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],[P] Doubt regarding Sentence Transforrmers Library
    by /u/nani_procastinator (Machine Learning) on June 16, 2024 at 3:16 pm

    Hi, my job is to create sentence embeddings for different LLMS like BERT, Gemma, Llama etc. I was wondering whether I could use Sentence Transformers library for the same. However, on further deep-dive I saw that it has only few pretrained models for the same. Can anyone provide clarification on the same? Thanks, in advance. https://preview.redd.it/6b8uq48day6d1.png?width=1431&format=png&auto=webp&s=d4d5c0ab164d0ebdf97f2a61303a5fee7167968d submitted by /u/nani_procastinator [link] [comments]

  • [D] ECAI 2024 Reviews Discussion
    by /u/Fun_Equal5145 (Machine Learning) on June 16, 2024 at 3:10 pm

    Discussion thread for ECAI 2024 reviews. submitted by /u/Fun_Equal5145 [link] [comments]

  • [D] Simple Questions Thread
    by /u/AutoModerator (Machine Learning) on June 16, 2024 at 3:00 pm

    Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]

  • [D] How you will solve dynamic resolution problem in CV?
    by /u/Dathvg (Machine Learning) on June 16, 2024 at 2:13 pm

    I'm currently working on problem that requires precise object detection on really weird resolutions (varying from 1:32 to 32:1 aspect ratio and up to 30000 on each side). Objects though generally around ~120 pixels on both sides, which means that in models like ViT during training size of object is less than one pixel (of 224 pixel square), and for YOLO & friends it's not much better. I have pretty large dataset of those images and manually crop them is very ineffective and stupid because no one will crop them in prod. My background is mostly NLP with transformers, and i thought that you can use dynamic patch count with padding, but i can't find anything similar on hf vit parameters. Is there any sane way to deal with this kind of task? Solutions for which i can swap head to use embeddings for different task are highly appreciated. submitted by /u/Dathvg [link] [comments]

  • [P] Instruction Finetuning From Scratch Implementation
    by /u/seraschka (Machine Learning) on June 16, 2024 at 1:14 pm

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

  • [D] Suggesting which RAG method will work best for you, based on your use case 🔎📑
    by /u/sarthakai (Machine Learning) on June 16, 2024 at 1:10 pm

    Most RAG apps use Dense Passage Retrieval to find relevant docs. But there are better methods: RAG-Token: It generates each token by considering different docs and chooses the most probable token at each step. So that every part of the answer is influenced by the best possible context. RAG-Sequence: It calculates the probability of each answer and selects the one with the highest combined probability, getting you the best possible answer based on multiple sources. It’s a lot like RAG-token but less granular. Fusion-in-Decoder (FiD): It encodes all pairs of questions and chunks in parallel and then combines these encodings before feeding them into the decoder, which generates the answer step-by-step. Graph RAG: In case your documents are highly interconnected, the links between them are probably important to generate a relevant response. Search results from Graph RAG are more likely to give you a comprehensive view of the entity being searched and the info connected to it. I spent the weekend creating a Python library which automatically creates this graph for the documents present in your vectordb. It also makes it easy for you to retrieve relevant documents connected to the best matches. Currently testing the library on medical documents to gauge its performance. Sharing version 0.1 tomorrow! You can follow my social media to stay tuned: https://linktr.ee/sarthakrastogi submitted by /u/sarthakai [link] [comments]

  • [D] What is the progress on label noise in 2024?
    by /u/EducationalOwl6246 (Machine Learning) on June 16, 2024 at 11:36 am

    Will LLM be affected by noisy labels? Or can LLM solve the problem of noisy labels? Do you think this study is still worthwhile? submitted by /u/EducationalOwl6246 [link] [comments]

  • [R] Understanding LoRA: A visual guide to Low-Rank Approximation for fine-tuning LLMs efficiently. 🧠
    by /u/ml_a_day (Machine Learning) on June 16, 2024 at 10:34 am

    TL;DR: LoRA is Parameter-Efficient Fine-Tuning (PEFT) method. It addresses the drawbacks of previous fine-tuning techniques by using low-rank adaptation, which focuses on efficiently approximating weight updates. This significantly reduces the number of parameters involved in fine-tuning by 10,000x and still converges to the performance of a fully fine-tuned model. This makes it cost, time, data, and GPU efficient without losing performance. What is LoRA and Why It Is Essential For Model Fine-Tuning: a visual guide. https://preview.redd.it/v2plu0mvvw6d1.png?width=1456&format=png&auto=webp&s=e5f74bcb777d305c08bc74274b0c8a7cc63c973e https://preview.redd.it/vvujm3r3ww6d1.png?width=1456&format=png&auto=webp&s=cfb6111c3bd22585d171ed29c3cadcf823c8839d submitted by /u/ml_a_day [link] [comments]

