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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.
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The App provides hundreds of quizzes and practice exam about:
– Machine Learning Operation on AWS
– Data Engineering
– Computer Vision,
– Exploratory Data Analysis,
– ML implementation & Operations
– Machine Learning Basics Questions and Answers
– Machine Learning Advanced Questions and Answers
– 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:
AWS Deep Learning AMIs (DLAMI)
Amazon Fraud Detector
Other Services and topics covered are:
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.
- [P] I trained an AI model on 120M+ songs from iTunesby /u/BullyMaguireJr (Machine Learning) on February 3, 2023 at 7:36 pm
Hey ML Reddit! I just shipped a project I’ve been working on called Maroofy: https://maroofy.com You can search for any song, and it’ll use the song’s audio to find other similar-sounding music. Demo: https://twitter.com/subby_tech/status/1621293770779287554 How does it work? I’ve indexed ~120M+ songs from the iTunes catalog with a custom AI audio model that I built for understanding music. My model analyzes raw music audio as input and produces embedding vectors as output. I then store the embedding vectors for all songs into a vector database, and use semantic search to find similar music! Here are some examples you can try: Fetish (Selena Gomez feat. Gucci Mane) — https://maroofy.com/songs/1563859943 The Medallion Calls (Pirates of the Caribbean) — https://maroofy.com/songs/1440649752 Hope you like it! This is an early work in progress, so would love to hear any questions/feedback/comments! 😀 submitted by /u/BullyMaguireJr [link] [comments]
- [N] Google Open Sources Vizier, Hyperparameter + Blackbox Optimization Service at Scaleby /u/enderlayer (Machine Learning) on February 3, 2023 at 4:33 pm
Github: https://github.com/google/vizier Google AI Blog: https://ai.googleblog.com/2023/02/open-source-vizier-towards-reliable-and.html Tweet from Zoubin Ghahramani: https://twitter.com/ZoubinGhahrama1/status/1621321675936768000?s=20&t=ZEuz9oSc_GWYxixtXDskqA submitted by /u/enderlayer [link] [comments]
- [R] editing colors on SHAP plot summaryby /u/lekayra (Machine Learning) on February 3, 2023 at 3:03 pm
We can change the colors of some texts and backgrounds on a SHAP summary plot by editing matplotlib's matplotlibrc file. We can also edit the plotting colors by passing a colormap but we're unable to change the colors of the "feature names" at the left side of the SHAP summary plot (beeswarm) -and the color of the y axis- by editing matplotlib's matplotlibrc file. Has anyone worked around this? Is there a way that we could overcome this restriction? submitted by /u/lekayra [link] [comments]
- [D] Using a public research dataset for "testing" NOT "training" a ML modelby /u/alzoubi36 (Machine Learning) on February 3, 2023 at 2:18 pm
Is it allowed to use a public dataset like the KITTI dataset to test a model trained for commercial use? Note that the KITTI dataset is only allowed to be used for research purposes and the model is trained with different data (company specific). submitted by /u/alzoubi36 [link] [comments]
- [D] Get log probs of a sentence using OpenAI APIs?by /u/Capable_Bumblebee645 (Machine Learning) on February 3, 2023 at 1:24 pm
Is there a way to use OpenAI APIs to get the log prob of a given sentence? I don't want new completions, I want to see how the model scores given sentences. submitted by /u/Capable_Bumblebee645 [link] [comments]
- [R] Graph Mixer Networksby /u/asarig_ (Machine Learning) on February 3, 2023 at 12:23 pm
I began exploring MLP-Mixer[1,2] on Graph Neural Networks in October 2021 and completed my implementation the ZINC dataset in November of the same year. My implementation is available on Github, but I was unable to fully conduct the experiments due to lack of computational resources. In December 2022, a group of leading figures in the field, including Xiaoxin He, Bryan Hooi, Thomas Laurent, Adam Perold, Yann Lecun, and Xavier Bresson, published a paper titled "A Generalization of ViT/MLP-Mixer to Graphs". Although I am pleased to be working alongside these prominent researchers on the application of MLP-Mixers to Graphs, I regret that I was unable to finish my experiments. Encouraged by my friends and advisors, I decided to make my work public by publishing it on arxiv. The paper and code can be found as the following: Paper/report: https://arxiv.org/abs/2301.12493 Github: https://github.com/asarigun/GraphMixerNetworks I used PNA as my baseline and did not utilize patches in my study, unlike the other study. I hope someone finds them interesting/useful. submitted by /u/asarig_ [link] [comments]
- [D] Understanding Vision Transformer (ViT) - What are the prerequisites?by /u/SAbdusSamad (Machine Learning) on February 3, 2023 at 11:51 am
Hello everyone, I'm interested in diving into the field of computer vision and I recently came across the concept of Vision Transformer (ViT). I want to understand this concept in depth but I'm not sure what prerequisites I need to have in order to grasp the concept fully. Do I need to have a strong background in Recurrent Neural Networks (RNNs) and Transformer (Attention Is All You Need) to understand ViT, or can I get by just knowing the basics of deep learning and Convolutional Neural Networks (CNNs)? I would really appreciate if someone could shed some light on this and provide some guidance. Thank you in advance! submitted by /u/SAbdusSamad [link] [comments]
