AWS Machine Learning Certification Specialty Exam Prep

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Pass the 2023 AWS Cloud Practitioner CCP CLF-C01 Certification with flying colors Ace the 2023 AWS Solutions Architect Associate SAA-C03 Exam with Confidence Pass the 2023 AWS Certified Machine Learning Specialty MLS-C01 Exam with Flying Colors

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


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


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:



Data analysis/visualization

Model training

Model deployment/inference


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

  • [N] Critical exploit in MLflow
    by /u/FlyingTriangle (Machine Learning) on March 24, 2023 at 12:21 pm

    We found an LFI/RFI that leads to system takeover and cloud account takeover in MLflow versions <2.2.1. The devs have had it patched for a few weeks now. No user interaction required Unauthenticated Remotely exploitable All configurations vulnerable including fresh install No prerequisite knowledge of the environment required We urge users of MLflow to patch immediately if they have not done so in the past month. submitted by /u/FlyingTriangle [link] [comments]

  • [P] Reinforcement learning evolutionary hyperparameter optimization - 10x speed up
    by /u/nicku_a (Machine Learning) on March 24, 2023 at 11:08 am

    Hey! We're creating an open-source training framework focused on evolutionary hyperparameter optimization for RL. This offers a speed up of 10x over other HPO methods! Check it out and please get involved if you would be interested in working on this - any contributions are super valuable. We believe this can change the way we train our models, and democratise access to RL for people and businesses who don't currently have the resources for it! GitHub: submitted by /u/nicku_a [link] [comments]

  • [D] I just realised: GPT-4 with image input can interpret any computer screen, any userinterface and any combination of them.
    by /u/Balance- (Machine Learning) on March 24, 2023 at 11:00 am

    GPT-4 is a multimodal model, which specifically accepts image and text inputs, and emits text outputs. And I just realised: You can layer this over any application, or even combinations of them. You can make a screenshot tool in which you can ask question. This makes literally any current software with an GUI machine-interpretable. A multimodal language model could look at the exact same interface that you are. And thus you don't need advanced integrations anymore. Of course, a custom integration will almost always be better, since you have better acces to underlying data and commands, but the fact that it can immediately work on any program will be just insane. Just a thought I wanted to share, curious what everybody thinks. submitted by /u/Balance- [link] [comments]

  • Reminder: Use the report button and read the rules!
    by /u/MTGTraner (Machine Learning) on March 24, 2023 at 9:32 am

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

  • [R] Artificial muses: Generative Artificial Intelligence Chatbots Have Risen to Human-Level Creativity
    by /u/blabboy (Machine Learning) on March 24, 2023 at 9:04 am

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

  • [D] Are there any methods to deal with false-negatives in a binary classification problem?
    by /u/Matthew2229 (Machine Learning) on March 24, 2023 at 7:40 am

    I'm interested in a binary classification problem. However I know my dataset contains false-negative labeled data (and no false-positive). Is there any literature or good approach for a problem like this? Maybe label smoothing or something? submitted by /u/Matthew2229 [link] [comments]

  • [P] ChatGPT with GPT-2: A minimum example of aligning language models with RLHF similar to ChatGPT
    by /u/liyanjia92 (Machine Learning) on March 24, 2023 at 7:32 am

    hey folks, happy Friday! I wish to get some feedback for my recent project of a minimum example of using RLHF on language models to improve human alignment. The goal is to compare with vanilla GPT-2 and supervised fine-tuned GPT-2 to see how much RLHF can benefit small models. Also I hope this project can show an example of the minimum requirements to build a RLHF training pipeline for LLMs. Github: Demo: Thanks a lot for any suggestions and feedback! submitted by /u/liyanjia92 [link] [comments]

  • [D] is it possible to use encodings from the vggface2 for face swap
    by /u/musssssssss (Machine Learning) on March 24, 2023 at 1:59 am

    i’m currently doing a project with the vggface2 resnet model. i had an idea to do a face swap with getting the encodings of the source and target faces, manipulating them. passing this new one into a decoder to get the face and blending it onto the original image. is this possible? i tried a version but the image was just noise and i think it was the decoder. i wasn’t too sure how to go about it submitted by /u/musssssssss [link] [comments]

