AWS Machine Learning Certification Specialty Exam Prep

AWS Machine Learning Specialty Certification Prep (Android)

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

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

Download AWS machine Learning Specialty Exam Prep App on iOs

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

AWS MLS-C01 Machine Learning Specialty Exam Prep PRO

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AWS machine learning certification prep
AWS machine learning certification prep

Download AWS machine Learning Specialty Exam Prep App on iOs

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

The App provides hundreds of quizzes and practice exam about:

– Machine Learning Operation on AWS

– Modelling

– Data Engineering

– Computer Vision,

– Exploratory Data Analysis,

– ML implementation & Operations

– Machine Learning Basics Questions and Answers

– Machine Learning Advanced Questions and Answers

– Scorecard

– Countdown timer

– Machine Learning Cheat Sheets

– Machine Learning Interview Questions and Answers

– Machine Learning Latest News

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

Domain 1: Data Engineering

Create data repositories for machine learning.

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

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

Identify and implement a data ingestion solution.

Data job styles/types (batch load, streaming)

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

Domain 2: Exploratory Data Analysis

Sanitize and prepare data for modeling.

Perform feature engineering.

Analyze and visualize data for machine learning.

Domain 3: Modeling

Frame business problems as machine learning problems.

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

Train machine learning models.

Perform hyperparameter optimization.

Evaluate machine learning models.

Domain 4: Machine Learning Implementation and Operations

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

tolerance.

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

problem.

Apply basic AWS security practices to machine learning solutions.

Deploy and operationalize machine learning solutions.

Machine Learning Services covered:

Amazon Comprehend

AWS Deep Learning AMIs (DLAMI)

AWS DeepLens

Amazon Forecast

Amazon Fraud Detector

Amazon Lex

Amazon Polly

Amazon Rekognition

Amazon SageMaker

Amazon Textract

Amazon Transcribe

Amazon Translate

Other Services and topics covered are:

Ingestion/Collection

Processing/ETL

Data analysis/visualization

Model training

Model deployment/inference

Operational

AWS ML application services

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

Notebooks and integrated development environments (IDEs),

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

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

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

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

Download AWS machine Learning Specialty Exam Prep App on iOs

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

  • [P] I'm Building an LM team. Want to join?
    by /u/AtAndDev (Machine Learning) on March 3, 2024 at 6:30 am

    Hey there! I'm building a team that will develelop creative and SOTA LLMs. I'm currently interested in Recursive Self-Improving and System-2 thinking. Background: Together, Oasst, Turing AI. Let's build y'all! 🙂 submitted by /u/AtAndDev [link] [comments]

  • [P] How do I deploy my trained LORA adapter to a ChatUI in huggingface?
    by /u/portmanteau98 (Machine Learning) on March 3, 2024 at 5:15 am

    I have a trained LORA adapter on Mistral8x7b and uploaded on huggingface. How do I deploy it to a huggingchatUI interface such that it looks like this? Zephyr Gemma Chat - a Hugging Face Space by HuggingFaceH4 1. I’m asking since the tutorials and guides I’ve seen so far are only for models that aren’t adapters. Thank you. submitted by /u/portmanteau98 [link] [comments]

  • [N] Chris Van Pelt: ML Tooling, Weights and Biases, Entrepreneurship | Learning from Machine Learning #9
    by /u/NLPnerd (Machine Learning) on March 3, 2024 at 3:56 am

    Chris Van Pelt, co-founder of Weights & Biases and Figure Eight/CrowdFlower, played a pivotal role in the development of MLOps platforms and has dedicated the last two decades to refining ML workflows and making machine learning more accessible. Chris provides valuable insights into the current state of the industry. He emphasizes the significance of Weights & Biases as a powerful developer tool, empowering ML engineers to navigate through the complexities of experimentation, data visualization, and model improvement. His candid reflections on the challenges in evaluating ML models and addressing the gap between AI hype and reality offer a profound understanding of the field's intricacies. Drawing from his entrepreneurial experience co-founding two machine learning companies, Chris leaves us with lessons in resilience, innovation, and a deep appreciation for the human dimension within the tech landscape. submitted by /u/NLPnerd [link] [comments]

