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

  • [D] Positional embeddings in LLMs
    by /u/gokstudio (Machine Learning) on June 20, 2024 at 11:35 pm

    Hi folks, As we know, we use positional embeddings to provide sequence information to transformers. From what I've seen in code and papers, the way we introduce this depends on the positional embedding strategy used. For example, 1. RoPE: add it to the query vectors in each attention layer 2. Alibi: add it to the attention masks in each layer 3. Learnable positional embeddings: add or concatenate to the token embeddings Why are there such differences? Specifically, why are #1 and #2 introduced at each attention layer but #3 is introduced only at the first layer? Thanks! submitted by /u/gokstudio [link] [comments]

  • [D] Good TTS Services??
    by /u/Quiet_Head_404 (Machine Learning) on June 20, 2024 at 9:34 pm

    Damn, ElevenLabs is expensive! I’ve been using eleven_monolingual_v1 and while I love the quality, the cost is just too much for me. I’m searching for cheaper TTS alternatives that don’t sound robotic. I don’t need voice cloning or multilingual features. I'm fine if it's slightly inferior. Will be streaming via API calls. Any suggestions? submitted by /u/Quiet_Head_404 [link] [comments]

  • [P] Using NeRFs to Convert Videos to VR Experiences
    by /u/ekolasky (Machine Learning) on June 20, 2024 at 9:03 pm

    Hi everyone, some friends and I are doing the Berkeley AI Hackathon this weekend and we had a crazy idea for our project. We want to use AI to convert a video of a scene into a VR experience. Ideally this experience would be "walkable" as in we would load the scene into Unity and load the scene onto a VR headset and allow the user to walk around. My background is in NLP so I'm have no idea how doable this project is. Obviously there's less ambitious variants we could try, such as just adding depth to the video to make it work with the Vision Pro. I'd love to get people's takes on this project; and it would be awesome if someone could send me resources so I can quickly learn up on NeRFs. Recent papers would be amazing, and any public online courses would be even better. Thanks in advance! submitted by /u/ekolasky [link] [comments]

  • [P] PixelProse 16M Dense Image Captions Dataset
    by /u/pidoyu (Machine Learning) on June 20, 2024 at 7:37 pm

    Hello everyone, Hope everything is well with you. We would like to introduce a new project from our group here. Hope it would be useful in your projects. We refresh the CC12M, RedCaps, and CommonPool with dense captions to produce a new 16M dataset using Gemini-1.0 Pro Vision, called PixelProse, consisting of over 16M pairs of image and dense caption. Hope you like it. Thanks. arXiv: https://arxiv.org/abs/2406.10328 huggingface repo: https://huggingface.co/datasets/tomg-group-umd/pixelprose Intro Figure: Dense synthetic image captions from PixelProse. Concrete phrases are highlighted in green, and negative descriptions are underlined in purple. submitted by /u/pidoyu [link] [comments]

  • Imperva optimizes SQL generation from natural language using Amazon Bedrock
    by Ori Nakar (AWS Machine Learning Blog) on June 20, 2024 at 5:19 pm

    This is a guest post co-written with Ori Nakar from Imperva. Imperva Cloud WAF protects hundreds of thousands of websites against cyber threats and blocks billions of security events every day. Counters and insights based on security events are calculated daily and used by users from multiple departments. Millions of counters are added daily, together

  • Create natural conversations with Amazon Lex QnAIntent and Knowledge Bases for Amazon Bedrock
    by Thomas Rindfuss (AWS Machine Learning Blog) on June 20, 2024 at 5:08 pm

    Customer service organizations today face an immense opportunity. As customer expectations grow, brands have a chance to creatively apply new innovations to transform the customer experience. Although meeting rising customer demands poses challenges, the latest breakthroughs in conversational artificial intelligence (AI) empowers companies to meet these expectations. Customers today expect timely responses to their questions

  • Evaluate the reliability of Retrieval Augmented Generation applications using Amazon Bedrock
    by Oussama Kandakji (AWS Machine Learning Blog) on June 20, 2024 at 5:04 pm

