AWS Machine Learning Certification Specialty Exam Prep

AWS Machine Learning Specialty Certification Prep (Android)

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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

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

  • [Discussion] Cognitive science inspired AI research
    by /u/theanswerisnt42 (Machine Learning) on February 8, 2023 at 11:02 am

    I came across a few comments on this community about researchers developing AI algorithms inspired by ideas from neuroscience/cognition. I'd like to know how successful this approach has been in terms of coming up with new perspectives on problems. What are some of the key issues researchers are trying to address this way? What are some future directions in which research may progress? I have a rough idea that this could be one way to inspire sample efficient RL but I'd love to hear about other work that goes on in this area submitted by /u/theanswerisnt42 [link] [comments]

  • [D] List of RL Papers
    by /u/C_l3b (Machine Learning) on February 8, 2023 at 8:36 am

    Hi, I want to open a thread about RL (non-deep and deep) What are the papers/books that are "must read" to have strong foundation? submitted by /u/C_l3b [link] [comments]

  • [D] What do you think about this 16 week curriculum for existing software engineers who want to pursue AI and ML?
    by /u/Imaginary-General687 (Machine Learning) on February 8, 2023 at 7:12 am

    ​ https://preview.redd.it/3szt07d50xga1.png?width=936&format=png&auto=webp&s=6838bf1464049f58c2be4e5fe50544b21f0cc699 submitted by /u/Imaginary-General687 [link] [comments]

  • [P] Scripts/Programs to collect Baseline Logs
    by /u/Dweeberbob (Machine Learning) on February 8, 2023 at 3:58 am

    Abit of a weird question. So I'm required to make & collect some clean (baseline) logs and dirty (malicious) logs for some mini-ML project I'm doing. So my question is, is there any scripts or programs out there, Linux or Windows, that allows the automation of mimicking an office staff doing work (ie. opening Outlook, sending emails, surfing the web, watching YouTube, opening and editing Word/Excel files, etc.) for the purpose of collecting baseline logs? I'm relative new to this kind of thing, if you guys have better suggestion on a more better/efficient way to do this, feel free to suggest! submitted by /u/Dweeberbob [link] [comments]

  • [N] New Book on Synthetic Data​: Version 3.0 Just Released
    by /u/MLRecipes (Machine Learning) on February 8, 2023 at 1:27 am

    The book has considerably grown since version 1.0. It started with synthetic data as one of the main components, but also diving into explainable AI, intuitive / interpretable machine learning, and generative AI. Now with 272 pages (up from 156 in the first version), the focus is clearly on synthetic data. Of course, I still discuss explainable and generative AI: these concepts are strongly related to data synthetization. Agent-based modeling in action However many new chapters have been added, covering various aspects of synthetic data — in particular working with more diversified real datasets, how to synthetize them, how to generate high quality random numbers with a very fast algorithm based on digits of irrational numbers, with visual illustrations and Python code in all chapters. In addition to agent-based modeling newly added, you will find material about GAN — generative adversarial networks applied using methods other than neural networks. GMM — Gaussian mixture models and alternatives based on multivariate stochastic and lattice processes. The Hellinger distance and other metrics to measure the quality of your synthetic data, and the limitations of these metrics. The use of copulas with detailed explanations on how it works, Python code, and application to mimicking a real dataset. Drawbacks associated with synthetic data, in particular a tendency to replicate algorithm bias that synthetization is supposed to eliminate (and how to avoid this). A technique somewhat similar to ensemble methods / tree boosting but specific to data synthetization, to further enhance the value of synthetic data when blended with real data; the goal is to make predictions more robust and applicable to a wider range of observations truly different from those in your original training set. Synthetizing nearest neighbor and collision graphs, locally random permutations, shapes, and an introduction to AI-art Newly added applications include dealing with numerous data types and datasets, including ocean times in Dublin (synthetic time series), temperatures in the Chicago area (geospatial data) and the insurance data set (tabular data). I also included some material from the course that I teach on the subject. For the time being, the book is available only in PDF format on my e-Store here, with numerous links, backlinks, index, glossary, large bibliography and navigation features to make it easy to browse. This book is a compact yet comprehensive resource on the topic, the first of its kind. The quality of the formatting and color illustrations is unusually high. I plan on adding new books in the future: the next one will be on chaotic dynamical systems with applications. However, the book on synthetic data has been accepted by a major publisher and a print version will be available. But it may take a while before it gets released, and the PDF version has useful features that can not be rendered well in print nor on devices such as Kindle. Once published in the computer science series with the publisher in question, the PDF version may no longer be available. You can check out the content on my GitHub repository, here where the Python code, sample chapters, and datasets also reside. submitted by /u/MLRecipes [link] [comments]