  • [D] "Sparks of Artificial General Intelligence: Early experiments with GPT-4" contained unredacted comments
    by /u/QQII (Machine Learning) on March 23, 2023 at 10:56 pm

    Microsoft's research paper exploring the capabilities, limitations and implications of an early version of GPT-4 was found to contain unredacted comments by an anonymous twitter user. (threadreader, nitter,, Commented section titled "Toxic Content": dv3 (the interval name for GPT-4) varun commented lines arxiv, original /r/MachineLearning thread, hacker news submitted by /u/QQII [link] [comments]

  • [D] Ben Eysenbach, CMU: On designing simpler and more principled RL algorithms
    by /u/thejashGI (Machine Learning) on March 23, 2023 at 10:36 pm

    Listen to the podcast episode with Ben Eysenbach from CMU where we discuss about designing simpler and more principled RL algorithms! submitted by /u/thejashGI [link] [comments]

  • [D] What is the best open source chatbot AI to do transfer learning on?
    by /u/to4life4 (Machine Learning) on March 23, 2023 at 10:16 pm

    Let's say I have some proprietary text data. I want to train a chatbot to absorb said knowledge and be able to answer questions about it. What are the best open source frameworks for getting started with such a project? Ideally I'd want to be able to build out human feedback as well for sample prompts, to better help train. submitted by /u/to4life4 [link] [comments]

  • [P] The noisy sentences dataset: 550K sentences in 5 European languages augmented with noise for training and evaluating spell correction tools or machine learning models.
    by /u/radi-cho (Machine Learning) on March 23, 2023 at 9:43 pm

    GitHub: We have constructed our dataset to cover representatives from the language families used across Europe. Germanic - English, German; Romance - French; Slavic - Bulgarian; Turkic - Turkish; Use case example: Apply language models or other techniques to compare the sentence pairs and reconstruct the original sentences from the augmented ones. You can use a single multilingual solution to solve the challenge or employ multiple models/techniques for the separate languages. Per-word dictionary lookup is also an option. submitted by /u/radi-cho [link] [comments]

  • Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing
    by Liv d'Aliberti (AWS Machine Learning Blog) on March 23, 2023 at 6:29 pm

    This is joint post co-written by Leidos and AWS. Leidos is a FORTUNE 500 science and technology solutions leader working to address some of the world’s toughest challenges in the defense, intelligence, homeland security, civil, and healthcare markets. Leidos has partnered with AWS to develop an approach to privacy-preserving, confidential machine learning (ML) modeling where

  • [N] ChatGPT plugins
    by /u/Singularian2501 (Machine Learning) on March 23, 2023 at 6:09 pm We’ve implemented initial support for plugins in ChatGPT. Plugins are tools designed specifically for language models with safety as a core principle, and help ChatGPT access up-to-date information, run computations, or use third-party services. submitted by /u/Singularian2501 [link] [comments]

  • [D] Cost for serving realtime inferences of a model like PaLM 62B
    by /u/allrod5 (Machine Learning) on March 23, 2023 at 5:10 pm

    Hi, I'm not that familiar with LLMS but I would like to know if it is reasonable to assume an approximation on the costs of serving a model like PaLM 62B in AWS considering an average of 100 tokens for input and 300 tokens for input per request and having about 10 thousand requests per month. I remember reading not long ago that the costs of serving GPT3 were token-based. submitted by /u/allrod5 [link] [comments]

  • [R] Zero-shot Sign Pose Embedding model
    by /u/mathias-claassen (Machine Learning) on March 23, 2023 at 2:19 pm

    We built a model that converts sign language videos into embeddings. It takes body and hand pose keypoints from a video and converts this into an embedding for use in downstream tasks. We show how classification can be done on an unseen dataset. You can check out the repo at and the accompanying blog post here. submitted by /u/mathias-claassen [link] [comments]