    In this post, we show you how to evaluate the performance, trustworthiness, and potential biases of your RAG pipelines and applications on Amazon Bedrock. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

  • Connect to Amazon services using AWS PrivateLink in Amazon SageMaker
    by Francisco Calderon Rodriguez (AWS Machine Learning Blog) on June 20, 2024 at 4:57 pm

    In this post, we present a solution for configuring SageMaker notebook instances to connect to Amazon Bedrock and other AWS services with the use of AWS PrivateLink and Amazon Elastic Compute Cloud (Amazon EC2) security groups.

  • [R] Image Restoration by Denoising Diffusion Models with Iteratively Preconditioned Guidance
    by /u/ViperTG98 (Machine Learning) on June 20, 2024 at 4:28 pm

    I'm excited to share that our paper "Image Restoration by Denoising Diffusion Models with Iteratively Preconditioned Guidance" has been accepted to CVPR 2024! In this work we propose a novel guidance technique, based on preconditioning that allows traversing from BP-based guidance to least squares based guidance along the restoration scheme. The proposed approach is robust to noise while still having much simpler implementation than alternative methods (e.g., it does not require SVD or a large number of iterations). We use it within both an optimization scheme and a sampling-based scheme, and demonstrate its advantages over existing methods for image deblurring and super-resolution. If you're in seattle, come visit tomorrow (June 21) - PM session poster #100! CVPR page: https://lnkd.in/dJzS92-G Paper: https://lnkd.in/dbDQpATY Code: https://lnkd.in/dukpjWAF submitted by /u/ViperTG98 [link] [comments]

  • [Project] Looking for Resources on Animating 3D Avatars Using Human-Pose Key Points from Video
    by /u/rajanghimire534 (Machine Learning) on June 20, 2024 at 3:10 pm

    Hi everyone, I'm working on a project where I want to animate a 3D avatar using human-pose key points from a video. The goal is to create a screen that mimics the dance steps of children. I have some experience with pose estimation, and I've come across a project on GitHub called DigiHuman, which is related. I'm looking for similar projects or resources to help me get started. Has anyone here worked on something like this before or do you know of any useful tools, libraries, or tutorials? Any advice or pointers would be greatly appreciated! Thanks in advance! submitted by /u/rajanghimire534 [link] [comments]

  • [R] starter code repos for RLHF?
    by /u/South-Conference-395 (Machine Learning) on June 20, 2024 at 2:24 pm

    Hello everyone, I am getting started with LLM research and RLHF in particular. I was looking for open course repos that can serve as a starting point. I found the following: 1) https://github.com/OpenLLMAI/OpenRLHF 2) https://github.com/huggingface/trl 3) https://github.com/CarperAI/trlx All of them seem to be compatible with the transformers library which in turn supports full open source (code+data, not only weights) models such as Pythia. All of them seem to be fairly updated. 1) and 3) support distributed training. Which one would you recommend? Any other suggestions? Apologies for my perhaps naive question. I am an LLM newbie 🙂 submitted by /u/South-Conference-395 [link] [comments]

  • [Project] Thoughts on algorithm plan for anomaly detection in time series data
    by /u/Imarami21 (Machine Learning) on June 20, 2024 at 1:55 pm