  • [P] AI/ML Engineering Tutor
    by /u/ThoseWhoAbandonViews (Machine Learning) on February 7, 2023 at 11:58 pm

    I am looking for a AI/Machine Learning Engineering tutor. I am specifically interested in help developing and building ~2 Medium/large NLP projects, using SOTA LLM’s (GPT3, etc.). After these, I would potentially be interested in diving into RL. I am looking for a little bit of theory when needed, but predominantly hands-on coding project help (eg. real-time coding help if I get stuck). Reach out if you are interested! (Of course $ negotiated). submitted by /u/ThoseWhoAbandonViews [link] [comments]

  • [Discussion] Can an AMD Ryzen 5 3400G computer with 16GB of RAM effectively train an AI model?
    by /u/erikaonline (Machine Learning) on February 7, 2023 at 10:42 pm

    I'm exploring the possibility of using my AMD Ryzen 5 3400G computer with 16GB of RAM to train an AI model. I'm curious to know if this setup is adequate for the task and, if so, what kind of AI models would be appropriate. I'm interested in understanding any limitations and drawbacks that I may face with this setup. If you have any relevant experience or information, I would greatly appreciate your participation in this discussion. Thanks! <3 submitted by /u/erikaonline [link] [comments]

  • [D] Image object detection, but for 1 dimensional data?
    by /u/Optoplasm (Machine Learning) on February 7, 2023 at 10:36 pm

    I have had a lot of fun and success using YOLO and other image object detection models on 2D or 3D image data for personal projects. I am now working on some projects where I need to scan long periods of timeseries data and find specific waveforms that are variable durations. Are there techniques or models that function like YOLO that can scan large amounts of data and only highlight specific segments of interest as specific classes? If it doesn’t exist, I wonder how well the underlying CNN architecture of YOLO would translate to 1 dimensional CNN architectures. Any info is appreciated, thanks! submitted by /u/Optoplasm [link] [comments]

  • [P] Best way to add a sampling step within a neural network end-to-end?
    by /u/geomtry (Machine Learning) on February 7, 2023 at 10:32 pm

    I'm looking to combine two separate models together end-to-end, but need help understanding the best way to connect discrete parts. The first part: I trained a classifier that given an input vector (512 dimensional) is able to predict one of twenty possible labels. The second part: given an input label (from the previous classifier), embed the label and use that label to make a prediction. Both models work decently, but I'm wondering if I can make this end-to-end and get some serious gains. To do this, I'd need a way of sampling from the first softmax. Once I have a sample, I can get the embedding of the sampled class, continue as normal, and hopefully propagate the loss through everything. Are there any similar examples I can look at? Is there a term for this in the literature? submitted by /u/geomtry [link] [comments]

  • Share medical image research on Amazon SageMaker Studio Lab for free
    by Stephen Aylward (AWS Machine Learning Blog) on February 7, 2023 at 9:04 pm

    This post is co-written with Stephen Aylward, Matt McCormick, Brianna Major from Kitware and Justin Kirby from the Frederick National Laboratory for Cancer Research (FNLCR). Amazon SageMaker Studio Lab provides no-cost access to a machine learning (ML) development environment to everyone with an email address. Like the fully featured Amazon SageMaker Studio, Studio Lab allows

  • [D] Can output time frame cover input time frame in machine learning?
    by /u/dencan06 (Machine Learning) on February 7, 2023 at 8:06 pm

    I recently had a disagreement with a friend and would like to hear other opinions. Say for a website, using the user actions for first week period, we want to predict total sales within 3 weeks. But one of the inputs is sales in the first week, so the output -total sales of 3 weeks- is including the sales in the first week. Is it ok to choose this output? Or should we adjust it in a way to prevent it from overlapping with the input time period and choose for ex. sales within 2 weeks after the first week for output What is the reasoning? submitted by /u/dencan06 [link] [comments]

