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

  • [P]I turned Elon Musk's face into a decision boundary.
    by /u/lildaemon (Machine Learning) on March 29, 2024 at 8:44 am

    I've seen examples of 2d decision boundaries taking on odd shapes, like spirals, and I've always been curious just how flexible neural networks can be. To that end, I tried to get it to learn a photograph, Elon Musk's face, and it worked. It seems to be the case that decision boundaries can be arbitrarily complex, assuming a sufficiently complex model. The photo is from wikipedia.jpg). The model takes in the x and y coordinates of each pixel, and is trained to predict the grayscale value mapped to values between 0 and 1. I used a decision threshold of 0.5. I've included both the image after applying the threshold(which illustrates the decision boundary), and the grayscale that the model generated before applying the threshold. I've also included what the model thinks a continuation of the image would look like. I also made a video of the training process, one image every few epochs, but can't share it on reddit :(. Anyway, hope everyone enjoys the pictures! ​ Elon the Decision Boundary ​ Elon the Grayscale (generated coordinate by coordinate) ​ Elon, Beyond the Frame -- what the NN thinks is outside of the picture. ​ submitted by /u/lildaemon [link] [comments]

  • [D] What is the effect of sampling rate on parameter estimation when fitting a markov state model to timeseries data?
    by /u/SnooPineapples375 (Machine Learning) on March 29, 2024 at 8:37 am

    Let us say that I have some timeseries data, which can be described by a markov state model. And the time series has been sampled every $Δt$ time units. The sampling rate (1/Δt) must control how much infromation we can extract from the timeseries. A slower sampling rate will essentially miss faster transtions and consequently yield poor estimates of transition rates. Or if the sample rate is too slow compared to some transition rates some states might not even show up in the collected data. I know that the maximum likelihood estimator for transtion probabilities is given by pij=nij/∑nij Here, pij is the transition probability from state i to j, and nij is the number of transitions from i to j. I am searching for a book where the effect of sampling frequency has been discussed systematically in this context and hopefully demonstrated with a simple example. Also, are there any analytical results which relate the error in the estimated parmeters to the sampling frequency? submitted by /u/SnooPineapples375 [link] [comments]

  • [D] Machine Learning feature versus Mobile/Web feature shipping
    by /u/Muse_Not_Found (Machine Learning) on March 29, 2024 at 7:02 am

    Hi redditors! I am a Machine Learning Engineer with 4 YOE in a product based startup where the tech team is of around 20 members and I am the only Machine Learning engineer here. The problem that I am currently facing at the moment is that ML features are expected to be shipped at the same rate as Mobile/Web features. Nobody here understands ML including the Engineering Manager. I am also expected to create Jira tickets the way developers do and I know for a fact that ML tasks never really follow the usual To-Do, In Dev, In Staging, In Review and In Prod lifecycle. The estimates are always subject to experimentation as one never knows what could go wrong during the training and so many variables that one person cannot handle it all alone. I don’t find the workplace to be toxic for sure but I would certainly like if people here start looking at things with a fresher perspective. Being here has made me go hands on with JS alongside Python. I have a good idea of how to take ML features live on production and how things work in native (both iOS and Android) although I might not be able to code in Kotlin and Swift. I love the growth here but I would certainly love if a few things change. Please feel free to drop your suggestions/experiences and I would be more than grateful. Thanks, again! submitted by /u/Muse_Not_Found [link] [comments]

  • ML Project Evaluation Questions [D]
    by /u/MuscleML (Machine Learning) on March 29, 2024 at 6:54 am

    Hey all, I was wondering what kinds of questions you would ask about a project if you were reviewing it for the first time. For context, we're teaching ML consulting to some people. I'll list my questions after the post has been up for a few days because I don't want to bias anyone. Give as many as you'd like. Thanks! submitted by /u/MuscleML [link] [comments]

  • [D] Critique my novel approach to adjust for multicollinearity in ensemble forecast averaging
    by /u/YourGoodFriend44 (Machine Learning) on March 29, 2024 at 6:50 am

