AWS Machine Learning Certification Specialty Exam Prep

AWS Machine Learning Specialty Certification Prep (Android)

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

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

Download AWS machine Learning Specialty Exam Prep App on iOs

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

AWS MLS-C01 Machine Learning Specialty Exam Prep PRO

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AWS machine learning certification prep
AWS machine learning certification prep

Download AWS machine Learning Specialty Exam Prep App on iOs

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

The App provides hundreds of quizzes and practice exam about:

– Machine Learning Operation on AWS

– Modelling

– Data Engineering

– Computer Vision,

– Exploratory Data Analysis,

– ML implementation & Operations

– Machine Learning Basics Questions and Answers

– Machine Learning Advanced Questions and Answers

– Scorecard

– Countdown timer

– Machine Learning Cheat Sheets

– Machine Learning Interview Questions and Answers

– Machine Learning Latest News

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

Domain 1: Data Engineering

Create data repositories for machine learning.

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

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

Identify and implement a data ingestion solution.

Data job styles/types (batch load, streaming)

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

Domain 2: Exploratory Data Analysis

Sanitize and prepare data for modeling.

Perform feature engineering.

Analyze and visualize data for machine learning.

Domain 3: Modeling

Frame business problems as machine learning problems.

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

Train machine learning models.

Perform hyperparameter optimization.

Evaluate machine learning models.

Domain 4: Machine Learning Implementation and Operations

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

tolerance.

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

problem.

Apply basic AWS security practices to machine learning solutions.

Deploy and operationalize machine learning solutions.

Machine Learning Services covered:

Amazon Comprehend

AWS Deep Learning AMIs (DLAMI)

AWS DeepLens

Amazon Forecast

Amazon Fraud Detector

Amazon Lex

Amazon Polly

Amazon Rekognition

Amazon SageMaker

Amazon Textract

Amazon Transcribe

Amazon Translate

Other Services and topics covered are:

Ingestion/Collection

Processing/ETL

Data analysis/visualization

Model training

Model deployment/inference

Operational

AWS ML application services

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

Notebooks and integrated development environments (IDEs),

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

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

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

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

Download AWS machine Learning Specialty Exam Prep App on iOs

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

  • [D] Apple's ane-transformers - experiences?
    by /u/alkibijad (Machine Learning) on February 2, 2023 at 12:04 am

    I'm using Huggingface's transformers regularly for experimentations, but I plan to deploy some of the models to iOS. I have found ml-ane-transformers repo from Apple, which shows how transformers can be rewritten to have much better performance on Apple's devices. There's an example of DistilBERT implemented in that optimized way. As I plan to deploy transformers to iOS, I started thinking about this. I'm hoping some already have experience about this, so we can discuss: Has anyone tried this themselves? Do they actually see the improvements in performance on iOS? I'm using Huggingface's transformer models in my experiments. How much work do you think there is to rewrite model in this optimized way? It's very difficult to train transformers from scratch (especially if they're big 🙂 ), so I'm fine-tuning on top of pre-trained models on Huggingface. Is it possible to use weights from pretrained Huggingface models with the Apple's reference code? How difficult is it? submitted by /u/alkibijad [link] [comments]

  • [D] Any open source model, or application to remove no speech parts of a video?
    by /u/CeFurkan (Machine Learning) on February 1, 2023 at 11:08 pm

    Currently I am using Davinci Resolve free edition to manually cut / remove no speech parts, or the parts where I take a breath It is extremely time consuming I am pretty sure this can be done via AI For example whisper is able to detect where we use filler words such as umh, um, uh etc That would be awesome to automatically remove these parts from a video Just direct me where to look thank you submitted by /u/CeFurkan [link] [comments]

  • [N] OpenAI starts selling subscriptions to its ChatGPT bot
    by /u/bikeskata (Machine Learning) on February 1, 2023 at 9:59 pm

    https://www.axios.com/2023/02/01/chatgpt-subscriptions-chatbot-openai Not fully paywalled, but there's a tiering system. submitted by /u/bikeskata [link] [comments]

