Thomjazz (Machine Learning) on March 28, 2024 at 4:26 pm

    I finally recorded this lecture I gave two weeks ago because people kept asking me for a video. So here it is, I hope you'll enjoy it "A Little guide to building Large Language Models in 2024". I tried to keep it short and comprehensive – focusing on concepts that are crucial for training good LLM but often hidden in tech reports. In the lecture, I introduce the students to all the important concepts/tools/techniques for training good performance LLM:- finding, preparing and evaluating web scale data- understanding model parallelism and efficient training- fine-tuning/aligning models- fast inference There is of course many things and details missing and that I should have added to it, don't hesitate to tell me you're most frustrating omission and I'll add it in a future part. In particular I think I'll add more focus on how to filter topics well and extensively and maybe more practical anecdotes and details. Now that I recorded it I've been thinking this could be part 1 of a two-parts series with a 2nd fully hands-on video on how to run all these steps with some libraries and recipes we've released recently at HF around LLM training (and could be easily adapted to your other framework anyway): datatrove for all things web-scale data preparation: https://github.com/huggingface/datatrove nanotron for lightweight 4D parallelism LLM training: https://github.com/huggingface/nanotron lighteval for in-training fast parallel LLM evaluations: https://github.com/huggingface/lighteval Here is the link to watch the lecture on Youtube: https://www.youtube.com/watch?v=2-SPH9hIKT8And here is the link to the Google slides: https://docs.google.com/presentation/d/1IkzESdOwdmwvPxIELYJi8--K3EZ98_cL6c5ZcLKSyVg/edit#slide=id.p Enjoy and happy to hear feedback on it and what to add, correct, extend in a second part. submitted by /u/Thomjazz [link] [comments]

  • The end of hallucination (for those who can afford it)? [R]
    by /u/we_are_mammals (Machine Learning) on March 28, 2024 at 3:04 pm

    DeepMind just published a paper about fact-checking text: ​ https://preview.redd.it/zsmv0a0293rc1.png?width=1028&format=png&auto=webp&s=789c1c2f9b31aa734a7ebcf459df3ad06bd74285 The approach costs $0.19 per model response, using GPT-3.5-Turbo, which is cheaper than human annotators, while being more accurate than them: ​ https://preview.redd.it/ob7bb3iv73rc1.png?width=1014&format=png&auto=webp&s=e79bbcaa578b29772cb3b43ead508daff7288091 They use this approach to create a factuality benchmark and compare some popular LLMs. Paper and code: https://arxiv.org/abs/2403.18802 submitted by /u/we_are_mammals [link] [comments]

  • [Discussion] MS in bioinformatics&digital health with minor in Machine Learning, Data science and AI? Is this a good combo for jobs in tech
    by /u/maansaee1 (Machine Learning) on March 28, 2024 at 2:11 pm

    Hi! I need advice. Will I be able to find jobs in the Machine Learning, Data Science and AI field if I have a MS in Bioinformatics with a minor in Machine learning? Bioinformatics is a niche field, and I’m worried that my minor will not be enough to transition into the tech field if there are no good jobs available in bioinf. The bioinformatics major also consists of a lot of machine learning and data analysis. Here’s more info in the courses listed in the major for anyone interested: https://www.aalto.fi/en/programmes/masters-programme-in-life-science-technologies/curriculum-2022-2024. Should I stick with only a minor in machine learning, or should I just complete my MS im machine learning? I’m interested in both fields, and want to make sure my future salary is good. submitted by /u/maansaee1 [link] [comments]

  • [D] Anyone remembers the AI company Element AI?
    by /u/xiikjuy (Machine Learning) on March 28, 2024 at 10:49 am

    Founded in 2016. Yoshua Bengio was one of its co-founders. Sold for 230M in 2020. I think with their talents, they could have provided one of the top llms now. Timing is really important for a startup ... submitted by /u/xiikjuy [link] [comments]