  • [R] [CVPR 2024] AV-RIR: Audio-Visual Room Impulse Response Estimation
    by /u/Snoo63916 (Machine Learning) on March 3, 2024 at 12:08 am

    submitted by /u/Snoo63916 [link] [comments]

  • [D][P] I have some questions about a project related to Chatbot that can give health advice.
    by /u/Infinite-Dragonfruit (Machine Learning) on March 3, 2024 at 12:05 am

    The requirements are that the chatbot must have good conversational flow and be able to help the user accurately. What sort of combination of deep learning nlp and LLMs would I need to use. submitted by /u/Infinite-Dragonfruit [link] [comments]

  • [P] TimesFM: Google's Foundation Model For Time-Series Forecasting
    by /u/apaxapax (Machine Learning) on March 2, 2024 at 10:45 pm

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

  • [D] Web data for building custom models?
    by /u/charlesthayer (Machine Learning) on March 2, 2024 at 10:00 pm

    I'm working on a new project and we're considering web crawling to collect data for building custom models. This crawl would be a combination of internal (private) and external pages and we might use gpt3.5 and RAG or build from scratch. What are others doing? Is everyone leveraging existing models, or is anyone doing more bespoke data collection and making their own models from scratch? Is there a better source of web data that's more recent than the common crawl, but not as hard as doing your own web crawling? Thanks! submitted by /u/charlesthayer [link] [comments]

  • [D] Explaining Transformers + VQ-VAE = LLMs that can generate images
    by /u/AvvYaa (Machine Learning) on March 2, 2024 at 8:15 pm

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

  • [R] Multiagent RL software
    by /u/misterpawan (Machine Learning) on March 2, 2024 at 7:59 pm

    A multi-agent RL framework in JAX. It implements popular IMPALA and OPRE multiagent frameworks. It has an interface to hundreds of multi-agent environments through melting pot and overcooked. submitted by /u/misterpawan [link] [comments]

  • [R] bGPT - Byte-Level Transformer
    by /u/Marha01 (Machine Learning) on March 2, 2024 at 6:47 pm

    submitted by /u/Marha01 [link] [comments]

  • Pytorch training help[D]
    by /u/NickRay1234 (Machine Learning) on March 2, 2024 at 5:56 pm

    I am training a deep neural network using Pytorch. At every step I want to see the parameter values. How do I do this. Even after I apply opt.step it shows the same values as the initialized ones submitted by /u/NickRay1234 [link] [comments]

  • [P] ArXiv Machine Learning Landscape
    by /u/lmcinnes (Machine Learning) on March 2, 2024 at 5:42 pm

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

  • [R] Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers
    by /u/Successful-Western27 (Machine Learning) on March 2, 2024 at 4:56 pm

    Training AI to understand and describe video content requires datasets which are expensive for humans to annotate manually. Now researchers from Snap, UC Merced, and the University of Trento have put together a new dataset called Panda-70M that aims to help. This new dataset has 70 million high-res YouTube clips paired with descriptive captions. The key is they used an automated pipeline with multiple cross-modal "teacher" AI models to generate captions based on different inputs like video, subtitles, images, etc. Some highlights: 70M 720p YouTube clips about 8 secs long with 13-word captions Teacher models include video QA, image captioning, text summarization Ensemble of teachers can accurately describe 84% of clips vs 31% for any single model Pretraining on this dataset improved video AI models' performance substantially: 18% boost in captioning accuracy after finetuning small 2.5M subset 7% better at text-video retrieval 77% reduction in video generation errors Limitations remain around content diversity, caption density, and automated quality. But I think this is a big step forward for assembling large-scale video-text training data to advance multimodal AI. Efficient pipelines like this could unlock video understanding capabilities approaching human level comprehension. Exciting to see some models trained on Panda-70M as they become available. Paper here. Summary here. submitted by /u/Successful-Western27 [link] [comments]