  • [D] Normalizing Flows in 2023?
    by /u/wellfriedbeans (Machine Learning) on February 1, 2023 at 9:28 pm

    What is the state of research in normalizing flows in 2023? Have they been superseded by diffusion models for sample generation? If so, what are some other applications where normalizing flows are still SOTA (or even useful)? submitted by /u/wellfriedbeans [link] [comments]

  • [D] Advice for a multi-label classification problem
    by /u/dle88 (Machine Learning) on February 1, 2023 at 8:49 pm

    Hi guys, I have a dataset of 12,000 products, each of which consists of a title, description, and some images. In addition, I also have a pre-defined set of product categories. Curious to learn if anyone has any suggestions on what model to be used to train using this dataset as input to classify each product in the dataset into the related categories within the given set? submitted by /u/dle88 [link] [comments]

  • 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,

  • [D] Why is stable diffusion much smaller than predecessors?
    by /u/dahdarknite (Machine Learning) on February 1, 2023 at 8:39 pm

    Stable diffusion seems to be a departure from the trend of building larger and larger models. It has 10x less parameters than other image generation models like DALLE-2. “Incredibly, compared with DALL-E 2 and Imagen, the Stable Diffusion model is a lot smaller. While DALL-E 2 has around 3.5 Billion parameters, and Imagen has 4.6 Billion, the first Stable Diffusion model has just 890 million parameters, which means it uses a lot less VRAM and can actually be run on consumer-grade graphics cards.” What allows stable diffusion to work so well with a lot less parameters? Are there any drawbacks to this, like requiring stable diffusion to be fine tuned more than DALLE-2 for example? submitted by /u/dahdarknite [link] [comments]

  • [R] Extracting Training Data from Diffusion Models
    by /u/pm_me_your_pay_slips (Machine Learning) on February 1, 2023 at 8:29 pm

    https://twitter.com/eric_wallace_/status/1620449934863642624?s=46&t=GVukPDI7944N8-waYE5qcw Extracting training data from diffusion models is possible by following, more or less, these steps: Compute CLIP embeddings for the images in a training dataset. Perform an all-pairs comparison and mark the pairs with l2 distance smaller than some threshold as near duplicates Use the prompts for training samples marked as near duplicates to generate N synthetic samples with the trained model Compute the all-pairs l2 distance between the embeddings of generated samples for a given training prompt. Build a graph where the nodes are generated samples and an edge exists if the l2 distance is less than some threshold. If the largest clique in the resulting graph is of size 10, then the training sample is considered to be memorized. Visually inspect the results to determine if the samples considered to be memorized are similar to the training data samples. With this method, the authors were able to find samples from Stable Diffusion and Imagen corresponding to copyrighted training images. submitted by /u/pm_me_your_pay_slips [link] [comments]

  • [D] Vectorizing computation of the Jaccard similarity between all instances in a large dataset in Python
    by /u/hopedallas (Machine Learning) on February 1, 2023 at 7:45 pm

    I am trying to claculate the Jaccard similarity between all instances in my dataframe. I am using the following method to do so, however, this method is painfully slow. My ```data_with_labels``` shape is (221277, 217). # Compute the Jaccard similarity between all instances n_instances = data_with_labels.shape[0] jaccard_similarity_matrix = np.zeros((n_instances, n_instances)) for i in range(n_instances): for j in range(n_instances): jaccard_similarity_matrix[i, j] = jaccard_score(data_with_labels[i, :], data_with_labels[j, :], average='micro') ​ Is there any way to do this process with numpy vectorization? I tried soomething like this but keep getting this error: n_instances = data_with_labels.shape[0] jaccard_similarity_matrix = np.zeros((n_instances, n_instances)) for i in range(n_instances): jaccard_similarity_matrix[i, :] = jaccard_score(data_with_labels[i, :], data_with_labels, average='micro') ValueError: Found input variables with inconsistent numbers of samples: [217, 221277] ​ submitted by /u/hopedallas [link] [comments]

  • [R] On the Expressive Power of Geometric Graph Neural Networks
    by /u/chaitjo (Machine Learning) on February 1, 2023 at 7:07 pm