    Thoughts on algorithm plan for anomaly detection in time series data Hi all, I'm working on detecting spikes in time series data, specifically cultural artifacts in ground magnetic diurnal data. Manually, this involves comparing two or 3 ground stations and assessing whether spikes occur in both, just one, or shifted between them, etc., to determine if they're cultural artifacts. I want to automate this task since, something like an explicit algorithm computing, say, a sliding window with a threshold, is just too crude an approach. The good thing is, we have over 15 projects worth of raw and corrected data (training data). Each project includes 100 days of ground diurnal data, with 2-3 ground stations per day. I've already compiled the training data and am now exploring model options, that I would love your help on, please! In short:. Use an LSTM Model: My idea is this algorithm is good for anamoly detection It is flexible enough to handle variable features, i.e., varying numbers of ground stations. Implement a Dual-Stream LSTM Model: Process each ground station through its respective LSTM layer. Concatenate outputs from LSTM layers. Use a dense layer to classify the combined outputs. Handling Imbalanced Data: The dataset is highly skewed, with 99.5% of labels being 0 (normal) and only 0.5% being 1 (anomalies). Use class weighting or SMOTE technique to balance the dataset. For Model Training: Batch the Input Data: Each time data has ~90,000 points (frequency: 10 data points per second) so batching would be a good idea here. Process Through LSTM Layers: Each ground station's data goes through its respective LSTM layer. Concatenate Outputs: Combine the outputs from the LSTM layers. Classify with Dense Layer: The dense layer uses the combined outputs to classify data for each ground station. Looking forward to any insights or suggestions on this approach! submitted by /u/Imarami21 [link] [comments]

  • [R] Should I respond to reviewers after I got an Accept recommendation for an ICML workshop?
    by /u/howtorewriteaname (Machine Learning) on June 20, 2024 at 11:42 am

    I've got three reviews and an area-chair meta-review recommending an acceptance to an ICLR workshop. The paper will also be published in PMLR. I'm wondering whether I should discuss with the reviewers in OpenReview. I've done it for other conferences since there was a "rebuttal period", but there's no such thing for this submission. Therefore it feels like the discussion part is not necessary, particularly after it's been accepted already by the area chair. However I think it's of course good to address their questions. Should I spend time on this? submitted by /u/howtorewriteaname [link] [comments]

  • [D] Need help in effective strategies for handling imbalanced datasets in machine learning?
    by /u/llumo-ai (Machine Learning) on June 20, 2024 at 11:26 am

    Hey all! I'm working on a machine learning project and struggling with imbalanced datasets. Besides the usual resampling techniques, what are some effective methods you've used to handle this issue? Any algorithm-level approaches or recent research insights would be super helpful. Thanks in advance! submitted by /u/llumo-ai [link] [comments]

  • Datasets for image generation [D]
    by /u/Agreeable_Release549 (Machine Learning) on June 20, 2024 at 11:04 am

    Hi, does anyone here have experience in what datasets you can use / you can’t use while training ML models? I mean 20k+ photos datasets for AI image generation / diffusion models. I’m based in EU. submitted by /u/Agreeable_Release549 [link] [comments]

  • [Project] Time series regression problem
    by /u/Realistic_Decision99 (Machine Learning) on June 20, 2024 at 8:18 am

    I have attached 2 pictures that show the issue I'm facing. I basically have a time series regression task, and instead of using the actual time series values as the target variable, I thought it would be a better idea to use the month-on-month percentage of change. When I use the detrended target variable, the linear model is naive and it's just bringing forward the previous month's observation. On the other hand, the model without the detrended target variable performs very differently. Why is this happening? (Legend: orange line are the observations, blue line are predictions) Predictions with detrending Predictions without detrending submitted by /u/Realistic_Decision99 [link] [comments]

  • [P] New Chat Model with 128K Context Window
    by /u/trj_flash75 (Machine Learning) on June 20, 2024 at 8:16 am

    I just found out about Buddhi, a new open-source chat model from AI Planet with a 128K long context window! It’s built on Mistral 7B Instruct and uses the YaRN (Yet Another Rope Extension) Technique to handle up to 128,000 tokens. It's got impressive benchmarks and is listed on the HF LLM leaderboard. You can try it out on Google Colab here (needs Colab Pro): Google Colab Link More details on the Hugging Face Model Card: Hugging Face Model Card Definitely worth checking out if you're into LLMs or RAG models. Let me know what you think! submitted by /u/trj_flash75 [link] [comments]

  • [D] Problems of using Time series forecasting model in real life.
    by /u/gorg278 (Machine Learning) on June 20, 2024 at 8:00 am