    Hi everyone, I'm a recent high school graduate that is extremely interested in ML/stats, and will be starting my data science degree soon. Lately I have been reading about ensemble modelling and the approach of averaging forecasts, and then thought about the problem of highly correlated features/forecasts. I have been thinking about a novel approach to address correlated feature, and I would like to seek your feedback on this to know if I'm just being dumb or missing something important. Scenario Consider N factors, each used to produce a simple linear forecast of the target variable Y(t+1). So at any time t, there are N forecasts of Y at t+1. A final combined forecast is calculated by taking the average of each forecast. Multi-colinearity problem Averaging these forecasts assumes equal importance for each factor, as it assigns equal weight to all (w=1/N). However, this method inaccurately emphasizes factors highly correlated with each other. My approach to adjust for correlated features To mitigate this, what if you applied a weighting method to each feature or forecast, based on the absolute correlation between each feature and others, such that: Correlation adjusted weight for feature X = (1 - Average of absolute correlations to all other features) where the final effective weight for each feature is just their Correlation adjusted weight divided by the sum of all features Correlation adjusted weight. The reason I use the average of a features absolute correlation to other features is that, whether a feature has a correlation of 1 or -1 with another feature, either way the feature adds no new value or information (unless I am wrong?). Rationale Each features weight should be adjusted down based on its correlation to other features, as this correlation quantifies duplication of information. Simplified Example Imagine 3 factors X1, X2, X3, where X1 has a correlation with X2 and X3 of 0, X2 and X3 have a correlation of 1. Averaging forecasts equally implies each factor is weighted 1/N (in this case, 1/3). However, if X2 and X3 are virtually identical, this means that X2 and X3 are virtually the same thing, and that averaging the forecasts of X1, X2, X3, results in incorrectly overweighting X2 and X3 (w=2/3), and underweighting X1 (w = 1/3). So using my approach to adjust each factors weighting, we get: Adjusted weight of X2= 1 - (1+0)/2 = 0.5 Adjusted weight of X3 = 1 - (1+0)/2 = 0.5 Adjusted weight of X1 = 1 - (0+0)/2 = 1 Sum of adjusted weights = 0.5 + 0.5 + 1 = 2 and if we take the ratio of each factors adjusted weight agains the sum of adjusted weights, we get Effective adjusted weight of X2 = 0.5 / 2 = 0.25 Effective adjusted weight of X3 = 0.5 / 2 = 0.25 Effective adjusted weight of X1 = 1 / 2 = 0.5 So as you can see, the adjustment has adjusted for X2 and X3's perfect correlation, as The sum of the effective adjusted weight of X2 and X3 sum to 1/2 and the effective adjusted weight of X1 is now 1/2 _____________________ My questions What are your thoughts on this approach? Does it logically make sense? Are there similar models/methods approaches? Any key flaws? ..... i.e Am I being a complete amateur idiot by ignoring something critual or obvious? Thank you! submitted by /u/YourGoodFriend44 [link] [comments]

  • [D]Are there any other non trivial use cases of Transformers?
    by /u/ApartmentEither4838 (Machine Learning) on March 29, 2024 at 6:00 am

    Seq2Seq prediction architectures were designed for sequence prediction and are naturally SOTA in text generations, but are there any other non trivial tasks where we can use them? Like MeshGPT uses a gpt model for mesh generation, and diffusion transformer are also now being studied infact sora uses one. Are there many other applications where these models might be efficient and scalable ? submitted by /u/ApartmentEither4838 [link] [comments]

  • Would people be interested in a discord server/blog that aggregates papers posted here? [D]
    by /u/shadowylurking (Machine Learning) on March 29, 2024 at 5:32 am

    Hi, so many posters post their research work and papers that they've found interesting its really hard to keep track. I've bookmarked interesting papers only to forget all about them. Was wondering if it'd be worthwhile for people here to do a discord server (updated daily) or a medium blog (updated weekly) that tracks papers posted on this sub. submitted by /u/shadowylurking [link] [comments]

  • [D] How would you answer this interview question?
    by /u/Conscious_Giraffe453 (Machine Learning) on March 29, 2024 at 3:56 am

    Not sure if this is a “career question” as per the rules but I was recently asked this interview question: In an F1 car race with 10 cars, how would you calculate/predict the probability of the second- place car overtaking the first-place car? What algorithms, data, and models are needed for this calculation? Explain each step. How would you answer this? (No other information is given) submitted by /u/Conscious_Giraffe453 [link] [comments]

  • [P] Jamba: the first production-grade Mamba-based model delivering best-in-class quality and performance.
    by /u/ghosthamlet (Machine Learning) on March 29, 2024 at 3:39 am

    Post: https://www.ai21.com/blog/announcing-jamba We are thrilled to announce Jamba, the world’s first production-grade Mamba based model. By enhancing Mamba Structured State Space model (SSM) technology with elements of the traditional Transformer architecture, Jamba compensates for the inherent limitations of a pure SSM model. Offering a 256K context window, it is already demonstrating remarkable gains in throughput and efficiency—just the beginning of what can be possible with this innovative hybrid architecture. Notably, Jamba outperforms or matches other state-of-the-art models in its size class on a wide range of benchmarks. ​ submitted by /u/ghosthamlet [link] [comments]

  • [Discussion] Struggling with a derivation in the diffusion probabilistic model paper
    by /u/possiblymonk (Machine Learning) on March 29, 2024 at 3:24 am

    Can someone help me figure out the error in my derivation from the paper, Deep Unsupervised Learning using Nonequilibrium Thermodynamics" (2015): https://arxiv.org/pdf/1503.03585.pdf I have posted the full question on AI stack exchange in the link below: https://ai.stackexchange.com/questions/45272/trying-to-understand-some-derivation-in-the-paper-deep-unsupervised-learning-us submitted by /u/possiblymonk [link] [comments]

