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] Fish Speech TTS: clone OpenAI TTS in 30 minutes
    by /u/lengyue233 (Machine Learning) on May 22, 2024 at 10:11 am

    While we are still figuring out ways to improve the agent's emotional response to OpenAI GPT-4o, we have already made significant progress in aligning OpenAI's TTS performance. To begin this experiment, we collected 10 hours of OpenAI TTS data to perform supervised fine-tuning (SFT) on both the LLM (medium) and VITS models, which took approximately 30 minutes. After that, we used 15 seconds of audio as a prompt during inference. Demos Available: here. As you can see, the model's emotion, rhythm, accent, and timbre match the OpenAI speakers, though there is some degradation in audio quality, which we are working on. To avoid any legal issues, we are unable to release the fine-tuned model, but I believe everyone can tune fish-speech to this level within hours and for around $20. Our experiment shows that with only 25 seconds of prompts (few-shot learning), without any fine-tuning, the model can mimic most behaviors except details like timbre and how it reads numbers. To the best of our knowledge, you can clone how someone speaks in English, Chinese, and Japanese with 30 minutes of data using this framework. Repo: https://github.com/fishaudio/fish-speech submitted by /u/lengyue233 [link] [comments]

  • [D] How successful are ML projects?
    by /u/natidone (Machine Learning) on May 22, 2024 at 6:37 am

    Our team just deployed our first ML solution. We have several people with DS/ML certificates, and have done ML in side projects and hackathons, but no one has a degree in stats or math or DS and no one's ever done ML professionally. We regularly consulted multiple DS / ML experts, but never had a dedicated DS on our team. It cost us ~$400K to implement, is expected to save $50K a year, and have operational costs of $20K a year. ​ It seems like pursuing this wasn't worth it. Was this a miscalculation on our part? What's the success rate of the projects you work on? How much do you cost the company vs how much money do you generate (or save)? ​ submitted by /u/natidone [link] [comments]

  • [D] Learning and Contributing in AI Agents
    by /u/Working_Resident2069 (Machine Learning) on May 22, 2024 at 6:13 am

    I find the AI agents powered by LLMs to be quite an interesting topic to look for! I was wondering if anyone can tell me what is the theoretical as well as practical background needed to understand and contribute it? Considering I am well versed with foundational NLP, Deep Learning techniques like RNNs, LSTMs and Transformers (Only SLMs) and basic RAG pipeline . Also, I also wanted to work/contribute in Open Source Projects related to ML as well so I would like to know what are some projects that you are aware of ? Please don't hesitate to share up your projects as well :). submitted by /u/Working_Resident2069 [link] [comments]

  • [Discussion] Metric to evaluate imbalance data.
    by /u/Civil_Statement_9331 (Machine Learning) on May 22, 2024 at 4:13 am

    I am processing a problem about classification images. I can augment and sampling my imbalance training set but my valid/test set got imbalance. Which metric I should use to evaluate my model (i know that F1-score exist but this is also a multiclass problem)? submitted by /u/Civil_Statement_9331 [link] [comments]

  • [D] What is your favorite way to expand your knowledge in the field post degree?
    by /u/RawCS (Machine Learning) on May 22, 2024 at 1:45 am

    I finished my M.S. in Statistics over a year ago, and my knowledge has gotten a bit rusty and I want to get back into pushing myself and growing my understanding of all these great concepts at our fingertips. I tend to love just picking up a textbook and working through it, or finding a cool data set and building out some models to solidify what I've been learning. I know that beyond getting a PhD or working in a lab, it's hard to actually make meaningful strides in terms of research; however, I honestly just deeply love math/statistics/CS, so studying this stuff is probably my favorite way to relax at the end of the day. What's your favorite way to keep yourself growing? submitted by /u/RawCS [link] [comments]

  • [D] How to combine PhD in Machine learning with Cloud and Distributed Systems?
    by /u/LeaderElectronic8810 (Machine Learning) on May 21, 2024 at 6:11 pm