  • [N] Microsoft announces new "next-generation" LLM, will be integrated with Bing and Edge
    by /u/currentscurrents (Machine Learning) on February 7, 2023 at 6:38 pm

    https://www.theverge.com/2023/2/7/23587454/microsoft-bing-edge-chatgpt-ai submitted by /u/currentscurrents [link] [comments]

  • [D] Multi-class classifications when a few of the classes are not mutually-exclusive
    by /u/hopedallas (Machine Learning) on February 7, 2023 at 5:38 pm

    I am dealing with a multi-class classification problem. I know one of the main assumption of this problem is that the classes are mutually exclusive. However, I realized that in my problem, some of these classes may happen together. So my problem is not an entirely a milt-class nor a multi-label. One solution is to relax the exclusivity assumption and fit a model, however, I am not sure how realistic is that. I was wondering if there is a better way to approach this problem? Briefly, the problem is in ads domain where a user can do task A or B after seeing an ad or can do both A&B at the same time. submitted by /u/hopedallas [link] [comments]

  • Amazon SageMaker Automatic Model Tuning now supports three new completion criteria for hyperparameter optimization
    by Doug Mbaya (AWS Machine Learning Blog) on February 7, 2023 at 5:21 pm

    Amazon SageMaker has announced the support of three new completion criteria for Amazon SageMaker automatic model tuning, providing you with an additional set of levers to control the stopping criteria of the tuning job when finding the best hyperparameter configuration for your model. In this post, we discuss these new completion criteria, when to use them, and

  • [D] Which is the fastest and lightweight ultra realistic TTS for real-time voice cloning?
    by /u/akshaysri0001 (Machine Learning) on February 7, 2023 at 5:15 pm

    Hey everyone, I want to make a personal voice assistant who sounds exactly like a real person. I tried some TTS like tortoise TTS and coqui TTS, it done a good job but it takes too long time to perform. So is there any other good realistic sounding TTS which I can use with my own voice cloning training dataset? Also I'm a bit amazed by the TTS used by eleven labs, so can someone explain how can I achieve that level of real-time efficiency in a voice assistant? submitted by /u/akshaysri0001 [link] [comments]

  • [D] Artificial Intelligence for Manufacturing
    by /u/Much-Bit3531 (Machine Learning) on February 7, 2023 at 4:53 pm

    Manufacturing 4.0 is undergoing a revolution with the integration of Artificial Intelligence (AI). AI is poised to revolutionize the process industry, where controlling input variables leads to an output. The current process industry, including pharmaceuticals, chemicals, and energy production, relies on human operators to turn knobs to achieve optimal output. However, this system is limited by several factors, including slow training, poor retention of large data sets, inaccurate sensors, and complex decision-making processes. ​ https://preview.redd.it/y6stc52zqsga1.png?width=734&format=png&auto=webp&s=c8f24ff9d7f7975380e6cd2fbb5021fce67c0e86 Here are some details about problems and AI solutions: 1) It takes forever to train this employee. This employee is running little mini experiments and getting coached by other employees and engineers along the way. So the quality of training per year is variable. AI eliminates this problem by retaining the results of the mini experience in its models. Now everyone has access to how the process behaves. 2) The numbers of KPIs can be huge and not all KPIs are linear. Humans are notoriously bad at retaining large data sets with multiple variables. Humans delete, distort and generalize data so we can come up with easier to follow rules of thumb. Machine are not limited by this. In AI, the more data the more way combined the better. The models can evolve as new data comes in. 3) The automatic sensors are many times are precise but not accurate. This can happen because the sensors get off calibration or the calibration is dependent on other variables in the process. Operators usually use manual measurements that are very accurate but not precise to know where the process actually is. This manual measurement can be used the calibrate the sensors but, it seen as a losing battle. AI can use that data to continuously update the calibration of the sensors and add calibrations for other input variables such as Ph, flow rate, or temperature. When this is done the you can trust the sensors. 4) Many process decisions require if then statements. These if then statements change by the product that is being run making it extremely complicated. AI systems can automatically update the if then statements by how previous runs behave. They can learn from expert operators to learn new conditions. These learning and be presented to the operator as a suggesting on how the run the process. For well defined processes, the process will benefit from making the changes faster. These faster changes will improve the overall cost of manufacturing. In conclusion, AI is set to revolutionize the process industry by addressing its limitations and providing faster, more accurate, and cost-effective solutions. By harnessing the power of AI, the process industry is poised for a bright future. submitted by /u/Much-Bit3531 [link] [comments]

