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] Please recommend recent ML talk or interview on YouTube
    by /u/20231027 (Machine Learning) on April 22, 2024 at 11:00 am

    These recent talks were very illuminating Andre conversation at the Sequoia -https://youtu.be/c3b-JASoPi0?si=0R9LFUokb2_GPLlY Kaiming tracing history of Computer vision networks - https://youtu.be/D_jt-xO_RmI?si=uaAtrCWeIXwHBiQ2 Do you have other recommendations? submitted by /u/20231027 [link] [comments]

  • [P] negative sampling from small negative observations for recommendation system
    by /u/No_Carpenter_9469 (Machine Learning) on April 22, 2024 at 10:28 am

    I am working on a recommendation system on a user item interaction matrix based on implicit feedback (binary), and I have positive observed interactions and a very small amount of negative observed interactions. For both user and item there are vector features available as embeddings. Are there any methods that I can perform negative sampling through the negative observations? I have heard of methods like contrastive learning but not aware of ways to integrate existing negative observations. submitted by /u/No_Carpenter_9469 [link] [comments]

  • [R] Recurrent Memory has broken the limits of Context Length for Transformer Neural Networks
    by /u/AIRI_Institute (Machine Learning) on April 22, 2024 at 10:08 am

    The researchers segmented the sequence and added special memory tokens to the input: memory states from the output of the previous segment became inputs for the next one. Thus, a whole transformer acts as a recurrent cell, and memory serves as the recurrent state of the network. This approach was called Recurrent Memory Transformer (RMT). The authors augmented small transformer models like BERT and GPT-2 with this memory and tested them on various question-answering tasks where facts needed for answering are somewhere in the text. It was found that using recurrent memory significantly increases the length of the input sequence while maintaining satisfactory neural network performance accuracy. In their experiments, scientists were able to extend this value to 2 million tokens. According to the authors, there are no fundamental limitations for this value to increase further, as the computational complexity of RMT grows linearly with the number of tokens. The accuracy of the pre-trained BERT model augmented with RMT on three tasks vs the number of tokens in the input sequence. The gray numbers indicate the GPU memory consumption, and the vertical lines represent the length limits in SOTA models (as of the end of 2023) The research was published in the proceedings of the AAAI-24 conference, additional details are provided in the preprint, and the code is available on GitHub. submitted by /u/AIRI_Institute [link] [comments]

  • [D] Copy Mechanism in transformers, help!!
    by /u/SnooOnions9136 (Machine Learning) on April 22, 2024 at 8:59 am

    Hi everyone, I was reading this paper on in-context learning: https://arxiv.org/abs/2212.07677 . In section 4 it refers to this “copy mechanism” but I’m struggling to understand what it actually does… My question is unrelated to the specifics of the paper, I’d like to know what is in general the copy mechanism !!! Can someone help please? :))))) submitted by /u/SnooOnions9136 [link] [comments]

  • [R] TriForce: Lossless Acceleration of Long Sequence Generation with Hierarchical Speculative Decoding
    by /u/SeawaterFlows (Machine Learning) on April 22, 2024 at 8:46 am

    Paper: https://arxiv.org/abs/2404.11912 Code: https://github.com/Infini-AI-Lab/TriForce Project page: https://infini-ai-lab.github.io/TriForce/ Abstract: With large language models (LLMs) widely deployed in long content generation recently, there has emerged an increasing demand for efficient long-sequence inference support. However, key-value (KV) cache, which is stored to avoid re-computation, has emerged as a critical bottleneck by growing linearly in size with the sequence length. Due to the auto-regressive nature of LLMs, the entire KV cache will be loaded for every generated token, resulting in low utilization of computational cores and high latency. While various compression methods for KV cache have been proposed to alleviate this issue, they suffer from degradation in generation quality. We introduce TriForce, a hierarchical speculative decoding system that is scalable to long sequence generation. This approach leverages the original model weights and dynamic sparse KV cache via retrieval as a draft model, which serves as an intermediate layer in the hierarchy and is further speculated by a smaller model to reduce its drafting latency. TriForce not only facilitates impressive speedups for Llama2-7B-128K, achieving up to 2.31× on an A100 GPU but also showcases scalability in handling even longer contexts. For the offloading setting on two RTX 4090 GPUs, TriForce achieves 0.108s/token—only half as slow as the auto-regressive baseline on an A100, which attains 7.78× on our optimized offloading system. Additionally, TriForce performs 4.86× than DeepSpeed-Zero-Inference on a single RTX 4090 GPU. TriForce's robustness is highlighted by its consistently outstanding performance across various temperatures. The code is available at this https URL. submitted by /u/SeawaterFlows [link] [comments]