  • [D] How is deep learning used to correct spelling errors in search engines?
    by /u/Seankala (Machine Learning) on March 28, 2024 at 10:46 am

    I was reading this 2021 blog post by Google about how Google handles spelling errors: https://blog.google/products/search/abcs-spelling-google-search/ They said that they introduced deep learning into their search but to me it's not really that straightforward. What is the input and output? Is the input a potentially misspelled query, and is the output a 0 or 1 where 1 is misspelled? Or is the input a potentially misspelled query and is the model generating a potentially correct query? How is deep learning used in general for search? submitted by /u/Seankala [link] [comments]

  • [D] a sentence level transformer to improve memory for a token level transformer?
    by /u/Alarming-Ad8154 (Machine Learning) on March 28, 2024 at 10:29 am

    I have an (probably dumb) idea for long term transformer memory. You can embed sentences into vectors of length ~128 - ~2048 right? Then you can cluster those sentences and effectively project them into lower dimensional spaces. I have often wondered whether you could take ~50.000 cardinal points in the embedding space (points such that the summed of squared distance to all sentences in a representative corpus is minimal). You'd then map each sentence in a big corpus to the nearest point, these points are then used as tokens. Subsequently you encode a massive text library into these tokens, and train a bog standard GPT model to predict "next sentence". Given the model deals in "sentences", even a 4096 context length would be BIG, but it wouldn't be able to give you the details of these sentence, as the 50k tokens are a very coarse representation of all possible sentences. However you could then train a token level model to predict next token, which takes input from both its own context (previous 4096 tokens, or more, whatever is expedient), AND the sentence level prediction model, which would have a courser memory going WAY WAY back... You could potentially use a cross attention style mechanism to feed the next sentence level model into the next token level model. its sort of a multi-modal model but the modalities are both text, just at different levels of organisation? submitted by /u/Alarming-Ad8154 [link] [comments]

  • [D]Evaluating xG Models: Comparing Discrete Outcomes with Continuous Predictions
    by /u/tipoviento (Machine Learning) on March 28, 2024 at 10:13 am

    I've recently developed an xG (expected goals) model using event data, and I'm exploring the best methods for evaluating its accuracy. Given the nature of football, where goals are discrete (or if we look at each shot, it is a binary outcome) but my model predicts a continuous probability range (0,1). I'm curious about the most appropriate statistical techniques or metrics for comparison, rather than just MSE/RMSE. How do you assess the accuracy of your xG models under these conditions? Any advice or references on this topic would be greatly appreciated. submitted by /u/tipoviento [link] [comments]

  • [N] The 77 French legal codes are now available via Hugging Face's Datasets library with daily updates
    by /u/louisbrulenaudet (Machine Learning) on March 28, 2024 at 7:37 am