  • [D][R] What is the current state of the art for unsupervised image segmentation?
    by /u/bahauddin_onar (Machine Learning) on March 2, 2024 at 4:39 pm

    I am working on 4k image data for a PhD project and we have a ton of unlabeled images that needs to be segmented. The advisor wants to start with unsupervised image segmentation. Which algorithm should I start with playing and tweaking? submitted by /u/bahauddin_onar [link] [comments]

  • [D] What Is Your LLM Tech Stack in Production?
    by /u/gamerx88 (Machine Learning) on March 2, 2024 at 4:37 pm

    Curious what everybody is using to implement LLM powered apps for production usage and your experience with these toolings and advice. This is what I am using for some RAG prototypes I have been building for users in finance and capital markets. Pre-processing\ETL: Unstructured.io + Spark, Airflow Embedding model: Cohere Embed v3 Previously using OpenAI Ada but Cohere has significantly better retrieval recall and precision for my use case. Also exploring other open weights embedding models Vector Database: Elasticsearch previously but now using Pinecone LLM: Gone through quite a few including hosted and self-hosted options. Went with gpt4 early during prototyping then switched to gpt3.5-turbo for more manageable costs and eventually open weights models. Now using a fine-tuned Llama2 30B model self hosted with vLLM LLM Framework: Started with Langchain initially but found it cumbersome to extend as the app became more complex. Tried implementing it in LlamaIndex at some point just to learn and found it just as bad. Went back to Langchain and now I am in the midst of replacing it with my own logic What is everyone else using? submitted by /u/gamerx88 [link] [comments]

  • [D] Gemma training datasets list
    by /u/cosminptr (Machine Learning) on March 2, 2024 at 3:28 pm

    Hi, I am trying to find the datasets that were used during training the Gemma model family. The docs only provide a short description but nothing concrete. Does anyone know whether or not I can find such thing? The reason I am doing this is that I want to make sure a dataset that I chose was certainly not within the training samples. submitted by /u/cosminptr [link] [comments]

  • Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions
    by /u/Ashamed-Put-2344 (Machine Learning) on March 2, 2024 at 3:05 pm

    submitted by /u/Ashamed-Put-2344 [link] [comments]

  • [D] experience with Google TPU?
    by /u/siliconductor1 (Machine Learning) on March 2, 2024 at 2:02 pm

    Does anyone have experience porting software to Google’s TPUs? Is it as simple as lifting and shifting an existing PyTorch or TensorFlow workload, or is it more complicated? How about writing code from scratch, any easier? submitted by /u/siliconductor1 [link] [comments]

  • [D] LLM with analytical capabilities
    by /u/Fun-Ad953 (Machine Learning) on March 2, 2024 at 1:34 pm

    My boss asked me to create an application which can be connected to Redshift or Postgres databases and can retrieve numerical data and can answer financial and analysis questions for the company. He doesn't understand that RAG technique is not suited for numerical analysis stuff. Any idea how I can achieve this. He mentioned to use amazon Q for this task, which I find is awful in giving answers to numeric datasets submitted by /u/Fun-Ad953 [link] [comments]

  • [D] Best way to replace clothes on the photo?
    by /u/sl0bodzyany (Machine Learning) on March 2, 2024 at 1:05 pm

    Hi community 👋 As I am a noob in ML, reaching out for some help from knowing people. My goal is to create a proof-of-concept for a tool, that will replace clothes on a person. Expected input: - photo of t-shirt/hoodie/sweater lying on the table or hanged on a hanger - full-body photo of a man/woman standing in simple modelling pose Expected output: - full-body photo of same man/woman standing in the t-shirt/hoodie/sweater that was passed My friends suggested to go with this model: https://ootd.ibot.cn/ But maybe you have encountered that problem before and found a better model/pipeline for it? Also, I am considering expanding the inputs to: item's fabric/item's fit (regular/slim/etc.)/some other attributes that may help achieve the most realistic result without defects. Thank you for your time! Will appreciate any help/suggestions on how to approach this problem 🙂 submitted by /u/sl0bodzyany [link] [comments]