  • [R] Many-Shot In-Context Learning
    by /u/SeawaterFlows (Machine Learning) on April 22, 2024 at 8:31 am

    Paper: https://arxiv.org/abs/2404.11018 Abstract: Large language models (LLMs) excel at few-shot in-context learning (ICL) -- learning from a few examples provided in context at inference, without any weight updates. Newly expanded context windows allow us to investigate ICL with hundreds or thousands of examples -- the many-shot regime. Going from few-shot to many-shot, we observe significant performance gains across a wide variety of generative and discriminative tasks. While promising, many-shot ICL can be bottlenecked by the available amount of human-generated examples. To mitigate this limitation, we explore two new settings: Reinforced and Unsupervised ICL. Reinforced ICL uses model-generated chain-of-thought rationales in place of human examples. Unsupervised ICL removes rationales from the prompt altogether, and prompts the model only with domain-specific questions. We find that both Reinforced and Unsupervised ICL can be quite effective in the many-shot regime, particularly on complex reasoning tasks. Finally, we demonstrate that, unlike few-shot learning, many-shot learning is effective at overriding pretraining biases and can learn high-dimensional functions with numerical inputs. Our analysis also reveals the limitations of next-token prediction loss as an indicator of downstream ICL performance. submitted by /u/SeawaterFlows [link] [comments]

  • [P] Zero shot logo detection
    by /u/CommercialFragrant (Machine Learning) on April 22, 2024 at 5:49 am

    I'm trying to create a web app that recognizes logos of brands in images. I've tried using Microsoft Azure Computer Vision/Custom Vision API but the results are not satisfactory. I have read about Yolo and Yolo world. If you've ever used them in your projects, can you help me see if zero shot logo detection can be achieved by this? submitted by /u/CommercialFragrant [link] [comments]

  • [Discussion]What is the reality for someone with extensive SWE experience who is trying to crack into ML engineering or Data engineering by doing personal projects and creating a portfolio. Is that even a realistic goal?
    by /u/Emergency-Director53 (Machine Learning) on April 22, 2024 at 5:11 am

    Looking for brutally honest opinions. Is the reality different for data engineers as I find the supply demand makes DE attractive currently ? submitted by /u/Emergency-Director53 [link] [comments]

  • [D] Looking for research on Transformers applied to niche tasks, not language. (ex. AlphaGeometry)
    by /u/RedditLovingSun (Machine Learning) on April 22, 2024 at 4:31 am

    I know there's been some research from google on using the transformer architecture for things like Geometry and Chess. Thinking of transformers as general algorithm learners interests me in learning about what other things they can be applied to and examining how they perform. Could the architecture learn to solve, for example, mazes? If it did would it's methods resemble A* or instead some other unknown algorithm? Can it converge on 'simulating' the most efficient algorithm for a given task or will it get stuck on inefficient methods (and if it did is that an architectural limitation)?. What roles do datasets have on achieving OOD generalization for tasks like this? Looking for niche and creative applications of transformers to do some more digging into these questions. Lemme know if you know of any good papers! (side note: an side interesting project may be to build a vector db of arvix paper abstracts so one could search for questions like this semantically). submitted by /u/RedditLovingSun [link] [comments]

  • [D] [R] AI logo generator Looka’s ML model
    by /u/Vishesh9096 (Machine Learning) on April 22, 2024 at 4:28 am

    I came across this AI logo generator website Looka. Does anyone have an idea of how does it actually work? What ML models are used to generate logos so fast or are there premade templates ? I also trued stable diffusion for generating logos, but it takes time and also dosent generates logo that good. submitted by /u/Vishesh9096 [link] [comments]

  • [D] Direct Preference Policy (DPO) - SFT dataset
    by /u/nohodlnodough (Machine Learning) on April 22, 2024 at 4:17 am