    This groundwork enables ecosystem players to consider deploying RAG solutions in real time without having to configure data retrieval systems. Link to Louis Brulé-Naudet's Hugging Face profile ```python import concurrent.futures import logging from datasets from tqdm import tqdm def dataset_loader( name:str, streaming:bool=True ) -> datasets.Dataset: """ Helper function to load a single dataset in parallel. Parameters ---------- name : str Name of the dataset to be loaded. streaming : bool, optional Determines if datasets are streamed. Default is True. Returns ------- dataset : datasets.Dataset Loaded dataset object. Raises ------ Exception If an error occurs during dataset loading. """ try: return datasets.load_dataset( name, split="train", streaming=streaming ) except Exception as exc: logging.error(f"Error loading dataset {name}: {exc}") return None def load_datasets( req:list, streaming:bool=True ) -> list: """ Downloads datasets specified in a list and creates a list of loaded datasets. Parameters ---------- req : list A list containing the names of datasets to be downloaded. streaming : bool, optional Determines if datasets are streamed. Default is True. Returns ------- datasets_list : list A list containing loaded datasets as per the requested names provided in 'req'. Raises ------ Exception If an error occurs during dataset loading or processing. Examples -------- >>> datasets = load_datasets(["dataset1", "dataset2"], streaming=False) """ datasets_list = [] with concurrent.futures.ThreadPoolExecutor() as executor: future_to_dataset = {executor.submit(dataset_loader, name): name for name in req} for future in tqdm(concurrent.futures.as_completed(future_to_dataset), total=len(req)): name = future_to_dataset[future] try: dataset = future.result() if dataset: datasets_list.append(dataset) except Exception as exc: logging.error(f"Error processing dataset {name}: {exc}") return datasets_list req = [ "louisbrulenaudet/code-artisanat", "louisbrulenaudet/code-action-sociale-familles", "louisbrulenaudet/code-assurances", "louisbrulenaudet/code-aviation-civile", "louisbrulenaudet/code-cinema-image-animee", "louisbrulenaudet/code-civil", "louisbrulenaudet/code-commande-publique", "louisbrulenaudet/code-commerce", "louisbrulenaudet/code-communes", "louisbrulenaudet/code-communes-nouvelle-caledonie", "louisbrulenaudet/code-consommation", "louisbrulenaudet/code-construction-habitation", "louisbrulenaudet/code-defense", "louisbrulenaudet/code-deontologie-architectes", "louisbrulenaudet/code-disciplinaire-penal-marine-marchande", "louisbrulenaudet/code-domaine-etat", "louisbrulenaudet/code-domaine-etat-collectivites-mayotte", "louisbrulenaudet/code-domaine-public-fluvial-navigation-interieure", "louisbrulenaudet/code-douanes", "louisbrulenaudet/code-douanes-mayotte", "louisbrulenaudet/code-education", "louisbrulenaudet/code-electoral", "louisbrulenaudet/code-energie", "louisbrulenaudet/code-entree-sejour-etrangers-droit-asile", "louisbrulenaudet/code-environnement", "louisbrulenaudet/code-expropriation-utilite-publique", "louisbrulenaudet/code-famille-aide-sociale", "louisbrulenaudet/code-forestier-nouveau", "louisbrulenaudet/code-fonction-publique", "louisbrulenaudet/code-propriete-personnes-publiques", "louisbrulenaudet/code-collectivites-territoriales", "louisbrulenaudet/code-impots", "louisbrulenaudet/code-impots-annexe-i", "louisbrulenaudet/code-impots-annexe-ii", "louisbrulenaudet/code-impots-annexe-iii", "louisbrulenaudet/code-impots-annexe-iv", "louisbrulenaudet/code-impositions-biens-services", "louisbrulenaudet/code-instruments-monetaires-medailles", "louisbrulenaudet/code-juridictions-financieres", "louisbrulenaudet/code-justice-administrative", "louisbrulenaudet/code-justice-militaire-nouveau", "louisbrulenaudet/code-justice-penale-mineurs", "louisbrulenaudet/code-legion-honneur-medaille-militaire-ordre-national-merite", "louisbrulenaudet/livre-procedures-fiscales", "louisbrulenaudet/code-minier", "louisbrulenaudet/code-minier-nouveau", "louisbrulenaudet/code-monetaire-financier", "louisbrulenaudet/code-mutualite", "louisbrulenaudet/code-organisation-judiciaire", "louisbrulenaudet/code-patrimoine", "louisbrulenaudet/code-penal", "louisbrulenaudet/code-penitentiaire", "louisbrulenaudet/code-pensions-civiles-militaires-retraite", "louisbrulenaudet/code-pensions-retraite-marins-francais-commerce-peche-plaisance", "louisbrulenaudet/code-pensions-militaires-invalidite-victimes-guerre", "louisbrulenaudet/code-ports-maritimes", "louisbrulenaudet/code-postes-communications-electroniques", "louisbrulenaudet/code-procedure-civile", "louisbrulenaudet/code-procedure-penale", "louisbrulenaudet/code-procedures-civiles-execution", "louisbrulenaudet/code-propriete-intellectuelle", "louisbrulenaudet/code-recherche", "louisbrulenaudet/code-relations-public-administration", "louisbrulenaudet/code-route", "louisbrulenaudet/code-rural-ancien", "louisbrulenaudet/code-rural-peche-maritime", "louisbrulenaudet/code-sante-publique", "louisbrulenaudet/code-securite-interieure", "louisbrulenaudet/code-securite-sociale", "louisbrulenaudet/code-service-national", "louisbrulenaudet/code-sport", "louisbrulenaudet/code-tourisme", "louisbrulenaudet/code-transports", "louisbrulenaudet/code-travail", "louisbrulenaudet/code-travail-maritime", "louisbrulenaudet/code-urbanisme", "louisbrulenaudet/code-voirie-routiere" ] dataset = load_datasets( req=req, streaming=True ) ``` submitted by /u/louisbrulenaudet [link] [comments]