  • [D] Time series synthetic data
    by /u/_what_the_f (Machine Learning) on March 2, 2024 at 11:58 am

    Hi all. Has anyone dealt with creating seasonal time series synthetic data? Could you suggest whats the best approach? I’m doing multivariate time series forecast and was wondering if synthetic data is even applicable here, as it should mimic underlying relationships. I did create additional year of data using SDV (specifying constraints and distributions), but the model showed worse scores. Quality report showed 92.64% (column shapes and column pair trends). But Im still confused is it even possible to recreate underlying relationships between X variables and also X and y? Thanks. submitted by /u/_what_the_f [link] [comments]

  • [D] BitNet 1-b/b1.58 LLMs - is that a threat to nvidia?
    by /u/tunggad (Machine Learning) on March 2, 2024 at 10:42 am

    Link to paper: https://arxiv.org/pdf/2402.17764.pdf is that real? it sounds too good to be real right? If it is true, it not only reduces VRAM capacity and bandwidth required to train and run LLMs, it also suggests simplified hardware implementation due to the lack of need for matmul , it only needs + operation is that not a threat for nvidia (stock) and amd as well ? submitted by /u/tunggad [link] [comments]

  • [R] Humanoid Locomotion as Next Token Prediction
    by /u/StartledWatermelon (Machine Learning) on March 2, 2024 at 9:54 am

    Paper: https://arxiv.org/abs/2402.19469 Abstract: We cast real-world humanoid control as a next token prediction problem, akin to predicting the next word in language. Our model is a causal transformer trained via autoregressive prediction of sensorimotor trajectories. To account for the multi-modal nature of the data, we perform prediction in a modality-aligned way, and for each input token predict the next token from the same modality. This general formulation enables us to leverage data with missing modalities, like video trajectories without actions. We train our model on a collection of simulated trajectories coming from prior neural network policies, model-based controllers, motion capture data, and YouTube videos of humans. We show that our model enables a full-sized humanoid to walk in San Francisco zero-shot. Our model can transfer to the real world even when trained on only 27 hours of walking data, and can generalize to commands not seen during training like walking backward. These findings suggest a promising path toward learning challenging real-world control tasks by generative modeling of sensorimotor trajectories. ​ submitted by /u/StartledWatermelon [link] [comments]

  • [D] What generated (cloned) voice model is better Tortoise TTS + Ecker ?
    by /u/chriscs777 (Machine Learning) on March 2, 2024 at 6:57 am

    Hi, I am using https://git.ecker.tech/mrq/ai-voice-cloning which uses Tortoise TTS. I do clone a particular voice and then get a couple of models under "training/{voiceName}/finetune/modules". For example: 200_gpt.pth 400_gpt.pth 501_gpt.pth Question: Which one from the above is the best model, that creates the most similar to the original voice TTS ? PS: I want to do cleanups! and remove all unecesary files as they take lots of gb. Same thing for the folder "training\{voiceName}\finetune\training_state". If I know for example that 501 is the best model I can delete anything else and clean up 8gb. submitted by /u/chriscs777 [link] [comments]

  • Knowledge Bases for Amazon Bedrock now supports hybrid search
    by Mani Khanuja (AWS Machine Learning Blog) on March 1, 2024 at 10:29 pm

    At AWS re:Invent 2023, we announced the general availability of Knowledge Bases for Amazon Bedrock. With a knowledge base, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for fully managed Retrieval Augmented Generation (RAG). In a previous post, we described how Knowledge Bases for Amazon Bedrock manages the end-to-end

  • Expedite your Genesys Cloud Amazon Lex bot design with the Amazon Lex automated chatbot designer
    by Joe Morotti (AWS Machine Learning Blog) on March 1, 2024 at 4:51 pm