    'm in college and I was wondering if there was any PhD focus for ML and building large efficient systems that run those models? I really like my ML research work(specifically deep learning and CV) and academic work, but I also like my cloud and distributed systems as a hobby. If I didn't want to do PhD, I would probably do something like MLops. Points: -Im not sure if I'm being too vague with my topics or if I'm looking up the wrong search terms, but I can't find any info on it. -Would I look for labs and professors that focus on HPC? I think of HPC as more general and focused on super computer work. I might like to explore edge devices ML or other scales. -Is there no point in combining ML with these, and that ML on large systems can be treated as just another branch of a CS PhD focused on distributed systems? submitted by /u/LeaderElectronic8810 [link] [comments]

  • [R] GRAD CAM on a Data Augmentation model
    by /u/LManuelG23 (Machine Learning) on May 21, 2024 at 5:11 pm

    UPDATE (I ADDE THE LINK AND SOME SS) hello everyone, i implemented a data augmentation model and im trying to watchh the Grad CAM of the neural network but theres a problem with the Data augmentation section and i cant solve that issue i search some implementation on google but is still not working and a didn`t found an implementation on a model with data augmentation, i asked to chatgpt but that code is not working do someone knows how to do it or any advice? i`ll post the link and the SS https://www.kaggle.com/code/luismanuelgnzalez/cnn-landuse data augmetation section https://preview.redd.it/rlvdeup5bt1d1.png?width=539&format=png&auto=webp&s=d695197b20fa7552d22e722fe54d0e668592b0cb MODEL https://preview.redd.it/h81y76q8bt1d1.png?width=582&format=png&auto=webp&s=6fd39c251d4a2267cb552e44bfc61d57867641e1 https://preview.redd.it/02xhz5habt1d1.png?width=668&format=png&auto=webp&s=29c153ee8c3c7fd3e432e47d49997933ad7662b7 code for CAM https://preview.redd.it/pscqz41fcu1d1.png?width=692&format=png&auto=webp&s=8dc790fcaba244940175c9fda7fb21da9cdd4bf0 https://preview.redd.it/o028579icu1d1.png?width=636&format=png&auto=webp&s=75fb6a81bdd90850a3b3217e22a1505b62e4f719 https://preview.redd.it/0a09vi1ncu1d1.png?width=889&format=png&auto=webp&s=ea91c089e9bb671dc849c3f6439a25a20724d410 and i got the following error https://preview.redd.it/4k2ftg7rcu1d1.png?width=984&format=png&auto=webp&s=b3c21e09b314e783fbdea1703a0347d9e2575785 submitted by /u/LManuelG23 [link] [comments]

  • Create a multimodal assistant with advanced RAG and Amazon Bedrock
    by Alfred Shen (AWS Machine Learning Blog) on May 21, 2024 at 4:28 pm

    In this post, we present a new approach named multimodal RAG (mmRAG) to tackle those existing limitations in greater detail. The solution intends to address these limitations for practical generative artificial intelligence (AI) assistant use cases. Additionally, we examine potential solutions to enhance the capabilities of large language models (LLMs) and visual language models (VLMs) with advanced LangChain capabilities, enabling them to generate more comprehensive, coherent, and accurate outputs while effectively handling multimodal data

  • Supermarket image dataset for planogram optimization[P]
    by /u/GMDragon23 (Machine Learning) on May 21, 2024 at 4:17 pm

    Hello, I have been working on object detection models for planogram optimization problems in the retail industry. So far, I have been using the SKU110K dataset. The issue with this dataset is that the products are not individually labeled. All the objects to detect are labeled as “object”. Do you know about a dataset similar to SKU110K that has the specific labels to each product in an image? Thank you submitted by /u/GMDragon23 [link] [comments]

  • How 20 Minutes empowers journalists and boosts audience engagement with generative AI on Amazon Bedrock
    by Aurélien Capdecomme (AWS Machine Learning Blog) on May 21, 2024 at 4:16 pm