  • [D] What are some of the big tech company sponsored ML research websites that you are aware of for constantly keeping up with the ML research and workings behind their products, like Apple Machine Learning Research (https://machinelearning.apple.com/) or Tesla's AI day videos?
    by /u/pontiac_RN (Machine Learning) on March 28, 2024 at 5:08 am

    It would be great if there were a bundle of such sources or if you have a go to place where you keep up to date with all the new research going on. submitted by /u/pontiac_RN [link] [comments]

  • [D] Machine Learning On The Edge
    by /u/TheLastMate (Machine Learning) on March 28, 2024 at 2:29 am

    Hi guys, I found it today in my drawer. I forgot I had it and have never used it. Then it came to mind how is the current state of ML on the edge and are your predictions for the near future. We usually see big advances and news on big models but not much on applications on device. submitted by /u/TheLastMate [link] [comments]

  • [P] deit3-jax: A codebase for training ViTs on TPUs
    by /u/affjljoo3581 (Machine Learning) on March 27, 2024 at 9:54 pm

    Hey all, I have written a codebase to train ViTs by following DeiT and DeiT-III recipes. As they are strong baselines to train vanilla ViTs, it is necessary to reproduce to adopt to the variant research. However, the original repository is implemented in PyTorch, it is impossible to run on TPUs. Therefore I re-implemented the simple ViT training codebase with DeiT and DeiT-III training recipes. Here is my repository: https://github.com/affjljoo3581/deit3-jax. I used Jax/Flax and webdataset to build a TPU-friendly training environment. Below are the reproduction results: DeiT Reproduction Name Data Resolution Epochs Time Reimpl. Original Config Wandb Model T/16 in1k 224 300 2h 40m 73.1% 72.2% config log ckpt S/16 in1k 224 300 2h 43m 79.68% 79.8% config log ckpt B/16 in1k 224 300 4h 40m 81.46% 81.8% config log ckpt DeiT-III on ImageNet-1k Name Data Resolution Epochs Time Reimpl. Original Config Wandb Model S/16 in1k 224 400 2h 38m 80.7% 80.4% config log ckpt S/16 in1k 224 800 5h 19m 81.44% 81.4% config log ckpt B/16 in1k 192 → 224 400 4h 42m 83.6% 83.5% pt / ft pt / ft pt / ft B/16 in1k 192 → 224 800 9h 28m 83.91% 83.8% pt / ft pt / ft pt / ft L/16 in1k 192 → 224 400 14h 10m 84.62% 84.5% pt / ft pt / ft pt / ft L/16 in1k 192 → 224 800 - - 84.9% pt / ft - - H/14 in1k 154 → 224 400 19h 10m 85.12% 85.1% pt / ft pt / ft pt / ft H/14 in1k 154 → 224 800 - - 85.2% pt / ft - - DeiT-III on ImageNet-21k Name Data Resolution Epochs Time Reimpl. Original Config Wandb Model S/16 in21k 224 90 7h 30m 83.04% 82.6% pt / ft pt / ft pt / ft S/16 in21k 224 240 20h 6m 83.39% 83.1% pt / ft pt / ft pt / ft B/16 in21k 224 90 12h 12m 85.35% 85.2% pt / ft pt / ft pt / ft B/16 in21k 224 240 33h 9m 85.68% 85.7% pt / ft pt / ft pt / ft L/16 in21k 224 90 37h 13m 86.83% 86.8% pt / ft pt / ft pt / ft L/16 in21k 224 240 - - 87% pt / ft - - H/14 in21k 126 → 224 90 35h 51m 86.78% 87.2% pt / ft pt / ft pt / ft H/14 in21k 126 → 224 240 - - - pt / ft - - I trained all models on TPU v4-64 Pod slice, provided by the TRC program. I uploaded the checkpoints to the huggingface hub and you can also see the training logs on wandb. For more details, please check out my repository. submitted by /u/affjljoo3581 [link] [comments]