    The rise of artificial intelligence (AI) has created opportunities to improve the customer experience in the contact center space. Machine learning (ML) technologies continually improve and power the contact center customer experience by providing solutions for capabilities like self-service bots, live call analytics, and post-call analytics. Self-service bots integrated with your call center can help

  • [P] Luminal: Fast ML in Rust through graph compilation
    by /u/jafioti (Machine Learning) on March 1, 2024 at 4:44 pm

    Hi everyone, I've been working on an ML framework in Rust for a while and I'm finally excited to share it. Luminal is a deep learning library that uses composable compilers to achieve high performance. Current ML libraries tend to be large and complex because they try to map high level operations directly on to low level handwritten kernels, and focus on eager execution. Libraries like PyTorch contain hundreds of thousands of lines of code, making it nearly impossible for a single programmer to understand it all, set aside do a large refactor. But does it need to be so complex? ML models tend to be static dataflow graphs made up of a few simple operators. This allows us to have a dirt simple core only supporting a few primitive operations, and use them to build up complex neural networks. We can then write compilers that modify the graph after we build it, to swap more efficient ops back in depending on which backend we're running on. Luminal takes this approach to the extreme, supporting only 11 primitive operations (primops): Unary - Log2, Exp2, Sin, Sqrt, Recip Binary - Add, Mul, Mod, LessThan Other - SumReduce, MaxReduce, Contiguous Every complex operation boils down to these primitive operations, so when you do a - b for instance, add(a, mul(b, -1)) gets written to the graph. Or when you do a.matmul(b), what actually gets put on the graph is sum_reduce(mul(reshape(a), reshape(b))). Once the graph is built, iterative compiler passes can modify it to replace primops with more efficient ops, depending on the device it's running on. On Nvidia cards, for instance, efficient Cuda kernels are written on the fly to replace these ops, and specialized cublas kernels are swapped in for supported operations. This approach leads to a simple library, and performance is only limited by the creativity of the compiler programmer, not the model programmer. Luminal has a number of other neat features, check out the repo here Please lmk if you have any questions! submitted by /u/jafioti [link] [comments]

  • Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock
    by Mark Roy (AWS Machine Learning Blog) on February 29, 2024 at 4:25 pm

    Amazon Bedrock provides a broad range of models from Amazon and third-party providers, including Anthropic, AI21, Meta, Cohere, and Stability AI, and covers a wide range of use cases, including text and image generation, embedding, chat, high-level agents with reasoning and orchestration, and more. Knowledge Bases for Amazon Bedrock allows you to build performant and

  • Unlock personalized experiences powered by AI using Amazon Personalize and Amazon OpenSearch Service
    by Reagan Rosario (AWS Machine Learning Blog) on February 29, 2024 at 4:18 pm

    OpenSearch is a scalable, flexible, and extensible open source software suite for search, analytics, security monitoring, and observability applications, licensed under the Apache 2.0 license. Amazon OpenSearch Service is a fully managed service that makes it straightforward to deploy, scale, and operate OpenSearch in the AWS Cloud. OpenSearch uses a probabilistic ranking framework called BM-25

  • Automate Amazon SageMaker Pipelines DAG creation
    by Luis Felipe Yepez Barrios (AWS Machine Learning Blog) on February 29, 2024 at 4:13 pm

    Creating scalable and efficient machine learning (ML) pipelines is crucial for streamlining the development, deployment, and management of ML models. In this post, we present a framework for automating the creation of a directed acyclic graph (DAG) for Amazon SageMaker Pipelines based on simple configuration files. The framework code and examples presented here only cover

  • Accelerating large-scale neural network training on CPUs with ThirdAI and AWS Graviton
    by Vihan Lakshman (AWS Machine Learning Blog) on February 29, 2024 at 4:09 pm