    This post is co-written with Aurélien Capdecomme and Bertrand d’Aure from 20 Minutes. With 19 million monthly readers, 20 Minutes is a major player in the French media landscape. The media organization delivers useful, relevant, and accessible information to an audience that consists primarily of young and active urban readers. Every month, nearly 8.3 million 25–49-year-olds choose

  • Efficient and cost-effective multi-tenant LoRA serving with Amazon SageMaker
    by Michael Nguyen (AWS Machine Learning Blog) on May 21, 2024 at 3:33 pm

    In this post, we explore a solution that addresses these challenges head-on using LoRA serving with Amazon SageMaker. By using the new performance optimizations of LoRA techniques in SageMaker large model inference (LMI) containers along with inference components, we demonstrate how organizations can efficiently manage and serve their growing portfolio of fine-tuned models, while optimizing costs and providing seamless performance for their customers. The latest SageMaker LMI container offers unmerged-LoRA inference, sped up with our LMI-Dist inference engine and OpenAI style chat schema. To learn more about LMI, refer to LMI Starting Guide, LMI handlers Inference API Schema, and Chat Completions API Schema.

  • [P] A post on probabilistic calibration in blog series on polynomial regression
    by /u/alexsht1 (Machine Learning) on May 21, 2024 at 2:47 pm

    Another chapter in my personal learning about polynomial regression with the Bernsten basis passes through the lands of probabilistic model calibration. I certainly enjoyed learning, and I hope you'll find it interesting as well. Series begins here: https://alexshtf.github.io/2024/01/21/Bernstein.html Latest post on calibration here: https://alexshtf.github.io/2024/05/19/BernsteinCalibration.html submitted by /u/alexsht1 [link] [comments]

  • [R] Enabling sparse, foundational LLMs for faster and more efficient models from Neural Magic and Cerebras
    by /u/markurtz (Machine Learning) on May 21, 2024 at 1:36 pm

    In a collaboration across Neural Magic, Cerebras, and IST Austria, we've pushed out, to the best of our knowledge, the first highly sparse, foundational LLMs with full recovery on several fine-tuning tasks, including chat, code generation, summarization, and more. Sparsity vs Baseline Accuracy Recovery for Popular Fine-tuning Tasks for Llama 2 7B Utilizing the models, we further demonstrate: Inference performance speedups from sparsity alone at 3x for CPUs and 1.7x for GPUs on Neural Magic's platform. Compounded gains with quantization for up to 8.6X faster inference performance. Close to theoretical gains for sparse training utilizing Cerebras's CS-3 AI accelerator. Prefill and Decode Llama 2 7B Performance at Various Sparsity Levels for FP32 and INT8 on an 8-core CPU. Paper: https://arxiv.org/abs/2405.03594 Models: https://huggingface.co/collections/neuralmagic/sparse-foundational-llama-2-models-65f48cec6396309f02e74d21 submitted by /u/markurtz [link] [comments]

  • [R] LLMs as active learning agents
    by /u/Kaesebrot109 (Machine Learning) on May 21, 2024 at 12:29 pm

    TL;DR: LLMs can be used as active learning components because they are good at finding difficult or diverse examples - even outperforming few-shot learning methods. ​ While LLMs such as GPT-4 are commonly used in the training process of smaller BERT-like models (pseudo-labelling or data augmentation), we wondered if they could also be used as active learning agents. In contrast to conventional active learning, we see the opportunity that LLMs are really good at capturing the diversity and difficulty of examples and do not face a cold start problem. Conventional active learning often requires many seed instances, which makes it somewhat unattractive for many tasks where BERT models already achieve good performance in few-shot scenarios. ​ We experiment with different LLMs as active learning components and indeed show that they can significantly improve performance in few-shot scenarios: Few-shot results (32 instances) with different LLMs on GLUE tasks. We also show that active learning with GPT-4 can outperform the few-shot learning method SetFit: Comparison on AGNews (32 instances) Paper: http://arxiv.org/abs/2405.10808 submitted by /u/Kaesebrot109 [link] [comments]