  • [D] How do you measure performance of AI copilot/assistant?
    by /u/n2parko (Machine Learning) on March 27, 2024 at 5:38 pm

    Curious to hear from those that are building and deploying products with AI copilots. How are you tracking the interactions? And are you feeding the interaction back into the model for retraining? Put together a how-to to do this with an OS Copilot (Vercel AI SDK) and Segment and would love any feedback to improve the spec: https://segment.com/blog/instrumenting-user-insights-for-your-ai-copilot/ submitted by /u/n2parko [link] [comments]

  • [D] What is the state-of-the-art for 1D signal cleanup?
    by /u/XmintMusic (Machine Learning) on March 27, 2024 at 4:52 pm

    I have the following problem. Imagine I have a 'supervised' dataset of 1D curves with inputs and outputs, where the input is a modulated noisy signal and the output is the cleaned desired signal. Is there a consensus in the machine learning community on how to tackle this simple problem? Have you ever worked on anything similar? What algorithm did you end up using? Example: https://imgur.com/JYgkXEe submitted by /u/XmintMusic [link] [comments]

  • Achieve DevOps maturity with BMC AMI zAdviser Enterprise and Amazon Bedrock
    by Sunil Bemarkar (AWS Machine Learning Blog) on March 27, 2024 at 4:37 pm

    This blog post discusses how BMC Software added AWS Generative AI capabilities to its product BMC AMI zAdviser Enterprise. The zAdviser uses Amazon Bedrock to provide summarization, analysis, and recommendations for improvement based on the DORA metrics data.

  • Fine-tune your Amazon Titan Image Generator G1 model using Amazon Bedrock model customization
    by Maira Ladeira Tanke (AWS Machine Learning Blog) on March 27, 2024 at 4:14 pm

    Amazon Titan lmage Generator G1 is a cutting-edge text-to-image model, available via Amazon Bedrock, that is able to understand prompts describing multiple objects in various contexts and captures these relevant details in the images it generates. It is available in US East (N. Virginia) and US West (Oregon) AWS Regions and can perform advanced image

  • [P] Insta Face Swap
    by /u/abdullahozmntr (Machine Learning) on March 27, 2024 at 2:03 pm

    ComfyUI node repo: https://github.com/abdozmantar/ComfyUI-InstaSwap Standalone repo: https://github.com/abdozmantar/Standalone-InstaSwap ​ ​ https://i.redd.it/9d4ti20fvvqc1.gif submitted by /u/abdullahozmntr [link] [comments]

  • [N] Introducing DBRX: A New Standard for Open LLM
    by /u/artificial_intelect (Machine Learning) on March 27, 2024 at 1:35 pm

    https://x.com/vitaliychiley/status/1772958872891752868?s=20 Shill disclaimer: I was the pretraining lead for the project DBRX deets: 16 Experts (12B params per single expert; top_k=4 routing) 36B active params (132B total params) trained for 12T tokens 32k sequence length training submitted by /u/artificial_intelect [link] [comments]

  • [D] Seeking Advice: Transitioning to Low-Level Implementations in AIoT Systems - Where to Start?
    by /u/MaTwickenham (Machine Learning) on March 27, 2024 at 1:20 pm