  • [D] [R] GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
    by /u/radi-cho (Machine Learning) on March 23, 2023 at 11:53 am

    A paper was released by OpenAI, OpenResearch & UPenn titled "GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models."Link: Abstract: We investigate the potential implications of Generative Pre-trained Transformer (GPT) models and related technologies on the U.S. labor market. Using a new rubric, we assess occupations based on their correspondence with GPT capabilities, incorporating both human expertise and classifications from GPT-4. Our findings indicate that approximately 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of GPTs, while around 19% of workers may see at least 50% of their tasks impacted. The influence spans all wage levels, with higher-income jobs potentially facing greater exposure. Notably, the impact is not limited to industries with higher recent productivity growth. We conclude that Generative Pre-trained Transformers exhibit characteristics of general-purpose technologies (GPTs), suggesting that these models could have notable economic, social, and policy implications. What do you think about the societal and economic impacts of LLMs? Also, I've started an open-source repository to track projects and research papers about GPT-4: There are some related papers listed already. I would greatly appreciate your contributions. submitted by /u/radi-cho [link] [comments]

  • [R] Question about Selection of Machine Learning Type for a Neuroscience/Biomedical Engineering Problem
    by /u/Neuron_on_Fire (Machine Learning) on March 23, 2023 at 11:37 am

    All: Thank you for reading this. I have the following problem and goals: Let's say that I have measurements of neuron spiking activity from a particular location of the brain of a rat. This rat is also involved in a behavioral task in which the rat has to press a button in response to some visual cue. Suppose we have the following neuron spike activity time series with overlaid instances of button presses: ​ I want to identify feature of the time series (for example, in the frequency domain) to then use to make a model that can make predictions on button presses based on neuron spike activity. I'm under the impression that I can then arrive at confidence intervals for button presses (in terms of the time period window when the model 'thinks' a button press has occurred). I'm lost when it comes to types of machine learning models I can use for my particular goal. Any input is appreciated. If I need to provide more information, please let me know. Thank you again. submitted by /u/Neuron_on_Fire [link] [comments]

  • [N] Prompt-to-voice (Dall-E for Voice)
    by /u/mamafied (Machine Learning) on March 23, 2023 at 10:50 am

    Blogpost: Introducing Prompt-to-Voice - Describe It to Hear It / Blog / Coqui There is still space for improvement, but that is an exciting take on voice creation. I wonder if it'd be open-sourced alongside TTS. submitted by /u/mamafied [link] [comments]

  • [N] PyG 2.3.0 released: PyTorch 2.0 support, native sparse tensor support, explainability and accelerations
    by /u/Balance- (Machine Learning) on March 23, 2023 at 10:48 am

    PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. Today version 2.3 got released: submitted by /u/Balance- [link] [comments]

  • [P] New toolchain to train robust spiking NNs for mixed-signal Neuromorphic chips
    by /u/FrereKhan (Machine Learning) on March 23, 2023 at 10:28 am

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

  • [D] LLaMA or Alpaca Weights
    by /u/CrashTimeV (Machine Learning) on March 23, 2023 at 7:35 am

    Was anyone able to download the LLaMA or Alpaca weights for the 7B, 13B and or 30B models? If yes please share, not looking for HF weights submitted by /u/CrashTimeV [link] [comments]

  • [P] Open-source GPT4 & LangChain Chatbot for large PDF docs
    by /u/radi-cho (Machine Learning) on March 23, 2023 at 5:15 am

    GitHub: Demo video: submitted by /u/radi-cho [link] [comments]

  • [P] GPT-4 powered full stack web development with no manual coding
    by /u/CryptoSpecialAgent (Machine Learning) on March 23, 2023 at 3:47 am What do you all think of this approach to full stack gpt-assisted web development? In a sense its no code because the human user does not write or even edit the code - but in a sense its the opposite, because only an experienced web developer or at least a product manager would know how to instruct GPT in a useful manner. *** We are seeking donations to ensure this project continues and, quite literally, keep the lights on. Cryptos, cash, cards, openai access tokens with free credits, hardware, cloud GPUs, etc... all is appreciated. Please DM to support this really cool open source project *** PS. I'm the injured engineer who made this thing out of necessity, because i injured my wrist building an AI platform that's become way too big for one engineer to maintain. So AMA 🙂 submitted by /u/CryptoSpecialAgent [link] [comments]