    This guest post is written by Vihan Lakshman, Tharun Medini, and Anshumali Shrivastava from ThirdAI. Large-scale deep learning has recently produced revolutionary advances in a vast array of fields. Although this stunning progress in artificial intelligence remains remarkable, the financial costs and energy consumption required to train these models has emerged as a critical bottleneck

  • Supercharge your AI team with Amazon SageMaker Studio: A comprehensive view of Deutsche Bahn’s AI platform transformation
    by Prasanna Tuladhar (AWS Machine Learning Blog) on February 29, 2024 at 3:54 pm

    AI’s growing influence in large organizations brings crucial challenges in managing AI platforms. These include developing a scalable and operationally efficient platform that adheres to organizational compliance and security standards. Amazon SageMaker Studio offers a comprehensive set of capabilities for machine learning (ML) practitioners and data scientists. These include a fully managed AI development environment

  • Build a robust text-to-SQL solution generating complex queries, self-correcting, and querying diverse data sources
    by Sanjeeb Panda (AWS Machine Learning Blog) on February 28, 2024 at 7:38 pm

    Structured Query Language (SQL) is a complex language that requires an understanding of databases and metadata. Today, generative AI can enable people without SQL knowledge. This generative AI task is called text-to-SQL, which generates SQL queries from natural language processing (NLP) and converts text into semantically correct SQL. The solution in this post aims to

  • How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker
    by Dr. Björn Blomqvist (AWS Machine Learning Blog) on February 27, 2024 at 5:51 pm

    This is a guest post written by Axfood AB.  In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker. Axfood is Sweden’s second largest food retailer,

  • Techniques and approaches for monitoring large language models on AWS
    by Bruno Klein (AWS Machine Learning Blog) on February 26, 2024 at 5:59 pm

    Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP), improving tasks such as language translation, text summarization, and sentiment analysis. However, as these models continue to grow in size and complexity, monitoring their performance and behavior has become increasingly challenging. Monitoring the performance and behavior of LLMs is a critical task

  • [D] Simple Questions Thread
    by /u/AutoModerator (Machine Learning) on February 25, 2024 at 4:00 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]

  • Streamline diarization using AI as an assistive technology: ZOO Digital’s story
    by Ying Hou (AWS Machine Learning Blog) on February 20, 2024 at 6:18 pm

    ZOO Digital provides end-to-end localization and media services to adapt original TV and movie content to different languages, regions, and cultures. It makes globalization easier for the world’s best content creators. Trusted by the biggest names in entertainment, ZOO Digital delivers high-quality localization and media services at scale, including dubbing, subtitling, scripting, and compliance. Typical

  • Run ML inference on unplanned and spiky traffic using Amazon SageMaker multi-model endpoints
    by Ram Vegiraju (AWS Machine Learning Blog) on February 19, 2024 at 6:13 pm

    Amazon SageMaker multi-model endpoints (MMEs) are a fully managed capability of SageMaker inference that allows you to deploy thousands of models on a single endpoint. Previously, MMEs pre-determinedly allocated CPU computing power to models statically regardless the model traffic load, using Multi Model Server (MMS) as its model server. In this post, we discuss a

  • Use Amazon Titan models for image generation, editing, and searching
    by Rohit Mittal (AWS Machine Learning Blog) on February 19, 2024 at 5:53 pm

    Amazon Bedrock provides a broad range of high-performing foundation models from Amazon and other leading AI companies, including Anthropic, AI21, Meta, Cohere, and Stability AI, and covers a wide range of use cases, including text and image generation, searching, chat, reasoning and acting agents, and more. The new Amazon Titan Image Generator model allows content

  • Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock
    by Manish Chugh (AWS Machine Learning Blog) on February 19, 2024 at 4:43 pm

    Modern chatbots can serve as digital agents, providing a new avenue for delivering 24/7 customer service and support across many industries. Their popularity stems from the ability to respond to customer inquiries in real time and handle multiple queries simultaneously in different languages. Chatbots also offer valuable data-driven insights into customer behavior while scaling effortlessly

  • Code Llama 70B is now available in Amazon SageMaker JumpStart
    by Kyle Ulrich (AWS Machine Learning Blog) on February 16, 2024 at 4:32 pm

    Today, we are excited to announce that Code Llama foundation models, developed by Meta, are available for customers through Amazon SageMaker JumpStart to deploy with one click for running inference. Code Llama is a state-of-the-art large language model (LLM) capable of generating code and natural language about code from both code and natural language prompts.