    Hello everyone, I'm a prospective graduate student who will be starting my studies in September this year, specializing in AIoT (Artificial Intelligence of Things) Systems. Recently, I've been reading papers from journals like INFOCOM and SIGCOMM, and I've noticed that they mostly focus on relatively low-level aspects of operating systems, including GPU/CPU scheduling, optimization of deep learning model inference, operator optimization, cross-platform migration, and deployment. I find it challenging to grasp the implementation details of these works at the code level. When I looked at the implementations of these works uploaded on GitHub, I found it relatively difficult to understand. My primary programming languages are Java and Python. During my undergraduate studies, I gained proficiency in implementing engineering projects and ideas using Python, especially in the fields of deep learning and machine learning. However, I lack experience and familiarity with C/C++ (many of the aforementioned works are based on C/C++). Therefore, I would like to ask for advice from senior professionals and friends on which areas of knowledge I should focus on. Do I need to learn CUDA programming, operating system programming, or other directions? Any recommended learning paths would be greatly appreciated. PS: Recently, I have started studying the MIT 6.S081 Operating System Engineering course. Thank you all sincerely for your advice. submitted by /u/MaTwickenham [link] [comments]

  • [P] Hybrid-Net: Real-time audio source separation, generate lyrics, chords, beat.
    by /u/CheekProfessional146 (Machine Learning) on March 27, 2024 at 12:11 pm

    Project: https://github.com/DoMusic/Hybrid-Net A transformer-based hybrid multimodal model, various transformer models address different problems in the field of music information retrieval, these models generate corresponding information dependencies that mutually influence each other. An AI-powered multimodal project focused on music, generate chords, beats, lyrics, melody, and tabs for any song. submitted by /u/CheekProfessional146 [link] [comments]

  • [P] Visualize RAG Data
    by /u/DocBrownMS (Machine Learning) on March 27, 2024 at 10:29 am

    Hey all, I've recently published a tutorial at Towards Data Science that explores a somewhat overlooked aspect of Retrieval-Augmented Generation (RAG) systems: the visualization of documents and questions in the embedding space: https://towardsdatascience.com/visualize-your-rag-data-evaluate-your-retrieval-augmented-generation-system-with-ragas-fc2486308557 While much of the focus in RAG discussions tends to be on the algorithms and data processing, I believe that visualization can help to explore the data and to gain insights into problematic subgroups within the data. This might be interesting for some of you, although I'm aware that not everyone is keen on this kind of visualization. I believe it can add a unique dimension to understanding RAG systems. submitted by /u/DocBrownMS [link] [comments]

  • [D] Is Synthetic Data a Reliable Option for Training Machine Learning Models?
    by /u/Data_Nerd1979 (Machine Learning) on March 27, 2024 at 3:49 am

    "The most obvious advantage of synthetic data is that it contains no personally identifiable information (PII). Consequently, it doesn’t pose the same cybersecurity risks as conventional data science projects. However, the big question for machine learning is whether this information is reliable enough to produce functioning ML models." Very informative blog regarding Using Synthetic Data in Machine Learning, source here https://opendatascience.com/is-synthetic-data-a-reliable-option-for-training-machine-learning-models/ submitted by /u/Data_Nerd1979 [link] [comments]

  • Build a receipt and invoice processing pipeline with Amazon Textract
    by Sushant Pradhan (AWS Machine Learning Blog) on March 26, 2024 at 3:35 pm

    In today’s business landscape, organizations are constantly seeking ways to optimize their financial processes, enhance efficiency, and drive cost savings. One area that holds significant potential for improvement is accounts payable. On a high level, the accounts payable process includes receiving and scanning invoices, extraction of the relevant data from scanned invoices, validation, approval, and

  • Best practices for building secure applications with Amazon Transcribe
    by Alex Bulatkin (AWS Machine Learning Blog) on March 25, 2024 at 5:15 pm