  • [R] Sparks of Artificial General Intelligence: Early experiments with GPT-4
    by /u/SWAYYqq (Machine Learning) on March 23, 2023 at 1:19 am

    New paper by MSR researchers analyzing an early (and less constrained) version of GPT-4. Spicy quote from the abstract: "Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system." What are everyone's thoughts? submitted by /u/SWAYYqq [link] [comments]

  • [R] Introducing SIFT: A New Family of Sparse Iso-FLOP Transformations to Improve the Accuracy of Computer Vision and Language Models
    by /u/CS-fan-101 (Machine Learning) on March 22, 2023 at 10:50 pm

    Note: Thank you r/MachineLearning for providing so many awesome naming alternatives! We'll revise the name accordingly. We look forward to hearing any additional feedback you have on the research. We are excited to announce the availability of our paper on arxiv on Sparse Iso-FLOP Transformations (SIFT), which increases accuracy and maintains the same FLOPs as the dense model using sparsity. In this research, we replace dense layers with SIFT and significantly improve computer vision and natural language processing tasks without modifying training hyperparameters Some of the highlights of this work include ResNet-18 on ImageNet achieving a 3.5% accuracy improvement and GPT-3 Small on WikiText-103 reducing perplexity by 0.4, both matching larger dense model variants that have 2x or more FLOPs. The SIFT transformations are simple to use, provide a larger search space to find optimal sparse masks, and are parameterized by a single hyperparameter - the sparsity level. This is independent of the research we posted yesterday, which demonstrates the ability to reduce pre-training FLOPs while maintaining accuracy on downstream tasks. This is the first work (that we know of!) to demonstrate the use of sparsity for improving the accuracy of models via a set of sparse transformations. submitted by /u/CS-fan-101 [link] [comments]

  • [P] Serge, a self-hosted app for running LLaMa models (Alpaca) entirely locally, no remote API needed.
    by /u/SensitiveCranberry (Machine Learning) on March 22, 2023 at 8:21 pm

    Hello there! Serge chat UI, with conversations on the left I've recently been working on Serge, a self-hosted dockerized way of running LLaMa models with a decent UI & stored conversations. It currently supports Alpaca 7B, 13B and 30B and we're working on integrating it with LangChain and the ReAct chain agent. I've tried my best at making the instructions dead easy, so it's all dockerized with a download manager for weights and it can be run with almost zero configuration required. I think being able to run those models locally will be key to expanding their ability, and so I hope this can contribute to that. Let me know if you have any feedback or suggestions on how to extend its capabilities! ​ GitHub: submitted by /u/SensitiveCranberry [link] [comments]

  • Automate Amazon Rekognition Custom Labels model training and deployment using AWS Step Functions
    by Veda Raman (AWS Machine Learning Blog) on March 22, 2023 at 4:45 pm

    With Amazon Rekognition Custom Labels, you can have Amazon Rekognition train a custom model for object detection or image classification specific to your business needs. For example, Rekognition Custom Labels can find your logo in social media posts, identify your products on store shelves, classify machine parts in an assembly line, distinguish healthy and infected

  • Build a machine learning model to predict student performance using Amazon SageMaker Canvas
    by Ashutosh Kumar (AWS Machine Learning Blog) on March 22, 2023 at 4:40 pm

    There has been a paradigm change in the mindshare of education customers who are now willing to explore new technologies and analytics. Universities and other higher learning institutions have collected massive amounts of data over the years, and now they are exploring options to use that data for deeper insights and better educational outcomes. You

  • Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler
    by Ajjay Govindaram (AWS Machine Learning Blog) on March 22, 2023 at 4:30 pm