    Geometric GNNs are an emerging class of GNNs for spatially embedded graphs in scientific and engineering applications, s.a. biomolecular structure, material science, and physical simulations. Notable examples include SchNet, DimeNet, Tensor Field Networks, and E(n) Equivariant GNNs. How powerful are geometric GNNs? How do key design choices influence expressivity and how to build maximally powerful ones? Check out this recent paper for more: 📄 PDF: http://arxiv.org/abs/2301.09308 💻 Code: http://github.com/chaitjo/geometric-gnn-dojo 💡Key findings: https://twitter.com/chaitjo/status/1617812402632019968 P.S. Are you new to Geometric GNNs, GDL, PyTorch Geometric, etc.? Want to understand how theory/equations connect to real code? Try this Geometric GNN 101 notebook before diving in: https://github.com/chaitjo/geometric-gnn-dojo/blob/main/geometric_gnn_101.ipynb submitted by /u/chaitjo [link] [comments]

  • [P] An open source tool for repeatable PyTorch experiments by embedding your code in each model checkpoint
    by /u/latefordinnerstudios (Machine Learning) on February 1, 2023 at 6:16 pm

    I made a new open source tool called JellyML that lets you go back to any of your checkpoints, and reproduce your code exactly as it was when you trained it. You can find the website here: https://jellyml.com The GitHub repo: https://gitHub.com/mmulet/jellyml You can install it with pip: pip install jellyml submitted by /u/latefordinnerstudios [link] [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

  • [D] What does a DL role look like in ten years?
    by /u/PassingTumbleweed (Machine Learning) on February 1, 2023 at 4:55 pm

    Every day, there seems to be new evidence of the generalization capabilities of LLMs. What does this mean for the future role of deep learning experts in academia and business? It seems like there's a significant chance that skills such as PyTorch and Jax will be displaced by prompt construction and off-the-shelf model APIs, with only a few large institutions working on the DNN itself. Curious to hear others' thoughts on this. submitted by /u/PassingTumbleweed [link] [comments]

  • [D] Tortoise TTS API for GPT-3.
    by /u/akshaysri0001 (Machine Learning) on February 1, 2023 at 3:37 pm

    Hey everyone, I thought of an idea to create a human like realistic voice assistant for ChatGPT. So I have a question that can we make an API of tortoise TTS trained on a specific voice. I've seen a lot of companies nowadays that provides most realistic text to speech solutions like eleven labs etc. Do they train these voices on tortoise TTS?? If there is another way of creating highly realistic voices and make an API of it, then please tell me how can I do it? And also how can I make this process fast as regular normal TTS? submitted by /u/akshaysri0001 [link] [comments]

  • [P] predictive modeling- Multi stage classification
    by /u/R-PRADY (Machine Learning) on February 1, 2023 at 3:21 pm

    Problem statement: assume a user come into a system and it typically takes 10 weeks for outcome(yes,no). I want to build a model which predicts the outcome on any particular week say how likely are they gonna succeed on week 1,2,3 etc. Question on model building approach: should I build weekly models and get the prediction ? Or is there a better way to do it. Ideally it would be great have single model that can be used for different weeks. I prefer the latter. Appreciate your ideas submitted by /u/R-PRADY [link] [comments]

  • [P] NER output label post processing
    by /u/hasiemasie (Machine Learning) on February 1, 2023 at 1:51 pm

    I’m looking to some aggregation on academic research and news articles to see what insights I get from it. I’m using textrazor to do named entity recognition on the documents, but getting a lot of dirty labels that have slightly different wording. For example, Tesla, Tesla ltd, Tesla Ltd. As a result, my aggregations have a lot of duplicate results. The dataset consists of about 4M labels so the solution has to be efficient to be viable. I was thinking of putting the labels through word2vec and then clustering them based on the word embedding distances? But then the problem arises of how many clusters to use? I’ve also tried simple regex preprocessing to get rid of the company abbreviations but there are other examples that cannot be solved that easily. submitted by /u/hasiemasie [link] [comments]