  • [Research] What are some great articles on algorithmic fairness?
    by /u/berkaufman (Machine Learning) on May 21, 2024 at 11:57 am

    I am currently preparing a presentation on algorithmic fairness and I would like to hear your article suggestions. I would love it if the articles are recent but earlier milestone articles are also appreciated. submitted by /u/berkaufman [link] [comments]

  • [D] Should data in different modalities be represented in the same space?
    by /u/Capital_Reply_7838 (Machine Learning) on May 21, 2024 at 9:25 am

    As I've studied language AI primarily, I'm getting used to multimodal AI. However it seems training methodologies are so diverse, not to mention evaluating those are much more difficult imo. At least, I've thought data in different modalities should be represented different spaces. Is there any 'better method(maybe)' researchers agree? submitted by /u/Capital_Reply_7838 [link] [comments]

  • [D] Question about a derivation in "Understanding Black-box Predictions via Influence Functions" by Pang Wei Koh et al.
    by /u/SwiftLynx (Machine Learning) on May 21, 2024 at 8:20 am

    I was reading through https://arxiv.org/abs/1703.04730 and was looking into the derivation of equation (1) given in the Appendix A. I understand how we get equations (6)-(10), but the jump from equation (10) to (11) is confusing me. I also don't really get how you'd 2nd order Taylor expand the RHS of equation (10), as it's a vector-to-vector function. Do they mean 2nd order Taylor expanding the perturbed loss function R(theta) + epsilon L(theta)? If this is the case, shouldn't the hessian should be part of a quadratic form? In equation (11) they're only right multiplying by delta_epsilon (not left multiplying), so it's not in this form. submitted by /u/SwiftLynx [link] [comments]

  • [D] Thoughts on cloning/hacking a production FSD to teach new FSD
    by /u/yazriel0 (Machine Learning) on May 21, 2024 at 8:19 am

    If a company deploys a significantly better FSD. And you feed videos from different cars into the FSD, and use those actions to supervise a new FSD.. Setting aside legalities and scale, this seems feasible and inevitable? Either a state actor or even a western manufacturer looking to get the last 9s ? Thoughts ?? submitted by /u/yazriel0 [link] [comments]

  • [R] Have you give a try to use Intel and AMD GPUs to train models?
    by /u/Various_Protection71 (Machine Learning) on May 21, 2024 at 1:05 am

    NVIDIA rules the market of GPUs for datacenter. The most adopted frameworks for ML support NVIDIA GPUs. But I'm wondering about the drawbacks and advantages of using Intel and AMD GPUs to train ML models. Have you experienced use these GPUs? What could you say about performance, usability, software stack and ecosystem? submitted by /u/Various_Protection71 [link] [comments]

  • [D] Wav2Lip for anime characters?
    by /u/yukiarimo (Machine Learning) on May 21, 2024 at 12:01 am

    Hello guys. I saw Wav2Lip, open-source software for creating “deepfake” talking heads based on a single image and audio. Is there something like this, but explicitly for anime 2D characters? Open-source only! submitted by /u/yukiarimo [link] [comments]

  • [D] Has ML actually moved the needle on human health?
    by /u/Potential_Athlete238 (Machine Learning) on May 20, 2024 at 10:26 pm

    We've been hearing about ML for drug discovery, precision medicine, personalized treatment, etc. for quite some time. What are some ways ML has actually moved the needle on human health? It seems like most treatments and diagnostics are still based on decades of focused biology research rather than some kind of unbiased ML approach. Radiology is one notable exception that benefited from advances in machine vision, but even they seem slow to accept AI as clinical practice. submitted by /u/Potential_Athlete238 [link] [comments]

  • [D] are there any reading groups/journal clubs for ML/AI related topic?
    by /u/Illustrious-Pay-7516 (Machine Learning) on May 20, 2024 at 10:04 pm