  • Code base documentation and testing using LLM [P]
    by /u/Soaccer (Machine Learning) on March 29, 2024 at 2:04 am

    Hi all , We were working on a new algorithm to train LLMs. We reached a breakthrough and the training algo is able to make an extremely small model match the performance of gpt-3.5 for a lot of tasks. We have open sourced a lib that documents your entire code base and runs dynamic analysis on your code. Here are the links https://github.com/PipableAI/pip-library-etl submitted by /u/Soaccer [link] [comments]

  • [N] Opportunities of GenAI in Healthcare
    by /u/PriorSuccessful156 (Machine Learning) on March 28, 2024 at 11:51 pm

    Not sure how many folks here are into genAI in healthcare... this is a great substack that outlines the opportunities and challenges in deploying LLMs: https://ambarbhattacharyya.substack.com/p/re-imagining-the-healthcare-delivery?r=12ee1&utm_campaign=post&utm_medium=web&triedRedirect=true submitted by /u/PriorSuccessful156 [link] [comments]

  • [D] What's the purpose of the transpose in official LoRA implementation code?
    by /u/kessa231 (Machine Learning) on March 28, 2024 at 9:21 pm

    Just skimmed their official implementation code and curious about this. For example, in their Embedding module they declared and used lora parameters like this: self.lora_A = nn.Parameter(self.weight.new_zeros((r, num_embeddings))) self.lora_B = nn.Parameter(self.weight.new_zeros((embedding_dim, r))) ... self.weight.data -= (self.lora_B @ self.lora_A).transpose(0, 1) * self.scaling ... after_A = F.embedding( x, self.lora_A.transpose(0, 1), self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse ) result += (after_A @ self.lora_B.transpose(0, 1)) * self.scaling ... So, why don't they just declare like this and use without transpose? self.lora_A = nn.Parameter(self.weight.new_zeros((r, embedding_dim))) self.lora_B = nn.Parameter(self.weight.new_zeros((num_embeddings, r))) What's the purpose of these transpose things? (official code link) submitted by /u/kessa231 [link] [comments]

  • [D] Is this contrastive learning? Does it help reduce class imbalance?
    by /u/AstronautVarious3791 (Machine Learning) on March 28, 2024 at 8:59 pm

    Class imbalance is a common problem with binary classification especially in search problems (where one result was clicked and the others were ignored). An alternative approach is to treat it as a multi-class classification problem where the classes are the search results shown to the user. So imagine a user was shown 20 results and they clicked on just one result. So we could make the model make 20 predictions (one for each result), apply softmax + cross entropy loss, and make the model learn to produce a high probability for the one result that was clicked. So we have effectively eliminated the binary class imbalance. Is there a standard name for this technique? Does this fall under the umbrella of contrastive learning? Does this effectively solve the class imbalance problem, since we are now making one multi-class training example from several binary classification problems which were imbalanced? Thanks submitted by /u/AstronautVarious3791 [link] [comments]

  • [R] NL-ITI: modifying LLM internal representations to make it more truthful
    by /u/autonomous_llm (Machine Learning) on March 28, 2024 at 8:30 pm

    Hi, here you can find our recent publication (along with code) in which we modify LLM internal representations to make it more truthful. In short, we optimized ITI method ( 2306.03341.pdf (arxiv.org) ) and achieved significant performance improvement. Evaluation was performed mostly on TruthfulQA, though we also tested generalization beyond it (MMLU, ARC, OpenBookQA). We used KL and CE metrics, to measure how invasive is intervention. https://paperswithcode.com/paper/nl-iti-optimizing-probing-and-intervention submitted by /u/autonomous_llm [link] [comments]

  • 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 adapted to work 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] 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] 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]

  • Advanced RAG patterns on Amazon SageMaker
    by Niithiyn Vijeaswaran (AWS Machine Learning Blog) on March 28, 2024 at 4:18 pm

    Today, customers of all industries—whether it’s financial services, healthcare and life sciences, travel and hospitality, media and entertainment, telecommunications, software as a service (SaaS), and even proprietary model providers—are using large language models (LLMs) to build applications like question and answering (QnA) chatbots, search engines, and knowledge bases. These generative AI applications are not only

  • Efficient continual pre-training LLMs for financial domains
    by Yong Xie (AWS Machine Learning Blog) on March 28, 2024 at 4:08 pm

    Large language models (LLMs) are generally trained on large publicly available datasets that are domain agnostic. For example, Meta’s Llama models are trained on datasets such as CommonCrawl, C4, Wikipedia, and ArXiv. These datasets encompass a broad range of topics and domains. Although the resulting models yield amazingly good results for general tasks, such as

  • 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 EDIT: Regarding the title of the post: Hallucination is defined (in Wikipedia) as "a response generated by AI which contains false or misleading information presented as fact.": Your code that does not compile is not, by itself, a hallucination. When you claim that the code is perfect, that's a hallucination. submitted by /u/we_are_mammals [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] 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]

  • 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

  • [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]

  • 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

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