  • [D] A report that compares the practices of high-performing companies in Europe to laggards in AI adoption
    by /u/madnessone1 (Machine Learning) on February 1, 2023 at 1:50 pm

    Discussion about a report that compares the practices and attitudes of companies that self-report as ahead of the competition in AI adoption in Europe, compared to companies that identify as behind or at the same stage as their competitors. It contains some interesting findings mixed with some somewhat obvious things. Kinda obvious that leading companies also are further ahead in using MLOps, but I thought it was interesting to see the frequency of fine-tuning and retraining. Not as obvious that most companies report a lack of access to training data, would have thought that is mostly something that smaller companies have issues with. Also not so obvious to me is that companies with a centralized decision-making related to AI seem to dominate among high-performers. Interesting that most companies seem to get some value out of their AI/ML projects, which seems to contradict some of the previous forecasts by the big consultancy companies. Link to the report: https://stagezero.ai/2022-survey-report/ submitted by /u/madnessone1 [link] [comments]

  • [R] EMNLP video interviews, workshops, and posters
    by /u/jayalammar (Machine Learning) on February 1, 2023 at 1:49 pm

    I learned a lot at EMNLP in December and captured some of what I learned in this video. Interviews I asked five NLP researchers these questions: 1- What is the most exciting development in NLP in 2022 2- What are you looking forward to in 2023? 3- What is an underrated idea that the field should pay more attention to? Their answers start at 01:22. Workshops I got to spend time at these workshops: Generation, Evaluation & Metrics (GEM) Massively Multilingual NLU Blackbox NLP My main takeaways are at 09:25. Posters If you've been to a conference you'd know there's an overwhelming number of posters. I recorded four of the ones I came across and thought were interesting (covering retrieval-augmented text generation, human evaluation, the BLOOM multimodal dataset, and a multimodal method to name music playlists). Poster presentations start at 14:38 Full video: https://www.youtube.com/watch?v=plCvF_7qrmY ​ What's your answer to these questions? 1- What is the most exciting development in NLP in 2022 2- What are you looking forward to in 2023? 3- What is an underrated idea that the field should pay more attention to? ​ submitted by /u/jayalammar [link] [comments]

  • [P] A CLI tool for easy transformer sequence classifier training and inference
    by /u/0xideas (Machine Learning) on February 1, 2023 at 9:36 am

    Hi everyone, I have developed a CLI tool to train a transformer sequence classification model. There are also options for preprocessing data and inference on new data. I was thinking that interesting use cases might be found within economics/finance and biological domains, and would be super interested in feedback on: - if the documentation is intelligible and enables you to use it - to which use cases from your industry/domain could discrete sequence modelling be applied - what additional features you'd need for it to be useful to you Basically, where would the prediction of a class (or the next item) based on discrete events/objects/tokens be useful? The project is called "sequifier" and can be found here: https://github.com/0xideas/sequifier submitted by /u/0xideas [link] [comments]

  • [P] Self Hostable OpenAI Alternative
    by /u/leepenkman (Machine Learning) on February 1, 2023 at 8:22 am

    Hi, Text-Generator.io is now self hostable, It's priced at $1000 USD per instance per year to self host. The service runs on a single 24GB VRAM GPU, and runs all services including speech to text, text and code generation for almost all languages and generating embeddings too. The text generator also downloads and analyses any input with links including documents, images, images with text inside and webpages for better understanding and to generate better text. It's a great alternative to OpenAI and has a compatible API making switching easy. You can check out the new pricing here. Let me know what you think and if there's anything i can do to help! All the best. Lee Penkman - Founder Text-Generator.io submitted by /u/leepenkman [link] [comments]

  • [R] SETI finds eight potential alien signals with ML
    by /u/logTom (Machine Learning) on February 1, 2023 at 5:48 am

    GitHub (sadly without weights). https://github.com/PetchMa/ML_GBT_SETI News. https://www-scinexx-de.translate.goog/news/kosmos/seti-findet-acht-potenzielle-alien-signale/?_x_tr_sl=de&_x_tr_tl=en&_x_tr_hl=de&_x_tr_pto=wapp submitted by /u/logTom [link] [comments]