    In the dpo paper, the authors recommended to do SFT prior to doing DPO to prevent distribution shift and also demonstrated the discrepancy in performance for non-SFT and SFT in the new paper: https://arxiv.org/pdf/2404.12358.pdf However, i am slightly unsure about whats the rule for curating the preference dataset using a SFT-ed model. Does it mean that before doing DPO, the ref model HAS to be SFTed on the same prompts (x) of the preference dataset/similar distribution dataset? OR the preference dataset has to be curated from the ref model? The latter would mean that you could do SFT on any dataset so long as the pref dataset is curated using the SFT-ed model and not using any available pref dataset you find online, which most likely is curated using some unknown policy. While the former is saying that the ref policy has to be SFTed on the same distribution of the pref dataset (ie similar prompt types), meaning this is just an additional SFT step on the pref dataset's chosen response as compared to the previous case. What are your thoughts on this? submitted by /u/nohodlnodough [link] [comments]

  • [D] Is the AI Workforce or Companies More Distributed Than Those in Other Tech Sectors?
    by /u/digital-bolkonsky (Machine Learning) on April 22, 2024 at 2:36 am

    submitted by /u/digital-bolkonsky [link] [comments]

  • [D] Recommendation for a language modeling dataset that breaks down into a large number of sub-domains
    by /u/alpthn (Machine Learning) on April 21, 2024 at 11:40 pm

    I could've sworn I've come across a paper that proposed such a dataset, but I can't seem to find it. They basically assemble a large number of small (relative to training data) text documents, each representative of some domain .e.g., social media, academic papers, etc. The purpose is to quickly compare LMs (using the same tokenization) by measuring their perplexity on these domains. The closest thing I've found is the Pile which breaks down to 21 domains, but i'd really like to re-find this dataset. Thanks in advance! submitted by /u/alpthn [link] [comments]

  • [D] Why isn't GNN in high demand in industry?
    by /u/Snoo_72181 (Machine Learning) on April 21, 2024 at 11:04 pm

    Almost no job posting for Data Scientist or ML Engineer needs GNNs. Is it because it's computationally expensive - both time and space? Or is it because preprocessing data to a graph format is not always intuitive? Or GNN awareness is still low outside of academia? submitted by /u/Snoo_72181 [link] [comments]

  • [D] Anomaly detection for seasonal data using interquartile range (IQR)
    by /u/spaceape__ (Machine Learning) on April 21, 2024 at 9:46 pm

    hi! I need to create an alerting bot for anomaly on data regarding funnel monitoring of different product on my company website. The specific metrics I need to monitor are: The conversion rate between users who visit our website and those who start the funnel. The conversion rate between users who start the funnel and those who reach the end (Thank you page). After a bit of experimenting with complex models (using pyOD, adtk and darts Python library mostly), I found out that the simpler the better and I'm using the Interquartile Range (IQR). Each day, for each metrics, I will send an alert if the current value is lower or greater than [Q1−1.5IQR, Q3+1.5IQR] , where IQR is calculated on the data of previous month. The only drawback is that my data has a weekly seasonality (visits peaks on friday and decrease on weekend). Do you have any suggestions on how take in account the seasonality and still using IQR? Thank you! submitted by /u/spaceape__ [link] [comments]

  • Best AudioBooks?[D]
    by /u/ResidentMaize2535 (Machine Learning) on April 21, 2024 at 6:38 pm

    Best up to date and current books to learn machine learning and AI from a technical perspective? I work in tech but would like to further my understanding. I have a general understanding of the concept. I do a lot of driving so this is a passive listen. submitted by /u/ResidentMaize2535 [link] [comments]

  • [Research] A visual deep dive into Tesla’s data engine as pioneered by Andrej Karpathy.
    by /u/ml_a_day (Machine Learning) on April 21, 2024 at 6:18 pm