    In this post, we show how to configure a new OAuth-based authentication feature for using Snowflake in Amazon SageMaker Data Wrangler. Snowflake is a cloud data platform that provides data solutions for data warehousing to data science. Snowflake is an AWS Partner with multiple AWS accreditations, including AWS competencies in machine learning (ML), retail, and

  • Remote monitoring of raw material supply chains for sustainability with Amazon SageMaker geospatial capabilities
    by Karsten Schroer (AWS Machine Learning Blog) on March 21, 2023 at 4:48 pm

    Deforestation is a major concern in many tropical geographies where local rainforests are at severe risk of destruction. About 17% of the Amazon rainforest has been destroyed over the past 50 years, and some tropical ecosystems are approaching a tipping point beyond which recovery is unlikely. A key driver for deforestation is raw material extraction

  • Best practices for viewing and querying Amazon SageMaker service quota usage
    by Bruno Klein (AWS Machine Learning Blog) on March 21, 2023 at 4:32 pm

    Amazon SageMaker customers can view and manage their quota limits through Service Quotas. In addition, they can view near real-time utilization metrics and create Amazon CloudWatch metrics to view and programmatically query SageMaker quotas. SageMaker helps you build, train, and deploy machine learning (ML) models with ease. To learn more, refer to Getting started with

  • Build custom code libraries for your Amazon SageMaker Data Wrangler Flows using AWS Code Commit
    by Uchenna Egbe (AWS Machine Learning Blog) on March 21, 2023 at 4:27 pm

    As organizations grow in size and scale, the complexities of running workloads increase, and the need to develop and operationalize processes and workflows becomes critical. Therefore, organizations have adopted technology best practices, including microservice architecture, MLOps, DevOps, and more, to improve delivery time, reduce defects, and increase employee productivity. This post introduces a best practice

  • Accelerate Amazon SageMaker inference with C6i Intel-based Amazon EC2 instances
    by Rohit Chowdhary (AWS Machine Learning Blog) on March 20, 2023 at 8:06 pm

    This is a guest post co-written with Antony Vance from Intel. Customers are always looking for ways to improve the performance and response times of their machine learning (ML) inference workloads without increasing the cost per transaction and without sacrificing the accuracy of the results. Running ML workloads on Amazon SageMaker running Amazon Elastic Compute

  • Intelligently search your organization’s Microsoft Teams data source with the Amazon Kendra connector for Microsoft Teams
    by Praveen Edem (AWS Machine Learning Blog) on March 17, 2023 at 6:49 pm

    Organizations use messaging platforms like Microsoft Teams to bring the right people together to securely communicate with each other and collaborate to get work done. Microsoft Teams captures invaluable organizational knowledge in the form of the information that flows through it as users collaborate. However, making this knowledge easily and securely available to users can

  • Bring legacy machine learning code into Amazon SageMaker using AWS Step Functions
    by Bhavana Chirumamilla (AWS Machine Learning Blog) on March 15, 2023 at 6:32 pm

    Tens of thousands of AWS customers use AWS machine learning (ML) services to accelerate their ML development with fully managed infrastructure and tools. For customers who have been developing ML models on premises, such as their local desktop, they want to migrate their legacy ML models to the AWS Cloud to fully take advantage of

  • How VMware built an MLOps pipeline from scratch using GitLab, Amazon MWAA, and Amazon SageMaker
    by Deepak Mettem (AWS Machine Learning Blog) on March 13, 2023 at 6:41 pm

    This post is co-written with Mahima Agarwal, Machine Learning Engineer, and Deepak Mettem, Senior Engineering Manager, at VMware Carbon Black VMware Carbon Black is a renowned security solution offering protection against the full spectrum of modern cyberattacks. With terabytes of data generated by the product, the security analytics team focuses on building machine learning (ML)

  • Few-click segmentation mask labeling in Amazon SageMaker Ground Truth Plus
    by Jonathan Buck (AWS Machine Learning Blog) on March 13, 2023 at 6:36 pm

    Amazon SageMaker Ground Truth Plus is a managed data labeling service that makes it easy to label data for machine learning (ML) applications. One common use case is semantic segmentation, which is a computer vision ML technique that involves assigning class labels to individual pixels in an image. For example, in video frames captured by

  • [D] Simple Questions Thread
    by /u/AutoModerator (Machine Learning) on March 12, 2023 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]

  • Accelerate time to insight with Amazon SageMaker Data Wrangler and the power of Apache Hive
    by Ajjay Govindaram (AWS Machine Learning Blog) on March 10, 2023 at 6:24 pm

    Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio. Data Wrangler enables you to access data from a wide variety of popular sources (Amazon S3, Amazon Athena, Amazon Redshift, Amazon EMR and Snowflake) and over 40 other third-party sources.