  • [D] Papers in NN compression using filter banks/index based access for Conv layers
    by /u/SlayahhEUW (Machine Learning) on June 16, 2024 at 10:03 am

    I have been experimenting a bit with the idea of reducing network binary size(and perhaps runtime) by using a scheme where a network is trained with a loss that enforces cosine similarity between individual convolutional filters of the same size for different layers in a network. Assuming an optimal case where all filters from the largest convolutional layer can be reused in other layers, the rest of the layers can store the index to the relevant filters in much less depending on filter bank size. In general I could see a solution that has an array of all necessary weights that is pre-ordered for cache hit optimization and the network is simply holding pointers to the various filters. Has there been research on the feasibility of this? submitted by /u/SlayahhEUW [link] [comments]

  • Conferences/workshops for AI in biomedical [P]
    by /u/ade17_in (Machine Learning) on June 16, 2024 at 9:13 am

    Hello folks, I've been working on a research (with university as part of curriculum) for 9 months now and now I have some decent results out of it. Nothing exceptional but it was a interesting topic and dataset was collected my me over 6 months. It is closely related to analysis of biomarkers for healthy vs diagnosed person for a mental disorder. For some reason my supervisor isn't interested and just wants to have a paper written out of it but not be published anywhere. I agree that it results are not groundbreaking and nothing to be very proud of, but I've worked hard enough to go it to arcvives forever. Is there a workshop/conference where I can submit it on my own? Anything whose deadline is approaching soon? I did have other authorship on my name but I never did the work of finding/submitting conferences, so no idea even after surfing for days. IEEE ones looked good at first but then I read reviews and found out most of them are scammy and worthless. submitted by /u/ade17_in [link] [comments]

  • [P] An interesting way to minimize tilted losses
    by /u/alexsht1 (Machine Learning) on June 16, 2024 at 7:27 am

    Some time ago I read a paper about the so-called tilted empirical risk minimization, and later a JMLR paper from the same authors: https://www.jmlr.org/papers/v24/21-1095.html Such a formulation allows us to train in a manner that is more 'fair' towards the difficult samples, or conversely, less sensitive to these difficult samples if they are actually outliers. But minimizing it is numerically challenging. So I decided to try and devise a remedy in a blog post. I think it's an interesting trick that is useful here, and I hope you'll find it nice as well: https://alexshtf.github.io/2024/06/14/Untilting.html submitted by /u/alexsht1 [link] [comments]

  • [D] Is OOD generalization still a future in the LLM era?
    by /u/EducationalOwl6246 (Machine Learning) on June 16, 2024 at 6:23 am

    I think OOD generalization is an important issue because it pulls in the distance from reality. But I am concerned that recent conferences like ICLR, ICML, NeurIPS etc. don't have many people working on this problem. And check out some OOD generalization benchmarks, many methods (Such as IRM, GroupDRO) are even weaker than ERM. So, I wonder if it's because of some difficulties in this field that people stopped studying it. Or is it because of some other reason. submitted by /u/EducationalOwl6246 [link] [comments]

  • [P] Tutorials on setting up GPU-accelerated LLM on Kaggle and Google Colab (free GPU)
    by /u/Spare-Solution-787 (Machine Learning) on June 16, 2024 at 6:22 am

    I made some tutorials and notebooks on setting up GPU-accelerated Large Language Models (LLMs) with llama-cpp on Google Colab and Kaggle. You can use their GPUs for free! Get started quickly with step-by-step guides and download models from Huggingface. The same setup works for local environments with Nvidia GPUs. You can find the notebooks and instructions here: https://github.com/casualcomputer/llm_google_colab. Hopefully these notebooks save you some time from configuring the environment for llama-cpp on google colab and kaggle. submitted by /u/Spare-Solution-787 [link] [comments]

  • [R] Position: Application-Driven Innovation in Machine Learning
    by /u/FlyingQuokka (Machine Learning) on June 16, 2024 at 5:38 am

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

  • [D] 1D CNN on Waveforms and Spectrograms vs. 2D CNN Performance
    by /u/ivanstepanovftw (Machine Learning) on June 16, 2024 at 3:18 am

    It's counter-intuitive that most successful audio frameworks are using 2-dimensional convolutional neural networks (CNN), so I have tried to experiment while trying to train on BirdCLEF-2024 on Kaggle using simple frameworks, and I have questions regarding learning: When learning waveform input, why 1D CNN does not converge and even diverge immediately on validation split? When training on spectrogram magnitude (stft -> abs -> log1p), why 1D CNN performs worse than 2D CNN? While it seems that spectrogram loses phase offset information when taking magnitude, it performs better than raw waveform. So, do humans/animals have torch.stft in their ear for better perception? For example, children can understand "never seen" high-pitched Mickey Mouse speech. submitted by /u/ivanstepanovftw [link] [comments]

  • [P] How to use FaceNet?
    by /u/myplstn (Machine Learning) on June 16, 2024 at 3:12 am