    Hi everyone, I have questions regarding the application of time series forecasting model in real life problem. Let's say I trained a model with the current dataset in which the target variable prediction needs other predictor variables to be accurate. The problem raised when I tried to predict the target outside the time of the dataset when the predictor variables have no data. I was told that I need to also build models to predict those predictors but what if each of them also need predictors and each would need different type of model to get the good result? Furthermore, as the time passed, I need to trained the new model again thus the list of predictors variable might be changed. Unless I did something wrong, can I ask how companies that implements time series forecasting deal with this kind of problem? Thank you so much beforehand. p.s. : It's weird that I hardly find anyone encountered this kind of problem. If anyone can link me posts or blogs for further research, it would be greatly appreciated. submitted by /u/gorg278 [link] [comments]

  • [D][R] Synthetic data benchmark
    by /u/goncalomribeiro (Machine Learning) on June 20, 2024 at 2:17 am

    Came across this benchmark about synthetic data vendors, focused on multi-table (databases) synthesis: https://mltechniques.com/2024/06/15/synthesizing-multi-table-databases-model-evaluation-vendor-comparison/ Does anyone have used one of these vendors already? What are your thoughts? submitted by /u/goncalomribeiro [link] [comments]

  • [P] Llama 3 Language Model Implementation from Scratch(one file)
    by /u/atronos_kronios (Machine Learning) on June 20, 2024 at 12:26 am

    Hey everyone! I'm excited to share my latest project - a from-scratch implementation of the Llama 3 language model! Inspired by the brilliant works of AAAAAAAAAA.org and Andreij Karpathy, I aimed to recreate the Llama3 model in a clear and modular format. 🔗 GitHub Repository: Llama 3 from Scratch This project has been a fantastic learning experience for me, and I hope it helps others in the community who are passionate about AI and machine learning. Check it out, give it a star ⭐, and feel free to contribute or provide feedback! Let's build and learn together submitted by /u/atronos_kronios [link] [comments]

  • Text Classification with Multiple Classes (Categories) [P]
    by /u/DonThe_Bomb (Machine Learning) on June 19, 2024 at 9:54 pm

    Hi all, I'm working on a project to classify textual data in the form of sentences and paragraphs, into a predefined set of categories. I'm working with about 100+ unique categories, which goes far beyond the typical binary classification examples I've encountered online and the multi-classification examples I've seen (3 or 4 categories at most). Specifically, I'm working with help desk tickets and attempting to classify them into 1 of the 100+ categories available At the moment, I'm using SVMs with OvR to carry this out with varying levels of success and I was hoping someone might be able to share alternative methods for carrying out this task? My knowledge of classification algorithms is fairly limited but I have some past experience working with clustering algorithm such as k-nearest neighbours, but I don't think it's practical for text classification in the case but I could be wrong? Thanks submitted by /u/DonThe_Bomb [link] [comments]

  • [P] [D] Automatic Image Cropping/Selection/Processing for the Lazy
    by /u/PsyBeatz (Machine Learning) on June 19, 2024 at 9:33 pm

    Hey guys, So recently I was working on a few LoRA's(Image based models) and I found it very time consuming to install multiple dependencies, half of them clashing with one another, multiple venv handling, etc. For editing captions, that led me to image processing and using birme, which was down at that time, and I needed a solution, making me resort to other websites. And then caption editing took too long to do manually; So, I did what any dev would do: Made my own local script. PS: I do know automatic1111 and kohya_ss gui have support for a few of these functionalities, but not all. PPS: Use any captioning system that you like, I use Automatic1111's batch process captioning. Link to Repo (StableDiffusionHelper) Image Functionalities: Converting all Images to PNG Removal of Same Images Checks Image for Suitability (by checking for image:face ratio, blurriness, sharpness, if there are any faces at all to begin with) Removing Black Bars from images Background removal (rudimentary, using rembg, need to train a model on my own and see how it works) Cropping Image to Face Makes sure the square box is the biggest that can fit on the screen, and then resizes it down to any size you want Caption Functionalities: Easier to handle caption files without manually sifting through Danbooru tag helper Displays most common words used Select any words that you want to delete from the caption files Add your uniqueWord (character name to the start, etc) Removes any extra commas and blank spaces It's all in a single .ipynb file, with its imports given in the repo. Run the .bat file included !! PS: You might have to go in hand-picking-ly remove any images that you don't want, that's something that idts can be optimized for your own taste for making the LoRA's Please let me know any feedback that you have, or any other functionalities you want implemented, Thank you for reading ~ submitted by /u/PsyBeatz [link] [comments]