  • [R] Faithful Chain-of-Thought Reasoning
    by /u/starstruckmon (Machine Learning) on February 1, 2023 at 2:01 am

    Paper : https://arxiv.org/abs/2301.13379 Abstract : While Chain-of-Thought (CoT) prompting boosts Language Models' (LM) performance on a gamut of complex reasoning tasks, the generated reasoning chain does not necessarily reflect how the model arrives at the answer (aka. faithfulness). We propose Faithful CoT, a faithful-by-construction framework that decomposes a reasoning task into two stages: Translation (Natural Language query → symbolic reasoning chain) and Problem Solving (reasoning chain → answer), using an LM and a deterministic solver respectively. We demonstrate the efficacy of our approach on 10 reasoning datasets from 4 diverse domains. It outperforms traditional CoT prompting on 9 out of the 10 datasets, with an average accuracy gain of 4.4 on Math Word Problems, 1.9 on Planning, 4.0 on Multi-hop Question Answering (QA), and 18.1 on Logical Inference, under greedy decoding. Together with self-consistency decoding, we achieve new state-of-the-art few-shot performance on 7 out of the 10 datasets, showing a strong synergy between faithfulness and accuracy. submitted by /u/starstruckmon [link] [comments]

  • [D] Audio segmentation - Machine Learning algorithm to segment a audio file into multiple class
    by /u/PlayfulMenu1395 (Machine Learning) on February 1, 2023 at 12:25 am

    Can someone suggest a machine learning model that will segment audio spectrogram to multiple classes. I have labeled data of heart beats. S1, S2, systole and diastole. How to train a segmentation model ? submitted by /u/PlayfulMenu1395 [link] [comments]

  • [R] Are there any Machine Learning Journals that accept Viewpoint Papers (~1500+ words)?
    by /u/Adi-Dewan (Machine Learning) on January 31, 2023 at 11:54 pm

    Basically the title. I have a sequence of two papers - a viewpoint and a complete paper in the works - that I'm looking to submit, the viewpoint outlining the theoretical premise for the latter. I've currently had no luck finding any ML-specific journals that allow viewpoint submissions (with the exception of simply posting to arXiv), and was wondering if anyone here was familiar with any. Thanks 😀 submitted by /u/Adi-Dewan [link] [comments]

  • Introducing NoRef-ER: A Multi-Language Referenceless ASR Metric (on HuggingFace) [R] [P]
    by /u/k_yuksel (Machine Learning) on January 31, 2023 at 7:45 pm

    I am proud to announce the release of NoRefER, a multi-language referenceless ASR metric based on a fine-tuned language model, for public use on HuggingFace. This metric allows for evaluating the outputs of ASR models without needing a reference transcript, making it a valuable tool for a/b testing multiple ASR models or model versions, or even ensembling their outputs. ASR is an important technology with various applications, but the quality of ASR systems can vary greatly. It's important to accurately evaluate and compare the performance of different ASR models, traditionally done using reference-based ASR quality evaluation metrics. However, obtaining those ground-truth transcriptions from human annotators is time-consuming and costly. Referenceless quality evaluation is becoming important as it allows for comparing ASR models on a level playing field, regardless of the quality or existence of a reference transcript. Just as referenceless evaluation has become crucial in Machine Translation with the introduction of referenceless metrics like COMET-QE, referenceless evaluation will play an important role in ASR. To fine-tune NoRefER for referenceless ASR quality evaluation, a contrastive-learning technique is employed with an innovative self-supervision method exploiting the known quality relationships in-between multiple compression levels of the same ASR, rather than human supervision on the quality of the ASR outputs obtained via transcriptions or evaluations of the annotators. Potential use-cases for NoRefER include: - A/B testing models or their versions to determine the best-performing one - Picking production outputs worth for the human-evaluation or post-editing - Ensemble the outputs of multiple ASR models to achieve a superior quality What's more, these abilities will be accessible in aiXplain no-code products, making it easy for anyone to use and benefit from this ASR quality estimation metric. We are excited to see how NoRefER will be used in the ASR community and would love to hear your thoughts and feedback. Try it out on HuggingFace and see how it can help diagnose and improve your ASR models! submitted by /u/k_yuksel [link] [comments]