    Hi, does anyone know if there are any reading groups/journal clubs where people share book chapters or papers regularly? It might be good to have some people reading the same book/paper share their ideas/thoughts if possible. Thanks! submitted by /u/Illustrious-Pay-7516 [link] [comments]

  • [D] - Can multimodal models tell images apart from text? Like if a text token and an image token are close vectors, will the model be able to "tell" if it is reading or seeing?
    by /u/30299578815310 (Machine Learning) on May 20, 2024 at 7:44 pm

    I ran into this doing some work with multimodal models. It seemed like they couldn't tell which part of the information was from the text vs the image portions of an input. Is there any research on this? submitted by /u/30299578815310 [link] [comments]

  • [D] Transliteration + translation of comments on Instagram app
    by /u/ts_aditya (Machine Learning) on May 20, 2024 at 3:31 pm

    Is it just me or does anyone notice the stark improvement in the quality of translation - especially translation from languages that is written using english characters (transliteration + translation). Wonder what kind of models they are using that led to the sudden improvement submitted by /u/ts_aditya [link] [comments]

  • [R] What is the state-of-art of model parallelism ?
    by /u/Various_Protection71 (Machine Learning) on May 20, 2024 at 12:51 am

    Is it easy to implement model parallelism with common frameworks like PyTorch and Tensorflow? It depends on the model architecture? What are the most used approaches on model parallelism ? submitted by /u/Various_Protection71 [link] [comments]

  • [P] Simplified PyTorch Implementation of AlphaFold 3
    by /u/csozboz (Machine Learning) on May 19, 2024 at 10:48 pm

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

  • [D] What role do you think machine learning will play in fields like computational biology and bioinformatics in the coming years?
    by /u/RawCS (Machine Learning) on May 19, 2024 at 8:19 pm

    I believe that computation biology and bioinformatics are going to be adopting ML work more and more, and I’m quite excited to see what advancements are made. I think it is going to open up a whole new world in terms of matching diseases to current medications that could potentially be used off label. What other things should we be on the lookout for? Who are some researchers working in this world? submitted by /u/RawCS [link] [comments]

  • [D] How did OpenAI go from doing exciting research to a big-tech-like company?
    by /u/UnluckyNeck3925 (Machine Learning) on May 19, 2024 at 4:46 pm

    I was recently revisiting OpenAI’s paper on DOTA2 Open Five, and it’s so impressive what they did there from both engineering and research standpoint. Creating a distributed system of 50k CPUs for the rollout, 1k GPUs for training while taking between 8k and 80k actions from 16k observations per 0.25s—how crazy is that?? They also were doing “surgeries” on the RL model to recover weights as their reward function, observation space, and even architecture has changed over the couple months of training. Last but not least, they beat the OG team (world champions at the time) and deployed the agent to play live with other players online. Fast forward a couple of years, they are predicting the next token in a sequence. Don’t get me wrong, the capabilities of gpt4 and its omni version are truly amazing feat of engineering and research (probably much more useful), but they don’t seem to be as interesting (from the research perspective) as some of their previous work. So, now I am wondering how did the engineers and researchers transition throughout the years? Was it mostly due to their financial situation and need to become profitable or is there a deeper reason for their transition? submitted by /u/UnluckyNeck3925 [link] [comments]

  • [D] Simple Questions Thread
    by /u/AutoModerator (Machine Learning) on May 19, 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]

  • Mixtral 8x22B is now available in Amazon SageMaker JumpStart
    by Marco Punio (AWS Machine Learning Blog) on May 17, 2024 at 4:02 pm

    Today, we are excited to announce the Mixtral-8x22B large language model (LLM), developed by Mistral AI, is available for customers through Amazon SageMaker JumpStart to deploy with one click for running inference. You can try out this model with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms and models so you

  • Building Generative AI prompt chaining workflows with human in the loop
    by Veda Raman (AWS Machine Learning Blog) on May 17, 2024 at 3:51 pm

    While Generative AI can create highly realistic content, including text, images, and videos, it can also generate outputs that appear plausible but are verifiably incorrect. Incorporating human judgment is crucial, especially in complex and high-risk decision-making scenarios. This involves building a human-in-the-loop process where humans play an active role in decision making alongside the AI system. In this blog post, you will learn about prompt chaining, how to break a complex task into multiple tasks to use prompt chaining with an LLM in a specific order, and how to involve a human to review the response generated by the LLM.