    TL;DR: Tesla uses lightweight "trigger classifiers" to detect rare scenarios when their ML model underperforms. Relevant data is uploaded to a server to improve the model, which is then trained again to cover different failure modes. How Tesla Continuously and Automatically Improves Autopilot and Full Self-Driving Capability On 5M+ Cars. A visual guide: How Tesla sets up their iterative ML pipeline P.S.: I spent several hours researching and preparing a visual deep dive of Tesla’s data engine as pioneered by Andrej Karpathy. The post lays out the iterative recipe of how Tesla improves it's fully self-driving and Autopilot capabilities. https://preview.redd.it/qxmjeavmjvvc1.jpg?width=1456&format=pjpg&auto=webp&s=94cb35f71f7e57b6bcc6e0bf9f1d5f05b5c7f086 https://preview.redd.it/htz4p8vmjvvc1.jpg?width=1456&format=pjpg&auto=webp&s=a722604b59d2c6fbb8f7e605ad496bede05a238e submitted by /u/ml_a_day [link] [comments]

  • [N] All PyData 2023 talks grouped by location and ordered by the view count
    by /u/TechTalksWeekly (Machine Learning) on April 21, 2024 at 5:17 pm

    submitted by /u/TechTalksWeekly [link] [comments]

  • [D] Simple Questions Thread
    by /u/AutoModerator (Machine Learning) on April 21, 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]

  • [D] Using clean architecture and DDD in machine learning | deep learning projects
    by /u/Illustrious-Class-65 (Machine Learning) on April 21, 2024 at 2:51 pm

    Basically, the title. Has anyone used principles of clean architecture and DDD to build a deep learning or machine learning solution? If so, how do you code domains? Do you introduce frameworks like PyTorch right into the domain and application layers? Or do you try to write abstractions for training processes, and implement these abstractions on the third | fourth layer? submitted by /u/Illustrious-Class-65 [link] [comments]

  • [P] Okkam - find polynomials that fit arbitrary datasets using GA
    by /u/topcodemangler (Machine Learning) on April 21, 2024 at 2:36 pm

    This might be a bit old-school compared to the current NN meta but if anyone is interested I've cooked up a tool for finding polynomials with configurable parameters (number of terms, exponent bits) for arbitrary data in CSV. It uses a configurable tournament-based GA algorithm to do it and offers an UI to see how it is going. It is written in Rust and relatively fast - tries to utilize all the available cores to the maximum so scales very well. Would be great to hear some feedback or suggestion and if you like what you're seeing please leave a star on the repo 🙂 The repo: Github submitted by /u/topcodemangler [link] [comments]

  • What’s the current state of recommendation algorithms/systems in machine learning research? [D]
    by /u/Direct-Touch469 (Machine Learning) on April 21, 2024 at 1:20 pm

    When I first started my ML journey about 4 years ago the most basic intro level recommendation algorithms I learned were about collaborative filtering and content based filtering. I want to know more about what the current state of recommender systems is. How has it changed? What methods are people trying to include in search of better recommendations? Has there been any mention of including causality in recommendation systems? This latter seems like the most “up to date” advancement, but I have yet to find an overview. submitted by /u/Direct-Touch469 [link] [comments]

  • [D] How do you train on large amount of data?
    by /u/RiseWarm (Machine Learning) on April 21, 2024 at 6:07 am

    I have about 4M newspaper articles. I want to train word embedding, topic modeling on them. I got colab pro+ and their high-ram spec only has around 60GB RAM. The runtime just crushes when I try to train anything on those 4M articles. I can think that we will load the data batch by batch from hard disk and send them? I have really no experience here. I would love to hear your experience and suggestions. submitted by /u/RiseWarm [link] [comments]

  • [D] How does GPT understand what it does not know?
    by /u/No_Grapefruit_4686 (Machine Learning) on April 21, 2024 at 3:57 am

    https://preview.redd.it/kf5t2k95arvc1.png?width=625&format=png&auto=webp&s=5631d9b49084815d44c2178e1b27cfd4926f6f59 I was to look up Cramer distance and GPT spots that I made a typo before I realized it. How did GPT understand the input might be wrong rather than always assume the input is right? Is this also done in an end-to-end manner, or what are additional procedures if there are any to identify the possible uncertainty in the input? I guess in general I'm just curious that how does GPT know it may not have an answer? submitted by /u/No_Grapefruit_4686 [link] [comments]

  • [D] How important is leetcode in ML?
    by /u/Amgadoz (Machine Learning) on April 20, 2024 at 7:36 pm