  • [Discussion] Cheaper setup to run the upcoming 400B models?
    by /u/t4kuy4x (Machine Learning) on June 19, 2024 at 9:30 pm

    I am looking for the “cheapest” option to run one of the upcoming 400B models locally. Any ideas? I guess you would need ideally like 700GB VRAM? That would require like 8 x H100, but that is like crazy expensive. I can buy a house with that 🤣. Some options I was thinking for was: One of the AMD EPYC cpus with 1TB RAM Probably the upcoming Mac Studio M4 Ultra that will likely have 256 unified memory. Not enough but maybe with some quantizated model. ?? Ideally want to keep it under 25k. Any ideas? It seems that’s why AI labs are raising billions, as these GPUs are crazy expensive. submitted by /u/t4kuy4x [link] [comments]

  • [R] AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodology
    by /u/FSoft_AIC (Machine Learning) on June 19, 2024 at 9:16 pm

    AgileCoder is new SOTA multi-agent framework for software development that draws inspiration from the widely-used Agile Methodology in professional software engineering. The key innovation lies in its task-oriented approach, where instead of assigning fixed roles to agents, AgileCoder mimics real-world software development by creating a backlog of tasks and dividing the development process into sprints, with the backlog being dynamically updated at each sprint. The evaluation using HumanEval, MBPP, and our manually curated datasets on complex software requirements (named ProjectDev) to produce complete software demonstrates that we outperform ChatDev and MetaGPT. Paper: https://arxiv.org/abs/2406.11912 Code: https://github.com/FSoft-AI4Code/AgileCoder submitted by /u/FSoft_AIC [link] [comments]

  • [D] What does it mean to understand? (Chinese room rethinking)
    by /u/somethingsomthang (Machine Learning) on June 19, 2024 at 9:01 pm

    I was thinking what would happen if a person was taught like an llm. Image learning Chinese only through Chinese text with no translations to English to keep it separated from all previous knowledge. And in that way simulate learning from scratch. If learning was done this way then even if i learn how to respond and write Chinese in a way that seems like i understand, I wouldn't actually have any idea on what is being written. I'd understand the Chinese text, but not the reality it represents. I can't think of any way i could actually understand how anything i could then write in Chinese relates to the real world since a connection was never made to bridge the self contained Chinese knowledge. So i would think that without anything grounding an ai system in reality it's going to be separated from it and in turn be away from what we'd normally call understanding. If the gap here was bridged for example with translations of Chinese and English then in this situation i could then connect it to reality and understand, or if the Chinese was connected to reality more directly with more context than just text. I think understanding Could be described as the ability to predict. So an llm trained on text does have the ability to understand text, But it's understanding doesn't extend to reality, only the ungrounded abstraction of the text. That is to say i think as we are getting system better capable multimodality, text, audio, image, video, 3d or whatever it'll be. That if they are all connected and relating to each other we might have something we can say truly understands like us by being connected to reality. But what do you guys think? submitted by /u/somethingsomthang [link] [comments]

  • [N] Ilya Sutskever and friends launch Safe Superintelligence Inc.
    by /u/we_are_mammals (Machine Learning) on June 19, 2024 at 7:29 pm

    With offices in Palo Alto and Tel Aviv, the company will be concerned with just building ASI. No product cycles. https://ssi.inc submitted by /u/we_are_mammals [link] [comments]

  • [D] Monitoring and Debugging RAG Systems in Production
    by /u/Jman7762 (Machine Learning) on June 19, 2024 at 5:08 pm