  • How LotteON built a personalized recommendation system using Amazon SageMaker and MLOps
    by SeungBum Shim (AWS Machine Learning Blog) on May 16, 2024 at 4:13 pm

    This post is co-written with HyeKyung Yang, Jieun Lim, and SeungBum Shim from LotteON. LotteON aims to be a platform that not only sells products, but also provides a personalized recommendation experience tailored to your preferred lifestyle. LotteON operates various specialty stores, including fashion, beauty, luxury, and kids, and strives to provide a personalized shopping

  • Build a serverless exam generator application from your own lecture content using Amazon Bedrock
    by Merieme Ezzaouia (AWS Machine Learning Blog) on May 15, 2024 at 4:21 pm

    Crafting new questions for exams and quizzes can be tedious and time-consuming for educators. The time required varies based on factors like subject matter, question types, experience level, and class level. Multiple-choice questions require substantial time to generate quality distractors and ensure a single unambiguous answer, and composing effective true-false questions demands careful effort to

  • Accelerate NLP inference with ONNX Runtime on AWS Graviton processors
    by Sunita Nadampalli (AWS Machine Learning Blog) on May 15, 2024 at 4:03 pm

    ONNX is an open source machine learning (ML) framework that provides interoperability across a wide range of frameworks, operating systems, and hardware platforms. ONNX Runtime is the runtime engine used for model inference and training with ONNX. AWS Graviton3 processors are optimized for ML workloads, including support for bfloat16, Scalable Vector Extension (SVE), and Matrix

  • Learn how Amazon Ads created a generative AI-powered image generation capability using Amazon SageMaker
    by Anita Lacea (AWS Machine Learning Blog) on May 15, 2024 at 3:45 pm

    Amazon Ads helps advertisers and brands achieve their business goals by developing innovative solutions that reach millions of Amazon customers at every stage of their journey. At Amazon Ads, we believe that what makes advertising effective is delivering relevant ads in the right context and at the right moment within the consumer buying journey. With that

  • RAG architecture with Voyage AI embedding models on Amazon SageMaker JumpStart and Anthropic Claude 3 models
    by Tengyu Ma (AWS Machine Learning Blog) on May 14, 2024 at 7:29 pm

    In this post, we provide an overview of the state-of-the-art embedding models by Voyage AI and show a RAG implementation with Voyage AI’s text embedding model on Amazon SageMaker Jumpstart, Anthropic’s Claude 3 model on Amazon Bedrock, and Amazon OpenSearch Service. Voyage AI’s embedding models are the preferred embedding models for Anthropic. In addition to general-purpose embedding models, Voyage AI offers domain-specific embedding models that are tuned to a particular domain.

  • Incorporate offline and online human – machine workflows into your generative AI applications on AWS
    by Tulip Gupta (AWS Machine Learning Blog) on May 14, 2024 at 5:52 pm

    Recent advances in artificial intelligence have led to the emergence of generative AI that can produce human-like novel content such as images, text, and audio. These models are pre-trained on massive datasets and, to sometimes fine-tuned with smaller sets of more task specific data. An important aspect of developing effective generative AI application is Reinforcement

  • Build generative AI applications with Amazon Titan Text Premier, Amazon Bedrock, and AWS CDK
    by Alain Krok (AWS Machine Learning Blog) on May 14, 2024 at 5:06 pm

    Amazon Titan Text Premier, the latest addition to the Amazon Titan family of large language models (LLMs), is now generally available in Amazon Bedrock. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and

  • Evaluation of generative AI techniques for clinical report summarization
    by Ekta Walia Bhullar (AWS Machine Learning Blog) on May 13, 2024 at 7:16 pm

    In this post, we provide a comparison of results obtained by two such techniques: zero-shot and few-shot prompting. We also explore the utility of the RAG prompt engineering technique as it applies to the task of summarization. Evaluating LLMs is an undervalued part of the machine learning (ML) pipeline.