  • [N] Getty Images Claims Stable Diffusion Has Stolen 12 Million Copyrighted Images, Demands $150,000 For Each Image
    by /u/vadhavaniyafaijan (Machine Learning) on February 7, 2023 at 4:43 pm

    From Article: Getty Images new lawsuit claims that Stability AI, the company behind Stable Diffusion's AI image generator, stole 12 million Getty images with their captions, metadata, and copyrights "without permission" to "train its Stable Diffusion algorithm." The company has asked the court to order Stability AI to remove violating images from its website and pay $150,000 for each. However, it would be difficult to prove all the violations. Getty submitted over 7,000 images, metadata, and copyright registration, used by Stable Diffusion. submitted by /u/vadhavaniyafaijan [link] [comments]

  • [D] question on first time training a model
    by /u/cobalt1137 (Machine Learning) on February 7, 2023 at 4:41 pm

    Basically I saw a stream the other day where someone used data from a person's YouTube channel and somehow used this data to create an AI version of them and interviewed them. It was fascinating and pretty accurate How difficult would this be to do myself? I don't even know where to start. Does anyone have any pointers? Is this a very large task that I'm underestimating or is it actually feasible? Here is the stream in question. The video and audio would be cool to have but I mean that's not necessary, even just having the text aspect would be pretty wild on its own. https://youtu.be/hjoYy5IVtfo (skip to any point, most of it is filled with the bot responding) submitted by /u/cobalt1137 [link] [comments]

  • Model/paper ideas: reinforcement learning with a deterministic environment [D]
    by /u/EmbarrassedFuel (Machine Learning) on February 7, 2023 at 4:02 pm

    I have a problem I need to solve that, as far as I can tell, doesn't fit very well into most of the existing RL literature. Essentially the task is to create on optimal plan over a time horizon extending a flexible number of steps into the future. The action space is both discrete and continuous - there are multiple available distinct actions, some of which need to be given continuous (but constrained) parameters. In this problem however, the state of the environment is known ahead of time for all the future time steps, and the updated state of the agent after each action can be calculated deterministically given the action and the environment state. Modelling the entire problem as a MILP is not feasible due to the size of the action and state space, and we have a very large data set for agent and environment state to play with. Does anyone have any suggestions for papers or models that might be appropriate for this scenario? submitted by /u/EmbarrassedFuel [link] [comments]

  • [N] Beyond Transformers with PyNeuraLogic
    by /u/Lukas_Zahradnik (Machine Learning) on February 7, 2023 at 3:36 pm

    Going beyond Transformers? 🤖 In this article, I'm discussing how we can use the power of hybrid architecture, i.e., marrying deep learning with symbolic artificial intelligence, for implementing different kinds of Transformers. Including the one used in GPT-3! https://towardsdatascience.com/beyond-transformers-with-pyneuralogic-10b70cdc5e45 ​ ​ The attention computation graph visualized submitted by /u/Lukas_Zahradnik [link] [comments]

  • [Discussion] Best practices for taking deep learning models to bare metal MCUs
    by /u/ramv0001 (Machine Learning) on February 7, 2023 at 10:51 am

    I would like to know what are some of the best practice is to convert pytorch to embedded C (bare metal micro-controllers) during A. initial phase and B. for deployment. A. Initial phase is to understand the profiling of the model performance (RAM usage and processing time) for a targetted hardware. I understand that Tensorflow lite might be the best route for initial profiling but there are restrictions. It will be great if you could tell the framework that you follow. Currently framework: 1. Pytorch -> 2. ONNX -> 3. Keras -> 4. Tensorflowlite or 5. Tensorflowlite micro B. Deployment is to run inference for production in a targetted hardware. I think hand coding in C is the best way. Please ignore optimisation techniques in the workflow for simplicity. submitted by /u/ramv0001 [link] [comments]

  • [D] Papers that inject embeddings into LMs
    by /u/_Arsenie_Boca_ (Machine Learning) on February 7, 2023 at 8:16 am