    Hi! I’m part of a team from MIT, where we specialize in developing advanced tools for data visualizations of the latent space. We are currently exploring how visualizations can help increase the effectiveness of RAG monitoring systems and would love to gather insights from how people manage RAGs currently. We know there are existing monitoring tools like Ragas, Arize(Phoenix), LangSmith. We are curious on how Frequency you are looking at monitoring data What does the end-user application your RAG support look like? We believe that a visualization tool could greatly enhance the ability to monitor and debug RAG systems in real-time by: Providing intuitive, graphical representations of system performance and behavior. Highlighting potential issues and bottlenecks at a glance. If you’re willing to share more detailed insights through an interview, please let us know! Happy to get connected and learn more! submitted by /u/Jman7762 [link] [comments]

  • [P] [D] Updated On: Hi I'm a senior machine learning engineer, looking for for buddies to build cool stuff with!
    by /u/Rude-Eye3588 (Machine Learning) on June 19, 2024 at 3:59 pm

    Old Post: https://www.reddit.com/r/MachineLearning/comments/1dj8pg6/p_d_hi_im_a_senior_machine_learning_engineer/ Wow, I wasn't expecting this post to gain so much attention! I've created a Google form for those who are serious about working on projects, Kaggle competitions, or Leetcode challenges. Please take a moment to fill it out. Form: https://forms.gle/k3jzCfNJy3rgz4ec6 Here's how it will work: I will create groups based on your goals and expertise. Each group will have a team leader to assist with progress, alongside my support. It will take some time to organize everyone into teams, so please be patient. I'll reach out to you soon. Thank you! submitted by /u/Rude-Eye3588 [link] [comments]

  • Maximize your Amazon Translate architecture using strategic caching layers
    by Praneeth Reddy Tekula (AWS Machine Learning Blog) on June 19, 2024 at 3:56 pm

    In this post, we explain how setting up a cache for frequently accessed translations can benefit organizations that need scalable, multi-language translation across large volumes of content. You’ll learn how to build a simple caching mechanism for Amazon Translate to accelerate turnaround times.

  • Deploy a Slack gateway for Amazon Bedrock
    by Rushabh Lokhande (AWS Machine Learning Blog) on June 19, 2024 at 2:06 pm

    In today’s fast-paced digital world, streamlining workflows and boosting productivity are paramount. That’s why we’re thrilled to share an exciting integration that will take your team’s collaboration to new heights. Get ready to unlock the power of generative artificial intelligence (AI) and bring it directly into your Slack workspace. Imagine the possibilities: Quick and efficient

  • [P] [D] Video lecture summarization with text+screenshots
    by /u/chilled_87 (Machine Learning) on June 19, 2024 at 1:51 pm

    I created this app vi-su.app that does just that. I post it here since I am in the ML field, and actually designed this app to help me to preview/remember video tutorials/courses on ML (as there are too many I wish to watch). It relies on a vision-language model to do the job, so inaccuracies can happen, but it usually does a good job. Latest example was yesterday with the 7th lecture of the intro to deep learning from MIT - and the summary there https://vi-su.app/P7Hkh2zOGQ0/summary.html gives in my opinion a good account of the lecture. See other examples in the search tab. Let me know if you find this useful or have suggestions. Depending on interest, I could also open-source it . submitted by /u/chilled_87 [link] [comments]

  • [P] [D] Hi I'm a senior machine learning engineer, looking for for buddies to build cool stuff with!
    by /u/Rude-Eye3588 (Machine Learning) on June 19, 2024 at 2:45 am

    Hi, I'm a senior machine learning engineer, looking for buddies to build cool stuff with! I'm looking to explore and experiment with fellow passionate engineers. We can do Kaggle projects, LeetCode, or just interview brainstorming. Reach out if anyone would like to ideate and see what cool things we can create together. UPDATE: Thank you for the overwhelming response! I've received over 100 responses, and I appreciate your interest and willingness to contribute. To ensure that I can effectively manage all the responses and filter potential serious candidates, I'll be creating a Google Form soon. Form: https://forms.gle/k3jzCfNJy3rgz4ec6 Please bear with me as I work on setting this up. In the meantime, if you have any ideas or suggestions, please share them. submitted by /u/Rude-Eye3588 [link] [comments]