    Amazon Transcribe is an AWS service that allows customers to convert speech to text in either batch or streaming mode. It uses machine learning–powered automatic speech recognition (ASR), automatic language identification, and post-processing technologies. Amazon Transcribe can be used for transcription of customer care calls, multiparty conference calls, and voicemail messages, as well as subtitle

  • [D] Simple Questions Thread
    by /u/AutoModerator (Machine Learning) on March 24, 2024 at 3:00 pm

    Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]

  • Boost your content editing with Contentful and Amazon Bedrock
    by Ulrich Hinze (AWS Machine Learning Blog) on March 22, 2024 at 2:25 pm

    This post is co-written with Matt Middleton from Contentful. Today, jointly with Contentful, we are announcing the launch of the AI Content Generator powered by Amazon Bedrock. The AI Content Generator powered by Amazon Bedrock is an app available on the Contentful Marketplace that allows users to create, rewrite, summarize, and translate content using cutting-edge

  • Unlock the potential of generative AI in industrial operations
    by Julia Hu (AWS Machine Learning Blog) on March 19, 2024 at 3:55 pm

    In this post, multi-shot prompts are retrieved from an embedding containing successful Python code run on a similar data type (for example, high-resolution time series data from Internet of Things devices). The dynamically constructed multi-shot prompt provides the most relevant context to the FM, and boosts the FM’s capability in advanced math calculation, time series data processing, and data acronym understanding. This improved response facilitates enterprise workers and operational teams in engaging with data, deriving insights without requiring extensive data science skills.

  • Enhance performance of generative language models with self-consistency prompting on Amazon Bedrock
    by Lucia Santamaria (AWS Machine Learning Blog) on March 19, 2024 at 3:47 pm

    With the batch inference API, you can use Amazon Bedrock to run inference with foundation models in batches and get responses more efficiently. This post shows how to implement self-consistency prompting via batch inference on Amazon Bedrock to enhance model performance on arithmetic and multiple-choice reasoning tasks.

  • Optimize price-performance of LLM inference on NVIDIA GPUs using the Amazon SageMaker integration with NVIDIA NIM Microservices
    by James Park (AWS Machine Learning Blog) on March 18, 2024 at 9:25 pm

    NVIDIA NIM microservices now integrate with Amazon SageMaker, allowing you to deploy industry-leading large language models (LLMs) and optimize model performance and cost. You can deploy state-of-the-art LLMs in minutes instead of days using technologies such as NVIDIA TensorRT, NVIDIA TensorRT-LLM, and NVIDIA Triton Inference Server on NVIDIA accelerated instances hosted by SageMaker. NIM, part

  • Fine-tune Code Llama on Amazon SageMaker JumpStart
    by Xin Huang (AWS Machine Learning Blog) on March 18, 2024 at 4:31 pm

    Today, we are excited to announce the capability to fine-tune Code Llama models by Meta using Amazon SageMaker JumpStart. The Code Llama family of large language models (LLMs) is a collection of pre-trained and fine-tuned code generation models ranging in scale from 7 billion to 70 billion parameters. Fine-tuned Code Llama models provide better accuracy

  • Transform one-on-one customer interactions: Build speech-capable order processing agents with AWS and generative AI
    by Moumita Dutta (AWS Machine Learning Blog) on March 15, 2024 at 9:53 pm

    In today’s landscape of one-on-one customer interactions for placing orders, the prevailing practice continues to rely on human attendants, even in settings like drive-thru coffee shops and fast-food establishments. This traditional approach poses several challenges: it heavily depends on manual processes, struggles to efficiently scale with increasing customer demands, introduces the potential for human errors,

  • Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker
    by Randy DeFauw (AWS Machine Learning Blog) on March 15, 2024 at 5:21 pm

    This post is co-written with Chaoyang He, Al Nevarez and Salman Avestimehr from FedML. Many organizations are implementing machine learning (ML) to enhance their business decision-making through automation and the use of large distributed datasets. With increased access to data, ML has the potential to provide unparalleled business insights and opportunities. However, the sharing of