    I am looking for papers that inject information into LMs directly using embeddings (without formatting information as text). I find it notoriously hard to search for these paper because they could come from various different domains, so I thought asking here might be a good option to reach people from many different domains. Some examples I already found are from the domain of knowledge graph augmented LMs: ERNIE https://arxiv.org/abs/1904.09223 K-BERT https://arxiv.org/abs/1909.07606 Prefix Tuning / Prompt Tuning are also somewhat similar to the idea, but they dont depend on any external information. Can you think of other papers that inject additional information into LMs via embeddings? submitted by /u/_Arsenie_Boca_ [link] [comments]

  • [P] Pythae 0.1.0 is out and supports distributed training for 25 Variational Autoencoders
    by /u/cchad-8 (Machine Learning) on February 7, 2023 at 6:53 am

    📢 News 📢 Pythae 0.1.0 is now out and supports distributed training using PyTorch DDP ! Train your favorite Variational Autoencoders (VAEs) faster 🏎️ and on larger datasets, still with a few lines of code 🖥️. 👉github: https://github.com/clementchadebec/benchmark_VAE 👉pypi: https://pypi.org/project/pythae/ https://preview.redd.it/jk4ukkgarpga1.png?width=1335&format=png&auto=webp&s=07c1ab2eaad104879637ad04472935d87baa31e9 submitted by /u/cchad-8 [link] [comments]

  • [P] ChatGPT without size limits: upload any pdf and apply any prompt to it
    by /u/aicharades (Machine Learning) on February 7, 2023 at 12:46 am

    hi all! I created a simple free tool where you can summarize and query documents of any size and estimate the cost to do so: https://www.wrotescan.com You can edit the prompts as well as automatically chunk and combine documents. There's also a cost estimator for any pdf you upload. Let me know if you want me to run some examples for you! Send me a pdf and tell me what you'd like summarized or extracted. Tips Please be sure to keep {text} in both prompts or the program will not input your document's text into the map reduce summarizer. {text} can only appear once in each prompt. It is where the text from each chunk to be summarized is input into the prompts. Create a temporary OpenAI key / org to use with this site so you do not have to provide credit card information then be sure to delete the temp key when you are done. Learnings Some interesting learnings I had while creating the tool: - Minimizing the number of steps through the AI improved summarization, so map reduce was often better than a more advanced refine workflow which passes the output through the model many more times. - LangChain is great for managing multiple step language model calls and bypassing the current limitations of ChatGPT submitted by /u/aicharades [link] [comments]

  • [Project] I used a new ML algo called "AnimeSR" to restore the Cowboy Bebop movie and up rez it to full 4K. Here's a link to the end result - honestly think it looks amazing! (Video and Model link in post)
    by /u/VR_Angel (Machine Learning) on February 6, 2023 at 9:39 pm

    It took me about 46 hours to run this on my 3080 at home. The original files was from the Blu-ray release that was unfortunately pretty poorly done in my opinion. This version really gives it new life I think. Here's a link to the video result to see for yourself: https://vimeo.com/796411232 And a link to the model I used! https://github.com/TencentARC/AnimeSR submitted by /u/VR_Angel [link] [comments]

  • Create powerful self-service experiences with Amazon Lex on Talkdesk CX Cloud contact center
    by Grazia Russo Lassner (AWS Machine Learning Blog) on February 6, 2023 at 9:01 pm

    This blog post is co-written with Bruno Mateus, Jonathan Diedrich and Crispim Tribuna at Talkdesk. Contact centers are using artificial intelligence (AI) and natural language processing (NLP) technologies to build a personalized customer experience and deliver effective self-service support through conversational bots. This is the first of a two-part series dedicated to the integration of

  • [N] Google: An Important Next Step On Our AI Journey
    by /u/EducationalCicada (Machine Learning) on February 6, 2023 at 8:12 pm

    https://blog.google/technology/ai/bard-google-ai-search-updates/ submitted by /u/EducationalCicada [link] [comments]

  • [N] Getty Images sues AI art generator Stable Diffusion in the US for copyright infringement
    by /u/Wiskkey (Machine Learning) on February 6, 2023 at 7:53 pm