  • Detect anomalies in manufacturing data using Amazon SageMaker Canvas
    by Helge Aufderheide (AWS Machine Learning Blog) on February 15, 2024 at 3:54 pm

    With the use of cloud computing, big data and machine learning (ML) tools like Amazon Athena or Amazon SageMaker have become available and useable by anyone without much effort in creation and maintenance. Industrial companies increasingly look at data analytics and data-driven decision-making to increase resource efficiency across their entire portfolio, from operations to performing

  • Enhance Amazon Connect and Lex with generative AI capabilities
    by Hamza Nadeem (AWS Machine Learning Blog) on February 14, 2024 at 5:43 pm

    Effective self-service options are becoming increasingly critical for contact centers, but implementing them well presents unique challenges. Amazon Lex provides your Amazon Connect contact center with chatbot functionalities such as automatic speech recognition (ASR) and natural language understanding (NLU) capabilities through voice and text channels. The bot takes natural language speech or text input, recognizes

  • Skeleton-based pose annotation labeling using Amazon SageMaker Ground Truth
    by Arthur Putnam (AWS Machine Learning Blog) on February 14, 2024 at 5:29 pm

    Pose estimation is a computer vision technique that detects a set of points on objects (such as people or vehicles) within images or videos. Pose estimation has real-world applications in sports, robotics, security, augmented reality, media and entertainment, medical applications, and more. Pose estimation models are trained on images or videos that are annotated with

  • Build generative AI chatbots using prompt engineering with Amazon Redshift and Amazon Bedrock
    by Ravikiran Rao (AWS Machine Learning Blog) on February 14, 2024 at 4:56 pm

    With the advent of generative AI solutions, organizations are finding different ways to apply these technologies to gain edge over their competitors. Intelligent applications, powered by advanced foundation models (FMs) trained on huge datasets, can now understand natural language, interpret meaning and intent, and generate contextually relevant and human-like responses. This is fueling innovation across

  • How BigBasket improved AI-enabled checkout at their physical stores using Amazon SageMaker
    by Santosh Waddi (AWS Machine Learning Blog) on February 13, 2024 at 5:44 pm

    This post is co-written with Santosh Waddi and Nanda Kishore Thatikonda from BigBasket. BigBasket is India’s largest online food and grocery store. They operate in multiple ecommerce channels such as quick commerce, slotted delivery, and daily subscriptions. You can also buy from their physical stores and vending machines. They offer a large assortment of over

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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AWS Data analytics DAS-C01 Exam Preparation: The AWS Data analytics DAS-C01 Exam Prep PRO App is very similar to real exam with a Countdown timer, a Score card.

It also gives users the ability to Show/Hide Answers, learn from Cheat Sheets, Flash Cards, and includes Detailed Answers and References for more than 300 AWS Data Analytics Questions.

Various Practice Exams covering Data Collection, Data Security, Data processing, Data Analysis, Data Visualization, Data Storage and Management,
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AWS Data Analytics DAS-C01 Exam Prep PRO


This App provides hundreds of Quizzes covering AWS Data analytics, Data Science, Data Lakes, S3, Kinesis, Lake Formation, Athena, Kibana, Redshift, EMR, Glue, Kafka, Apache Spark, SQL, NoSQL, Python, DynamoDB, DocumentDB,  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, Data cleansing, ETL, IoT, etc.

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  • Machine Learning Cheat Sheets
  • Python Cheat Sheets
  • SQL Cheat Sheets
  • Data Science and Data analytics cheat sheets