  • [D] Have researchers given up on traditional machine learning methods?
    by /u/fujidaiti (Machine Learning) on January 31, 2023 at 9:18 am

    This may be a silly question for those familiar with the field, but don't machine learning researchers expect any more prospects for traditional methods (I mean, "traditional" is other than deep learning)? I feel that most of the time when people talk about machine learning in the world today, they are referring to deep learning, but is this the same in the academic world? Have people who have been studying traditional methods switched to neural networks? I know that many researchers are excited about deep learning, but I am wondering what they think about other methods. [ EDITED ] I’m glad that I got far more responses than I expected! However, I would like to add here that my intention did not seem to come across to some people because of my inaccurate English. I think “have given up" was poorly phrased. What I really meant to say was, are ML researchers no longer interested in traditional ML? Have those who studied, say, SVM moved on to DL field? That was my point, but u/qalis gave me a good comment on it. Thanks to all the others. submitted by /u/fujidaiti [link] [comments]

  • [P] I launched “CatchGPT”, a supervised model trained with millions of text examples, to detect GPT created content
    by /u/qthai912 (Machine Learning) on January 30, 2023 at 7:09 pm

    I’m an ML Engineer at Hive AI and I’ve been working on a ChatGPT Detector. Here is a free demo we have up: https://hivemoderation.com/ai-generated-content-detection From our benchmarks it’s significantly better than similar solutions like GPTZero and OpenAI’s GPT2 Output Detector. On our internal datasets, we’re seeing balanced accuracies of >99% for our own model compared to around 60% for GPTZero and 84% for OpenAI’s GPT2 Detector. Feel free to try it out and let us know if you have any feedback! submitted by /u/qthai912 [link] [comments]

  • 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,

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    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

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    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

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    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

  • Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 2
    by Vidya Sagar Ravipati (AWS Machine Learning Blog) on January 13, 2023 at 5:23 pm

    This blog post is co-written with Chaoyang He and Salman Avestimehr from FedML. Analyzing real-world healthcare and life sciences (HCLS) data poses several practical challenges, such as distributed data silos, lack of sufficient data at a single site for rare events, regulatory guidelines that prohibit data sharing, infrastructure requirement, and cost incurred in creating a

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    This blog post is co-written with Chaoyang He and Salman Avestimehr from FedML. Analyzing real-world healthcare and life sciences (HCLS) data poses several practical challenges, such as distributed data silos, lack of sufficient data at any single site for rare events, regulatory guidelines that prohibit data sharing, infrastructure requirement, and cost incurred in creating a

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    by Mark Lott (AWS Machine Learning Blog) on January 12, 2023 at 5:51 pm

    This post was co-authored with Mark Lott, Distinguished Technical Architect, Salesforce, Inc. Enterprises that operate globally are experiencing challenges sourcing customer support professionals with multi-lingual experience. This process can be cost-prohibitive and difficult to scale, leading many enterprises to only support English for chats. Using human interpreters for translation support is expensive, and infeasible since

  • Redacting PII data at The Very Group with Amazon Comprehend
    by Andy Whittle (AWS Machine Learning Blog) on January 12, 2023 at 5:46 pm

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    by Marios Skevofylakas (AWS Machine Learning Blog) on January 11, 2023 at 7:03 pm

    This post is co-authored by Marios Skevofylakas, Jason Ramchandani and Haykaz Aramyan from Refinitiv, An LSEG Business. Financial service providers often need to identify relevant news, analyze it, extract insights, and take actions in real time, like trading specific instruments (such as commodities, shares, funds) based on additional information or context of the news item.

  • Best practices for load testing Amazon SageMaker real-time inference endpoints
    by Marc Karp (AWS Machine Learning Blog) on January 10, 2023 at 5:35 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. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so

Download AWS machine Learning Specialty Exam Prep App on iOs

AWS machine learning certification prep
AWS machine learning certification prep

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

Download AWS machine Learning Specialty Exam Prep App on iOs

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

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