  • Using Amazon SageMaker with Point Clouds: Part 1- Ground Truth for 3D labeling
    by Isaac Privitera (AWS Machine Learning Blog) on March 10, 2023 at 6:20 pm

    In this two-part series, we demonstrate how to label and train models for 3D object detection tasks. In part 1, we discuss the dataset we’re using, as well as any preprocessing steps, to understand and label data. In part 2, we walk through how to train a model on your dataset and deploy it to

  • Real-time fraud detection using AWS serverless and machine learning services
    by Giedrius Praspaliauskas (AWS Machine Learning Blog) on March 10, 2023 at 6:14 pm

    Online fraud has a widespread impact on businesses and requires an effective end-to-end strategy to detect and prevent new account fraud and account takeovers, and stop suspicious payment transactions. In this post, we show a serverless approach to detect online transaction fraud in near-real time. We show how you can apply this approach to various data streaming and event-driven architectures, depending on the desired outcome and actions to take to prevent fraud (such as alert the user about the fraud or flag the transaction for additional review).

  • Architect personalized generative AI SaaS applications on Amazon SageMaker
    by Joao Moura (AWS Machine Learning Blog) on March 9, 2023 at 6:57 pm

    The AI landscape is being reshaped by the rise of generative models capable of synthesizing high-quality data, such as text, images, music, and videos. The course toward democratization of AI helped to further popularize generative AI following the open-source releases for such foundation model families as BERT, T5, GPT, CLIP and, most recently, Stable Diffusion.

  • Use a data-centric approach to minimize the amount of data required to train Amazon SageMaker models
    by Nicolas Bernier (AWS Machine Learning Blog) on March 9, 2023 at 6:04 pm

    As machine learning (ML) models have improved, data scientists, ML engineers and researchers have shifted more of their attention to defining and bettering data quality. This has led to the emergence of a data-centric approach to ML and various techniques to improve model performance by focusing on data requirements. Applying these techniques allows ML practitioners

  • Use Snowflake as a data source to train ML models with Amazon SageMaker
    by Amit Arora (AWS Machine Learning Blog) on March 8, 2023 at 5:42 pm

    Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. Sagemaker provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so

  • How Marubeni is optimizing market decisions using AWS machine learning and analytics
    by Hernan Figueroa (AWS Machine Learning Blog) on March 8, 2023 at 5:38 pm

    This post is co-authored with Hernan Figueroa, Sr. Manager Data Science at Marubeni Power International. Marubeni Power International Inc (MPII) owns and invests in power business platforms in the Americas. An important vertical for MPII is asset management for renewable energy and energy storage assets, which are critical to reduce the carbon intensity of our

  • Portfolio optimization through multidimensional action optimization using Amazon SageMaker RL
    by Dilshad Raihan Akkam Veettil (AWS Machine Learning Blog) on March 8, 2023 at 5:25 pm

    Reinforcement learning (RL) encompasses a class of machine learning (ML) techniques that can be used to solve sequential decision-making problems. RL techniques have found widespread applications in numerous domains, including financial services, autonomous navigation, industrial control, and e-commerce. The objective of an RL problem is to train an agent that, given an observation from its

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 2023 AWS Cloud Practitioner CCP CLF-C01 Certification with flying colors Ace the 2023 AWS Solutions Architect Associate SAA-C03 Exam with Confidence Pass the 2023 AWS Certified Machine Learning Specialty MLS-C01 Exam with Flying Colors

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