  • AWS DeepRacer enables builders of all skill levels to upskill and get started with machine learning
    by Ange Krueger (AWS Machine Learning Blog) on May 10, 2024 at 6:08 pm

    In today’s technological landscape, artificial intelligence (AI) and machine learning (ML) are becoming increasingly accessible, enabling builders of all skill levels to harness their power. As more companies adopt AI solutions, there’s a growing need to upskill both technical and non-technical teams in responsibly expanding AI usage. Getting hands-on experience is crucial for understanding and

  • Transform customer engagement with no-code LLM fine-tuning using Amazon SageMaker Canvas and SageMaker JumpStart
    by Yann Stoneman (AWS Machine Learning Blog) on May 10, 2024 at 4:09 pm

    Fine-tuning large language models (LLMs) creates tailored customer experiences that align with a brand’s unique voice. Amazon SageMaker Canvas and Amazon SageMaker JumpStart democratize this process, offering no-code solutions and pre-trained models that enable businesses to fine-tune LLMs without deep technical expertise, helping organizations move faster with fewer technical resources. SageMaker Canvas provides an intuitive

  • How LotteON built dynamic A/B testing for their personalized recommendation system
    by HyeKyung Yang (AWS Machine Learning Blog) on May 9, 2024 at 4:08 pm

    This post is co-written with HyeKyung Yang, Jieun Lim, and SeungBum Shim from LotteON. LotteON is transforming itself into an online shopping platform that provides customers with an unprecedented shopping experience based on its in-store and online shopping expertise. Rather than simply selling the product, they create and let customers experience the product through their

  • Unleashing the power of generative AI: Verisk’s journey to an Instant Insight Engine for enhanced customer support
    by Tom Famularo (AWS Machine Learning Blog) on May 9, 2024 at 4:02 pm

    This post is co-written with Tom Famularo, Abhay Shah and Nicolette Kontor from Verisk. Verisk (Nasdaq: VRSK) is a leading data analytics and technology partner for the global insurance industry. Through advanced analytics, software, research, and industry expertise across over 20 countries, Verisk helps build resilience for individuals, communities, and businesses. The company is committed

  • Establishing an AI/ML center of excellence
    by Ankush Chauhan (AWS Machine Learning Blog) on May 9, 2024 at 3:42 pm

    The rapid advancements in artificial intelligence and machine learning (AI/ML) have made these technologies a transformative force across industries. According to a McKinsey study, across the financial services industry (FSI), generative AI is projected to deliver over $400 billion (5%) of industry revenue in productivity benefits. As maintained by Gartner, more than 80% of enterprises

  • Build a Hugging Face text classification model in Amazon SageMaker JumpStart
    by Hemant Singh (AWS Machine Learning Blog) on May 8, 2024 at 8:43 pm

    Amazon SageMaker JumpStart provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various types of input data, including

  • How Dialog Axiata used Amazon SageMaker to scale ML models in production with AI Factory and reduced customer churn within 3 months
    by Senthilvel (Vel) Palraj (AWS Machine Learning Blog) on May 8, 2024 at 6:21 pm

    The telecommunications industry is more competitive than ever before. With customers able to easily switch between providers, reducing customer churn is a crucial priority for telecom companies who want to stay ahead. To address this challenge, Dialog Axiata has pioneered a cutting-edge solution called the Home Broadband (HBB) Churn Prediction Model. This post explores the

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

Pass the 2024 AWS Cloud Practitioner CCP CLF-C01 Certification with flying colors Ace the 2024 AWS Solutions Architect Associate SAA-C03 Exam with Confidence

AWS Data analytics DAS-C01 Exam Preparation

AWS Data analytics DAS-C01 Exam Prep

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