    I recently interviewed with a faang for Applied Data Scientist and it went like this: - 1x ML interview - 3x Leetcode interviews - 1x high level system design interview How important is leetcode to the actual job of ML / DS practitioners? Is it that important to have 3 leetcode problems vs 1 ml problem? When I am doing interview prep I just feel like I am wasting time doing leetcode when I could be upskilling in other areas in ML or even other technical skills like K8s, cuda or data engineering. I am interested in knowing what everyone else thinks about this. submitted by /u/Amgadoz [link] [comments]

  • [D] Meta's H100 figure represents its H100 purchase as per company earnings call 1 Feb 2024. Excludes a further 250,000 H100 equivalents worth of GPU.
    by /u/ewelumokeke (Machine Learning) on April 20, 2024 at 7:32 pm

    submitted by /u/ewelumokeke [link] [comments]

  • Introducing automatic training for solutions in Amazon Personalize
    by Ba'Carri Johnson (AWS Machine Learning Blog) on April 20, 2024 at 12:38 am

    Amazon Personalize is excited to announce automatic training for solutions. Solution training is fundamental to maintain the effectiveness of a model and make sure recommendations align with users’ evolving behaviors and preferences. As data patterns and trends change over time, retraining the solution with the latest relevant data enables the model to learn and adapt,

  • Use Kubernetes Operators for new inference capabilities in Amazon SageMaker that reduce LLM deployment costs by 50% on average
    by Rajesh Ramchander (AWS Machine Learning Blog) on April 19, 2024 at 4:55 pm

    We are excited to announce a new version of the Amazon SageMaker Operators for Kubernetes using the AWS Controllers for Kubernetes (ACK). ACK is a framework for building Kubernetes custom controllers, where each controller communicates with an AWS service API. These controllers allow Kubernetes users to provision AWS resources like buckets, databases, or message queues

  • Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock – Part 2
    by Archana Inapudi (AWS Machine Learning Blog) on April 19, 2024 at 3:15 pm

    In Part 1 of this series, we presented a solution that used the Amazon Titan Multimodal Embeddings model to convert individual slides from a slide deck into embeddings. We stored the embeddings in a vector database and then used the Large Language-and-Vision Assistant (LLaVA 1.5-7b) model to generate text responses to user questions based on

  • Scale AI training and inference for drug discovery through Amazon EKS and Karpenter
    by Matthew Welborn (AWS Machine Learning Blog) on April 19, 2024 at 3:07 pm

    This is a guest post co-written with the leadership team of Iambic Therapeutics. Iambic Therapeutics is a drug discovery startup with a mission to create innovative AI-driven technologies to bring better medicines to cancer patients, faster. Our advanced generative and predictive artificial intelligence (AI) tools enable us to search the vast space of possible drug

  • Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock
    by Ebbey Thomas (AWS Machine Learning Blog) on April 18, 2024 at 5:57 pm

    As you navigate the complexities of cloud migration, the need for a structured, secure, and compliant environment is paramount. AWS Landing Zone addresses this need by offering a standardized approach to deploying AWS resources. This makes sure your cloud foundation is built according to AWS best practices from the start. With AWS Landing Zone, you eliminate the guesswork in security configurations, resource provisioning, and account management. It’s particularly beneficial for organizations looking to scale without compromising on governance or control, providing a clear path to a robust and efficient cloud setup. In this post, we show you how to generate customized, compliant IaC scripts for AWS Landing Zone using Amazon Bedrock.

  • Live Meeting Assistant with Amazon Transcribe, Amazon Bedrock, and Knowledge Bases for Amazon Bedrock
    by Bob Strahan (AWS Machine Learning Blog) on April 18, 2024 at 5:08 pm

    You’ve likely experienced the challenge of taking notes during a meeting while trying to pay attention to the conversation. You’ve probably also experienced the need to quickly fact-check something that’s been said, or look up information to answer a question that’s just been asked in the call. Or maybe you have a team member that always joins meetings late, and expects you to send them a quick summary over chat to catch them up. Then there are the times that others are talking in a language that’s not your first language, and you’d love to have a live translation of what people are saying to make sure you understand correctly. And after the call is over, you usually want to capture a summary for your records, or to send to the participants, with a list of all the action items, owners, and due dates. All of this, and more, is now possible with our newest sample solution, Live Meeting Assistant (LMA).