- [R] [P] Noisy Sentences Datasetby /u/radi-cho (Machine Learning) on February 3, 2023 at 10:14 am
550K sentences in 5 European languages augmented with noise for training and evaluating spell correction tools or machine learning models. 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. Link: https://github.com/radi-cho/noisy-sentences-dataset submitted by /u/radi-cho [link] [comments]
- [d]? Is there a way to access youtube alphabetically or by id?by /u/loonathefloofyfox (Machine Learning) on February 3, 2023 at 6:55 am
I'm guessing i probably am not the first person who has wanted to work with youtube data so I'm hoping here is a good place to ask So i had an idea to make a neural network that would go through your youtube history and then train a neural network on it. Afterwards if there is a way to access all of youtube by id in a way that you can check every video then you could store all of the id for videos you might like and then use a youtube downloader like youtube-dl to download a certain amount. Was just a dumb idea i had but now i want to actually try it but I'm unsure if I'll actually be able to get the data i need to do it submitted by /u/loonathefloofyfox [link] [comments]
- [p] Is it possible to add more classes to an already trained resnet image classifier model without the need to retrain it in all dataset again? [p]by /u/YukkiiCode (Machine Learning) on February 3, 2023 at 3:47 am
[p] I am working on massive dataset, and in the future, we'll have to add some more classes over time, can I train the model in the only new classes?[p] submitted by /u/YukkiiCode [link] [comments]
- [Project] I built a minimal stateless ML project template built on my current favourite stackby /u/AntreasAntoniou (Machine Learning) on February 3, 2023 at 2:07 am
Dear r/MachineLearning, Hello everyone! I hope you are all out there having fun, training deep nets and generating fun story-telling with stable-diffusion! 🙂 I am here today to share with you all a minimal ml project template that I've recently built, which can be found at https://github.com/AntreasAntoniou/minimal-ml-template/. I became increasingly annoyed at how there weren't any repos out there that provided stateless ML project templates, which are absolutely necessary when using kubernetes on spot instances, and I decided to build one. By stateless I mean a repo that by default can store model weights in a remote repo and then download them to continue from where it left off if the previous machine dies. The result was this repository. The repo remains minimal and extremely readable, all while being packed with a cool stack that I use every day. I'd love to get some feedback, so have a look and let me know. Regards, Antreas P.S. A short summary straight from the Github Repo: This repo implements a minimal machine learning template, that is fully featured for most of the things a machine learning project might need. The most important parts that set this repo apart from the rest are: It is stateless. Any given experiment ran using this template, will, automatically and periodically stores the model weights and configuration to HuggingFace Hub and wandb respectively. As a result, if your machine dies or job exits, and you resume on another machine, the code will automatically locate and download the previous history and continue from where it left off. This makes this repo very useful when using spot instances, or using schedulers like slurm and kubernetes. It provides support for all the latest and greatest GPU and TPU optimization and scaling algorithms through HuggingFace Accelerate. It provides mature configuration support via Hydra-Zen and automates configuration generation via decorators implemented in this repo. It has a minimal callback based boilerplate that allows a user to easily inject any functionality at predefined places in the system without spagettifying the code. It uses HuggingFace Models and Datasets to streamline building/loading of models, and datasets, but is also not forcing you to use those, allowing for very easy injection of any models and datasets you care about, assuming you use models implemented under PyTorch's nn.Module and Dataset classes. It provides plug and play functionality that allows easy hyperparameter search on Kubernetes clusters using BWatchCompute and some readily available scripts and yaml templates. The Software Stack This machine learning project template is built using the following software stack: 1. Deep Learning Framework: PyTorch 2. Dataset storage and retrieval: Huggingface Datasets 3. Model storage and retrieval Huggingface Hub, and HuggingFace Models 4. GPU/TPU/CPU Optimization and Scaling up options library: Huggingface Accelerate 5. Experiment configuration + command line argument parsing: Hydra-zen 6. Experiment tracking: Weights and Biases 7. Simple python based ML experiment running with Kubernetes using BWatchCompute submitted by /u/AntreasAntoniou [link] [comments]
- Predict football punt and kickoff return yards with fat-tailed distribution using GluonTSby 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 Labby 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
- [P] Domestic Violence Datasetby /u/Naive-Aioli4849 (Machine Learning) on February 2, 2023 at 8:43 pm
Hi, I am working on project and for that I need a Twitter Domestic Violence Dataset. Basically I need a dataset with domestic violence tweets against woman. I have searched Kaggle and other websites but found no luck. Plus, I tried using Snscrape, but I need some phrases ideas related to domestic violence so I can get some tweets using that. I tried "Domestic Violence" , "My husband tried to kill me" and looking for more. Help is appreciated. submitted by /u/Naive-Aioli4849 [link] [comments]