  • Improving air quality with generative AI
    by Sandra Topic (AWS Machine Learning Blog) on June 18, 2024 at 5:23 pm

    This post presents a solution that uses a generative artificial intelligence (AI) to standardize air quality data from low-cost sensors in Africa, specifically addressing the air quality data integration problem of low-cost sensors. The solution harnesses the capabilities of generative AI, specifically Large Language Models (LLMs), to address the challenges posed by diverse sensor data and automatically generate Python functions based on various data formats. The fundamental objective is to build a manufacturer-agnostic database, leveraging generative AI’s ability to standardize sensor outputs, synchronize data, and facilitate precise corrections.

  • Use zero-shot large language models on Amazon Bedrock for custom named entity recognition
    by Sujitha Martin (AWS Machine Learning Blog) on June 18, 2024 at 5:15 pm

    Name entity recognition (NER) is the process of extracting information of interest, called entities, from structured or unstructured text. Manually identifying all mentions of specific types of information in documents is extremely time-consuming and labor-intensive. Some examples include extracting players and positions in an NFL game summary, products mentioned in an AWS keynote transcript, or

  • Safeguard a generative AI travel agent with prompt engineering and Guardrails for Amazon Bedrock
    by Antonio Rodriguez (AWS Machine Learning Blog) on June 18, 2024 at 5:13 pm

    In this post, we explore a comprehensive solution for addressing the challenges of securing a virtual travel agent powered by generative AI. We provide an end-to-end example and its accompanying code to demonstrate how to implement prompt engineering techniques, content moderation, and various guardrails to make sure the assistant operates within predefined boundaries by relying on Guardrails for Amazon Bedrock. Additionally, we delve into monitoring strategies to track the activation of these safeguards, enabling proactive identification and mitigation of potential issues.

  • Streamline financial workflows with generative AI for email automation
    by Hariharan Nammalvar (AWS Machine Learning Blog) on June 18, 2024 at 5:04 pm

    This post explains a generative artificial intelligence (AI) technique to extract insights from business emails and attachments. It examines how AI can optimize financial workflow processes by automatically summarizing documents, extracting data, and categorizing information from email attachments. This enables companies to serve more clients, direct employees to higher-value tasks, speed up processes, lower expenses, enhance data accuracy, and increase efficiency.

  • How Twilio used Amazon SageMaker MLOps pipelines with PrestoDB to enable frequent model retraining and optimized batch transform
    by Madhur Prashant (AWS Machine Learning Blog) on June 17, 2024 at 5:09 pm

    This post is co-written with Shamik Ray, Srivyshnav K S, Jagmohan Dhiman and Soumya Kundu from Twilio. Today’s leading companies trust Twilio’s Customer Engagement Platform (CEP) to build direct, personalized relationships with their customers everywhere in the world. Twilio enables companies to use communications and data to add intelligence and security to every step of

  • Accelerate deep learning training and simplify orchestration with AWS Trainium and AWS Batch
    by Scott Perry (AWS Machine Learning Blog) on June 17, 2024 at 4:50 pm

    In large language model (LLM) training, effective orchestration and compute resource management poses a significant challenge. Automation of resource provisioning, scaling, and workflow management is vital for optimizing resource usage and streamlining complex workflows, thereby achieving efficient deep learning training processes. Simplified orchestration enables researchers and practitioners to focus more on model experimentation, hyperparameter tuning,

  • [D] Simple Questions Thread
    by /u/AutoModerator (Machine Learning) on June 16, 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]

  • Build a custom UI for Amazon Q Business
    by Ennio Pastore (AWS Machine Learning Blog) on June 12, 2024 at 4:44 pm

    Enable branded user experiences with specialized features like feedback handling and seamless conversation flows personalized for your use case and business needs.