    From the article: Getty Images has filed a lawsuit in the US against Stability AI, creators of open-source AI art generator Stable Diffusion, escalating its legal battle against the firm. The stock photography company is accusing Stability AI of “brazen infringement of Getty Images’ intellectual property on a staggering scale.” It claims that Stability AI copied more than 12 million images from its database “without permission ... or compensation ... as part of its efforts to build a competing business,” and that the startup has infringed on both the company’s copyright and trademark protections. This is different from the UK-based news from weeks ago. submitted by /u/Wiskkey [link] [comments]

  • Image classification model selection using Amazon SageMaker JumpStart
    by Kyle Ulrich (AWS Machine Learning Blog) on February 6, 2023 at 6:29 pm

    Researchers continue to develop new model architectures for common machine learning (ML) tasks. One such task is image classification, where images are accepted as input and the model attempts to classify the image as a whole with object label outputs. With many models available today that perform this image classification task, an ML practitioner may

  • Predict football punt and kickoff return yards with fat-tailed distribution using GluonTS
    by Tesfagabir Meharizghi (AWS Machine Learning Blog) on February 2, 2023 at 9:48 pm

    Today, the NFL is continuing their journey to increase the number of statistics provided by the Next Gen Stats Platform to all 32 teams and fans alike. With advanced analytics derived from machine learning (ML), the NFL is creating new ways to quantify football, and to provide fans with the tools needed to increase their

  • Analyze and visualize multi-camera events using Amazon SageMaker Studio Lab
    by Kevin Song (AWS Machine Learning Blog) on February 2, 2023 at 9:42 pm

    The National Football League (NFL) is one of the most popular sports leagues in the United States and is the most valuable sports league in the world. The NFL, BioCore, and AWS are committed to advancing human understanding around the diagnosis, prevention, and treatment of sports-related injuries to make the game of football safer. More

  • How to decide between Amazon Rekognition image and video API for video moderation
    by Lana Zhang (AWS Machine Learning Blog) on February 1, 2023 at 8:40 pm

    Almost 80% of today’s web content is user-generated, creating a deluge of content that organizations struggle to analyze with human-only processes. The availability of consumer information helps them make decisions, from buying a new pair of jeans to securing home loans. In a recent survey, 79% of consumers stated they rely on user videos, comments,

  • Scaling distributed training with AWS Trainium and Amazon EKS
    by Scott Perry (AWS Machine Learning Blog) on February 1, 2023 at 5:52 pm

    Recent developments in deep learning have led to increasingly large models such as GPT-3, BLOOM, and OPT, some of which are already in excess of 100 billion parameters. Although larger models tend to be more powerful, training such models requires significant computational resources. Even with the use of advanced distributed training libraries like FSDP and

  • Amazon SageMaker built-in LightGBM now offers distributed training using Dask
    by Xin Huang (AWS Machine Learning Blog) on January 30, 2023 at 6:10 pm

    Amazon SageMaker 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 tabular,

  • Build a water consumption forecasting solution for a water utility agency using Amazon Forecast
    by Dhiraj Thakur (AWS Machine Learning Blog) on January 30, 2023 at 5:59 pm

    Amazon Forecast is a fully managed service that uses machine learning (ML) to generate highly accurate forecasts, without requiring any prior ML experience. Forecast is applicable in a wide variety of use cases, including estimating supply and demand for inventory management, travel demand forecasting, workforce planning, and computing cloud infrastructure usage. You can use Forecast

  • [D] Simple Questions Thread
    by /u/AutoModerator (Machine Learning) on January 29, 2023 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]

  • Best Egg achieved three times faster ML model training with Amazon SageMaker Automatic Model Tuning
    by Tristan Miller (AWS Machine Learning Blog) on January 26, 2023 at 5:45 pm

    This post is co-authored by Tristan Miller from Best Egg. Best Egg is a leading financial confidence platform that provides lending products and resources focused on helping people feel more confident as they manage their everyday finances. Since March 2014, Best Egg has delivered $22 billion in consumer personal loans with strong credit performance, welcomed

  • Build a loyalty points anomaly detector using Amazon Lookout for Metrics
    by Dhiraj Thakur (AWS Machine Learning Blog) on January 25, 2023 at 4:19 pm

    Today, gaining customer loyalty cannot be a one-off thing. A brand needs a focused and integrated plan to retain its best customers—put simply, it needs a customer loyalty program. Earn and burn programs are one of the main paradigms. A typical earn and burn program rewards customers after a certain number of visits or spend.