- [p] I built an open source platform to deploy computationally intensive Python functions as serverless jobs, with no timeoutsby /u/seattleite849 (Machine Learning) on February 2, 2023 at 7:44 pm
Hi friends! I ran into this problem enough times at my last few jobs that I built a tool to solve it. I spent many hours building Docker containers for my Python functions, as many of the data science modules required building C libraries (since they significantly speed up compute-intensive routines, such as math calculations). Deploying the containers to AWS Lambda or Fargate (if the processes required more CPU or memory or were >15 minutes) and wiring functions to talk to each other using queues, databases, and blob storage made iterating on the actual code, which wasn't even that complex most of the time, slow. I made cakework https://github.com/usecakework/cakework, a platform that lets you spin up your Python functions as serverless, production-scale backends with a single command. Using the client SDK, you submit requests, check status, and get results. You can also specify the amount of CPU (up to 16 cores) and memory (up to 128GB) for each individual request, which is helpful when your data size and complexity varies across different requests. A common pattern that I built cakework for is doing file processing for ML: - ingest data from some source daily, or in response to an external event (data written to blob storage) - run my function (often using pandas/numpy/scipy) - write results to storage, update database - track failures and re-run/fix It's open source <3. Here are some fun examples to get you started: https://docs.cakework.com/examples Would love to hear your thoughts! submitted by /u/seattleite849 [link] [comments]
- [P] Time series outlier / anomaly detectionby /u/dudester_el (Machine Learning) on February 2, 2023 at 6:57 pm
I have traffic speed time series data for each day of the week over several months, with data samples about every 30 seconds. I'd like to find periods of time (subsequences) where the speed is much slower than usual. Any recommendations for algorithms that would be well suited to this problem? Thanks submitted by /u/dudester_el [link] [comments]
- [D] Querying with multiple vectors during embedding nearest neighbor search?by /u/mostlyhydrogen (Machine Learning) on February 2, 2023 at 5:33 pm
Are there tools or techniques that permit you to joint query using more than one query vector? Use case: iterative ANN search refinement, where I start with a seed vector, select matches, and re-query with more examples to improve the search results. I tried doing this with FAISS, but it performs a "batch query" that returns a separate set of results for each query vector (not a joint query). submitted by /u/mostlyhydrogen [link] [comments]
- [D] Do high leverage points affect Neural Net and Tree-based model?by /u/Temporary_Cap_2855 (Machine Learning) on February 2, 2023 at 4:23 pm
I know they can affect linear regression badly but given the fact that neural net and tree-based models can approximate non-linear complex functions, I don't think the high leverage points would be a problem. Just curious about your opinion whether my thinking makes sense submitted by /u/Temporary_Cap_2855 [link] [comments]
- [D] ImageNet normalization vs [-1, 1] normalizationby /u/netw0rkf10w (Machine Learning) on February 2, 2023 at 4:10 pm
For ImageNet classification, there are two common ways of normalizing the input images: - Normalize to [-1, 1] using an affine transformation (2*(x/255) - 1). - Normalize using ImageNet mean = (0.485, 0.456, 0.406) and std = (0.229, 0.224, 0.225). I observe that the first one is more common in TensorFlow codebases (including Jax models with TensorFlow data processing, e.g. the official Vision Transformers code), whereas the second is ubiquitous in PyTorch codebases. I tried to find empirical comparisons of the two, but there doesn't seem to be any. Which one is better in your opinion? I guess the performance shouldn't be too different, but still it's interesting to hear your experience. submitted by /u/netw0rkf10w [link] [comments]
- [N] Microsoft integrates GPT 3.5 into Teamsby /u/bikeskata (Machine Learning) on February 2, 2023 at 1:55 pm
Official blog post: https://www.microsoft.com/en-us/microsoft-365/blog/2023/02/01/microsoft-teams-premium-cut-costs-and-add-ai-powered-productivity/ Given the amount of money they pumped into OpenAI, it's not surprising that you'd see it integrated into their products. I do wonder how this will work in highly regulated fields (finance, law, medicine, education). submitted by /u/bikeskata [link] [comments]
- [D] Why do LLMs like InstructGPT and LLM use RL to instead of supervised learning to learn from the user-ranked examples?by /u/alpha-meta (Machine Learning) on February 2, 2023 at 1:13 pm
Aligned LLMs such as InstructGPT and ChatGPT are trained via supervised fine-tuning after the initial self-supervised pretraining. Then, the researchers train a reward model on responses ranked by humans. When I understand correctly, they let the LLM generate responses that humans have to rank on a scale from 1-5. Then, they train a reward model (I suppose in supervised fashion?) on these ranked outputs. Once that's done, they use reinforcement learning (RL) with proximal policy optimization (PPO) to update the LLM. My question is why they use RL with PPO for this last step? Why don't they fine-tune the LLM using regular supervised learning, whereas the human-ranked outputs represent the labels. Since these are labels in the range 1-5, this could be a ranking or ordinal regression loss for supervised learning. submitted by /u/alpha-meta [link] [comments]