  • Meta Llama 3 models are now available in Amazon SageMaker JumpStart
    by Kyle Ulrich (AWS Machine Learning Blog) on April 18, 2024 at 4:31 pm

    Today, we are excited to announce that Meta Llama 3 foundation models are available through Amazon SageMaker JumpStart to deploy and run inference. The Llama 3 models are a collection of pre-trained and fine-tuned generative text models. In this post, we walk through how to discover and deploy Llama 3 models via SageMaker JumpStart. What is

  • Slack delivers native and secure generative AI powered by Amazon SageMaker JumpStart
    by Jackie Rocca (AWS Machine Learning Blog) on April 18, 2024 at 12:00 pm

    We are excited to announce that Slack, a Salesforce company, has collaborated with Amazon SageMaker JumpStart to power Slack AI’s initial search and summarization features and provide safeguards for Slack to use large language models (LLMs) more securely. Slack worked with SageMaker JumpStart to host industry-leading third-party LLMs so that data is not shared with the infrastructure owned by third party model providers. This keeps customer data in Slack at all times and upholds the same security practices and compliance standards that customers expect from Slack itself.

  • Uncover hidden connections in unstructured financial data with Amazon Bedrock and Amazon Neptune
    by Xan Huang (AWS Machine Learning Blog) on April 17, 2024 at 3:00 pm

    In asset management, portfolio managers need to closely monitor companies in their investment universe to identify risks and opportunities, and guide investment decisions. Tracking direct events like earnings reports or credit downgrades is straightforward—you can set up alerts to notify managers of news containing company names. However, detecting second and third-order impacts arising from events

  • Open source observability for AWS Inferentia nodes within Amazon EKS clusters
    by Riccardo Freschi (AWS Machine Learning Blog) on April 17, 2024 at 2:54 pm

    This post walks you through the Open Source Observability pattern for AWS Inferentia, which shows you how to monitor the performance of ML chips, used in an Amazon Elastic Kubernetes Service (Amazon EKS) cluster, with data plane nodes based on Amazon Elastic Compute Cloud (Amazon EC2) instances of type Inf1 and Inf2.

  • Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks
    by Pranav Murthy (AWS Machine Learning Blog) on April 16, 2024 at 11:00 pm

    Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them. They then use SQL to explore, analyze, visualize, and integrate

  • Distributed training and efficient scaling with the Amazon SageMaker Model Parallel and Data Parallel Libraries
    by Xinle Sheila Liu (AWS Machine Learning Blog) on April 16, 2024 at 4:18 pm

    In this post, we explore the performance benefits of Amazon SageMaker (including SMP and SMDDP), and how you can use the library to train large models efficiently on SageMaker. We demonstrate the performance of SageMaker with benchmarks on ml.p4d.24xlarge clusters up to 128 instances, and FSDP mixed precision with bfloat16 for the Llama 2 model.

  • Manage your Amazon Lex bot via AWS CloudFormation templates
    by Thomas Rindfuss (AWS Machine Learning Blog) on April 16, 2024 at 4:11 pm

    Amazon Lex is a fully managed artificial intelligence (AI) service with advanced natural language models to design, build, test, and deploy conversational interfaces in applications. It employs advanced deep learning technologies to understand user input, enabling developers to create chatbots, virtual assistants, and other applications that can interact with users in natural language. Managing your

  • A secure approach to generative AI with AWS
    by Anthony Liguori (AWS Machine Learning Blog) on April 16, 2024 at 4:00 pm

    Generative artificial intelligence (AI) is transforming the customer experience in industries across the globe. Customers are building generative AI applications using large language models (LLMs) and other foundation models (FMs), which enhance customer experiences, transform operations, improve employee productivity, and create new revenue channels. The biggest concern we hear from customers as they explore the advantages of generative AI is how to protect their highly sensitive data and investments. At AWS, our top priority is safeguarding the security and confidentiality of our customers' workloads. We think about security across the three layers of our generative AI stack ...

  • Cost-effective document classification using the Amazon Titan Multimodal Embeddings Model
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Download AWS machine Learning Specialty Exam Prep App on iOs

AWS machine learning certification prep
AWS machine learning certification prep

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

Download AWS machine Learning Specialty Exam Prep App on iOs

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

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