  • Scalable intelligent document processing using Amazon Bedrock
    by Venkata Kampana (AWS Machine Learning Blog) on June 12, 2024 at 4:32 pm

    In today’s data-driven business landscape, the ability to efficiently extract and process information from a wide range of documents is crucial for informed decision-making and maintaining a competitive edge. However, traditional document processing workflows often involve complex and time-consuming manual tasks, hindering productivity and scalability. In this post, we discuss an approach that uses the

  • Use weather data to improve forecasts with Amazon SageMaker Canvas
    by Charles Laughlin (AWS Machine Learning Blog) on June 12, 2024 at 3:53 pm

    Photo by Zbynek Burival on Unsplash Time series forecasting is a specific machine learning (ML) discipline that enables organizations to make informed planning decisions. The main idea is to supply historic data to an ML algorithm that can identify patterns from the past and then use those patterns to estimate likely values about unseen periods

  • Reimagining software development with the Amazon Q Developer Agent
    by Christian Bock (AWS Machine Learning Blog) on June 11, 2024 at 4:52 pm

    Amazon Q Developer uses generative artificial intelligence (AI) to deliver state-of-the-art accuracy for all developers, taking first place on the leaderboard for SWE-bench, a dataset that tests a system’s ability to automatically resolve GitHub issues. This post describes how to get started with the software development agent, gives an overview of how the agent works, and discusses its performance on public benchmarks. We also delve into the process of getting started with the Amazon Q Developer Agent and give an overview of the underlying mechanisms that make it a state-of-the-art feature development agent.

  • Get started quickly with AWS Trainium and AWS Inferentia using AWS Neuron DLAMI and AWS Neuron DLC
    by Niithiyn Vijeaswaran (AWS Machine Learning Blog) on June 11, 2024 at 2:47 pm

    Starting with the AWS Neuron 2.18 release, you can now launch Neuron DLAMIs (AWS Deep Learning AMIs) and Neuron DLCs (AWS Deep Learning Containers) with the latest released Neuron packages on the same day as the Neuron SDK release. When a Neuron SDK is released, you’ll now be notified of the support for Neuron DLAMIs

  • Sprinklr improves performance by 20% and reduces cost by 25% for machine learning inference on AWS Graviton3
    by Sunita Nadampalli (AWS Machine Learning Blog) on June 11, 2024 at 2:44 pm

    This is a guest post co-written with Ratnesh Jamidar and Vinayak Trivedi from Sprinklr. Sprinklr’s mission is to unify silos, technology, and teams across large, complex companies. To achieve this, we provide four product suites, Sprinklr Service, Sprinklr Insights, Sprinklr Marketing, and Sprinklr Social, as well as several self-serve offerings. Each of these products are

  • How Wiz is empowering organizations to remediate security risks faster with Amazon Bedrock
    by Shaked Rotlev (AWS Machine Learning Blog) on June 11, 2024 at 2:36 pm

    Wiz is a cloud security platform that enables organizations to secure everything they build and run in the cloud by rapidly identifying and removing critical risks. Over 40% of the Fortune 100 trust Wiz’s purpose-built cloud security platform to gain full-stack visibility, accurate risk prioritization, and enhanced business agility. Organizations can connect Wiz in minutes

  • Code generation using Code Llama 70B and Mixtral 8x7B on Amazon SageMaker
    by Shikhar Kwatra (AWS Machine Learning Blog) on June 10, 2024 at 2:16 pm

    In the ever-evolving landscape of machine learning and artificial intelligence (AI), large language models (LLMs) have emerged as powerful tools for a wide range of natural language processing (NLP) tasks, including code generation. Among these cutting-edge models, Code Llama 70B stands out as a true heavyweight, boasting an impressive 70 billion parameters. Developed by Meta

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

Download AWS machine Learning Specialty Exam Prep App on iOs

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AWS Data analytics DAS-C01 Exam Preparation

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