- [D] Commercial Use of a Model that has been trained using Human3.6Mby /u/mfarahmand98 (Machine Learning) on February 2, 2023 at 12:58 pm
I wanted to use the Learnable Trainangulation model in a commercial project. The source code itself is under MIT licensing. However, the dataset they have used is Human3.6M, which states that the license is "FREE OF CHARGE FOR ACADEMIC USE ONLY". Yet, recent court rulings (in the US) state that models can use copyrighted data during training, and the results are no longer bound by that copyright (e.g. Google Books). Does the same apply here? submitted by /u/mfarahmand98 [link] [comments]
- [D] Global Optimum of K-Means Cost Functionby /u/healthymonkey100 (Machine Learning) on February 2, 2023 at 10:13 am
I've recently started reading up on classical ML and I got a question about K-Means. More concretely, I am confused about the uniqueness of the global optimal solution of K-Means's cost function. Let's state the problem formally below, extracted from Bishop's Pattern Recognition and Machine Learning book, exercise 9.1. Consider the 𝐾-means algorithm discussed in Section 9.1. Show that as a consequence of there being a finite number of possible assignments for the set of discrete indicator variables 𝑟𝑛𝑘, and that for each such assignment there is a unique optimum for the 𝝁𝑘, the K-means algorithm must converge after a finite number of iterations. I made an answer [here](https://stats.stackexchange.com/questions/603327/question-on-the-proof-of-convergence-of-k-means) detailing the proof of why it does converge in Lloyd's algorithm, but I think I still do not understand why Lloyd's do not converge to a global minimum, which mathematical theorem/understanding am I missing here? I think that optimizing both the assignments and the centroids of K-Means at the same time is non-convex and hence there are many local minimums, we can use brute force to search for the global minimum but of course it is exponential to the number of data points. On the other hand, Lloyd optimizes it (greedily) alternatively, and hence you will find the cost functions' local minima (guaranteed)? submitted by /u/healthymonkey100 [link] [comments]
- [P] [R] A simplistic UI to edit images with Stable Diffusion and InstructPix2Pixby /u/radi-cho (Machine Learning) on February 2, 2023 at 10:06 am
https://preview.redd.it/ut4us5251rfa1.png?width=2000&format=png&auto=webp&s=dbf79c3832b20287203faa97e5c1303472bdbc22 Currently, the UI supports a picture upload and uses InstructPix2Pix to edit it. Also, it uses upscaling models for quality enhancements. More models are coming soon. The goal is to provide a way for non-ML people to use diffusion-based image editing through simplistic app design. Web demo: https://diffground.com/ submitted by /u/radi-cho [link] [comments]
- [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]
- [N] OpenAI starts selling subscriptions to its ChatGPT botby /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]
- How to decide between Amazon Rekognition image and video API for video moderationby 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,
- [R] Extracting Training Data from Diffusion Modelsby /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]
- Scaling distributed training with AWS Trainium and Amazon EKSby 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 Daskby 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 Forecastby 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 Threadby /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 Tuningby 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 Metricsby 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 Clarifyby 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 JumpStartby 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 SageMakerby 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 SageMakerby 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 CDKby 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 Jumpstartby 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 DeepRacerby 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 projectsby 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 2by 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
- Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 1by Olivia Choudhury (AWS Machine Learning Blog) on January 13, 2023 at 5:22 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 any single site for rare events, regulatory guidelines that prohibit data sharing, infrastructure requirement, and cost incurred in creating a
- Multilingual customer support translation made easy on Salesforce Service Cloud using Amazon Translateby 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 Comprehendby Andy Whittle (AWS Machine Learning Blog) on January 12, 2023 at 5:46 pm
This is guest post by Andy Whittle, Principal Platform Engineer – Application & Reliability Frameworks at The Very Group. At The Very Group, which operates digital retailer Very, security is a top priority in handling data for millions of customers. Part of how The Very Group secures and tracks business operations is through activity logging
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