  • Explain text classification model predictions using Amazon SageMaker Clarify
    by Pinak Panigrahi (AWS Machine Learning Blog) on January 25, 2023 at 4:13 pm

    Model explainability refers to the process of relating the prediction of a machine learning (ML) model to the input feature values of an instance in humanly understandable terms. This field is often referred to as explainable artificial intelligence (XAI). Amazon SageMaker Clarify is a feature of Amazon SageMaker that enables data scientists and ML engineers

  • Upscale images with Stable Diffusion in Amazon SageMaker JumpStart
    by Vivek Madan (AWS Machine Learning Blog) on January 25, 2023 at 4:09 pm

    In November 2022, we announced that AWS customers can generate images from text with Stable Diffusion models in Amazon SageMaker JumpStart. Today, we announce a new feature that lets you upscale images (resize images without losing quality) with Stable Diffusion models in JumpStart. An image that is low resolution, blurry, and pixelated can be converted

  • Cohere brings language AI to Amazon SageMaker
    by Sudip Roy (AWS Machine Learning Blog) on January 25, 2023 at 1:32 pm

    It’s an exciting day for the development community. Cohere’s state-of-the-art language AI is now available through Amazon SageMaker. This makes it easier for developers to deploy Cohere’s pre-trained generation language model to Amazon SageMaker, an end-to-end machine learning (ML) service. Developers, data scientists, and business analysts use Amazon SageMaker to build, train, and deploy ML models quickly and easily using its fully managed infrastructure, tools, and workflows.

  • ­­How CCC Intelligent Solutions created a custom approach for hosting complex AI models using Amazon SageMaker
    by Christopher Diaz (AWS Machine Learning Blog) on January 20, 2023 at 6:28 pm

    This post is co-written by Christopher Diaz, Sam Kinard, Jaime Hidalgo and Daniel Suarez  from CCC Intelligent Solutions. In this post, we discuss how CCC Intelligent Solutions (CCC) combined Amazon SageMaker with other AWS services to create a custom solution capable of hosting the types of complex artificial intelligence (AI) models envisioned. CCC is a

  • Set up Amazon SageMaker Studio with Jupyter Lab 3 using the AWS CDK
    by Cory Hairston (AWS Machine Learning Blog) on January 17, 2023 at 8:36 pm

    Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) partly based on JupyterLab 3. Studio provides a web-based interface to interactively perform ML development tasks required to prepare data and build, train, and deploy ML models. In Studio, you can load data, adjust ML models, move in between steps to adjust experiments,

  • Churn prediction using multimodality of text and tabular features with Amazon SageMaker Jumpstart
    by Xin Huang (AWS Machine Learning Blog) on January 17, 2023 at 6:49 pm

    Amazon SageMaker JumpStart is the Machine Learning (ML) hub of SageMaker providing pre-trained, publicly available models for a wide range of problem types to help you get started with machine learning. Understanding customer behavior is top of mind for every business today. Gaining insights into why and how customers buy can help grow revenue. Customer churn is

  • Leveraging artificial intelligence and machine learning at Parsons with AWS DeepRacer
    by Jenn Bergstrom (AWS Machine Learning Blog) on January 13, 2023 at 11:46 pm

    This post is co-written with Jennifer Bergstrom, Sr. Technical Director, ParsonsX. Parsons Corporation (NYSE:PSN) is a leading disruptive technology company in critical infrastructure, national defense, space, intelligence, and security markets providing solutions across the globe to help make the world safer, healthier, and more connected. Parsons provides services and capabilities across cybersecurity, missile defense, space ground

  • How Thomson Reuters built an AI platform using Amazon SageMaker to accelerate delivery of ML projects
    by Ramdev Wudali (AWS Machine Learning Blog) on January 13, 2023 at 5:26 pm

    This post is co-written by Ramdev Wudali and Kiran Mantripragada from Thomson Reuters. In 1992, Thomson Reuters (TR) released its first AI legal research service, WIN (Westlaw Is Natural), an innovation at the time, as most search engines only supported Boolean terms and connectors. Since then, TR has achieved many more milestones as its AI

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