  • Enable data sharing through federated learning: A policy approach for chief digital officers
    by Nitin Kumar (AWS Machine Learning Blog) on March 15, 2024 at 4:53 pm

    This is a guest blog post written by Nitin Kumar, a Lead Data Scientist at T and T Consulting Services, Inc. In this post, we discuss the value and potential impact of federated learning in the healthcare field. This approach can help heart stroke patients, doctors, and researchers with faster diagnosis, enriched decision-making, and more

  • The journey of PGA TOUR’s generative AI virtual assistant, from concept to development to prototype
    by Ahsan Ali (AWS Machine Learning Blog) on March 14, 2024 at 7:53 pm

    This is a guest post co-written with Scott Gutterman from the PGA TOUR. Generative artificial intelligence (generative AI) has enabled new possibilities for building intelligent systems. Recent improvements in Generative AI based large language models (LLMs) have enabled their use in a variety of applications surrounding information retrieval. Given the data sources, LLMs provided tools

  • Enhance code review and approval efficiency with generative AI using Amazon Bedrock
    by Xan Huang (AWS Machine Learning Blog) on March 14, 2024 at 7:43 pm

    In the world of software development, code review and approval are important processes for ensuring the quality, security, and functionality of the software being developed. However, managers tasked with overseeing these critical processes often face numerous challenges, such as the following: Lack of technical expertise – Managers may not have an in-depth technical understanding of

  • Best practices to build generative AI applications on AWS
    by Jay Rao (AWS Machine Learning Blog) on March 14, 2024 at 5:15 pm

    Generative AI applications driven by foundational models (FMs) are enabling organizations with significant business value in customer experience, productivity, process optimization, and innovations. However, adoption of these FMs involves addressing some key challenges, including quality output, data privacy, security, integration with organization data, cost, and skills to deliver. In this post, we explore different approaches

  • Gemma is now available in Amazon SageMaker JumpStart 
    by Kyle Ulrich (AWS Machine Learning Blog) on March 14, 2024 at 12:33 am

    Today, we’re excited to announce that the Gemma model is now available for customers using Amazon SageMaker JumpStart. Gemma is a family of language models based on Google’s Gemini models, trained on up to 6 trillion tokens of text. The Gemma family consists of two sizes: a 7 billion parameter model and a 2 billion parameter model. Now,

  • Moderate audio and text chats using AWS AI services and LLMs
    by Lana Zhang (AWS Machine Learning Blog) on March 13, 2024 at 4:54 pm

    Online gaming and social communities offer voice and text chat functionality for their users to communicate. Although voice and text chat often support friendly banter, it can also lead to problems such as hate speech, cyberbullying, harassment, and scams. Today, many companies rely solely on human moderators to review toxic content. However, verifying violations in

  • Set up cross-account Amazon S3 access for Amazon SageMaker notebooks in VPC-only mode using Amazon S3 Access Points
    by Kiran Khambete (AWS Machine Learning Blog) on March 13, 2024 at 4:47 pm

    Advancements in artificial intelligence (AI) and machine learning (ML) are revolutionizing the financial industry for use cases such as fraud detection, credit worthiness assessment, and trading strategy optimization. To develop models for such use cases, data scientists need access to various datasets like credit decision engines, customer transactions, risk appetite, and stress testing. Managing appropriate

  • Run an audience overlap analysis in AWS Clean Rooms
    by Eric Saccullo (AWS Machine Learning Blog) on March 12, 2024 at 3:55 pm

    In this post, we explore what an audience overlap analysis is, discuss the current technical approaches and their challenges, and illustrate how you can run secure audience overlap analysis using AWS Clean Rooms.

  • Large language model inference over confidential data using AWS Nitro Enclaves
    by Chris Renzo (AWS Machine Learning Blog) on March 12, 2024 at 3:43 pm

    This post discusses how Nitro Enclaves can help protect LLM model deployments, specifically those that use personally identifiable information (PII) or protected health information (PHI). This post is for educational purposes only and should not be used in production environments without additional controls.

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