    Hello everyone, I am a beginner working on a project that involves facial recognition using facenet. To my understanding, facenet takes a photo and produces a 128 element vector embedding, we then use a classification algorithm such as SVM that is used to determine the identity using the embedding. My question is, can I make this work with only two pictures provided by the user? I am assuming 2 pictures are not enough to train the classifier. So how can I make accurate predictions of a person's face using only 2 pictures? submitted by /u/myplstn [link] [comments]

  • [D] Need help finding an old Geoffrey Hinton video
    by /u/edirgl (Machine Learning) on June 15, 2024 at 9:37 pm

    Marking as [D] even though, it's not really that. Something like 15 years ago, I saw a video on YouTube that was a Geoff Hinton lecture at the University of Toronto. In the video he was explaining classic Neural Network for MNIST digit recognition. At some point in the video, he talks about how one can run the inference in reverse, effectively making the network "Imagine" a digit. I can't seem to find this video anywhere. If I recall correctly, it was perhaps uploaded at some point to the Andrew Ng's learning platform, that for some reason this sub doesn't let me say by name. Please help me finding this video. Thanks in advance! submitted by /u/edirgl [link] [comments]

  • [Discussion] Diminishing Return problem as a Machine Learning Engineer.
    by /u/The-AI-Alchemy (Machine Learning) on June 15, 2024 at 9:06 pm

    For all my fellow Machine Learning Engineer or Applied Scientist, how do you deal with diminishing return problem at work? Say you already move your model to DL, you already use transformer and you already used all the features you can think of, now what do you do to continue show impact as a Machine Learning Engineer in a product team. submitted by /u/The-AI-Alchemy [link] [comments]

  • [P] Created an open source version of "Math Notes" from Apple with GPT-4o!
    by /u/_ayushp_ (Machine Learning) on June 15, 2024 at 8:22 pm

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

  • [P] Seeking Feedback on My GenAI Job Fit Project - New to LangChain/LangGraph
    by /u/Nimitzxz (Machine Learning) on June 15, 2024 at 6:04 pm

    Hi all, Soo, i have been working on a a projectcalled GenAI Job Fit. It's an AI-driven system designed to enhance job applications by providing tailored recommendations based on individual profiles. I'm relatively new to LangChain and LangGraph, and I've incorporated them into this project. I would greatly appreciate it if you could check out the repository and provide any feedback or suggestions for improvement. Your insights on how I can better implement LangChain/LangGraph, or any other aspect of the project, would be incredibly valuable. I'm eager to learn and make this project as robust as possible. Thank you in advance for your time and feedback! Repo Link : https://github.com/DAVEinside/GenAI_Job_Fit submitted by /u/Nimitzxz [link] [comments]

  • [R] Resources Critiquing Grad-CAM Paper Versions
    by /u/dduka99 (Machine Learning) on June 15, 2024 at 3:43 pm

    Hello everyone, I'm writing a report about the Grad-CAM paper that was published a few years ago. During my research, I discovered that there are multiple versions of this paper, ranging from version 1 to version 4. I am particularly interested in finding resources that critique the shortcomings of the earlier versions and what changed in the other versions. If anyone could point me toward any such resources, it would be greatly appreciated. Thank you! submitted by /u/dduka99 [link] [comments]

  • [P] Creating a sign-language to speech converter gui, but facing issues with bbox from cv2. Any idea?
    by /u/Queasy_Boss5998 (Machine Learning) on June 15, 2024 at 3:37 pm

    High school student creating an ASL sign-language to text converter. I tried updating my cv2 and bbox, but its already the latest version. Everytime I run the data_collection_final.py file, it returns the error saying 'TypeError: list indices must be integers or slices, not str', with regards to this line: x, y, w, h = hand['bbox'] If relevant, it also returns this beforehand. Not sure what it means. 2024-06-15 23:33:23.467993: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. Can anyone help/advise how to resolve this faulty bbox error? import cv2 from cvzone.HandTrackingModule import HandDetector import numpy as np import os as oss import traceback capture = cv2.VideoCapture(0) hd = HandDetector(maxHands=1) hd2 = HandDetector(maxHands=1) count = len(oss.listdir("C:\\Users\\Arush\\ASL_Sign_Language_To_Speech_Translator\\CUSTOM_OBJECT_DETECTION_MODEL\\AtoZ_3.1\\A\\")) c_dir = 'A' offset = 15 step = 1 flag = False suv = 0 white = np.ones((400, 400), np.uint8) * 255 cv2.imwrite("C:\\Users\\Arush\\ASL_Sign_Language_To_Speech_Translator\\CUSTOM_OBJECT_DETECTION_MODEL\\white.jpg", white) while True: try: _, frame = capture.read() frame = cv2.flip(frame, 1) hands = hd.findHands(frame, draw=False, flipType=True) white = cv2.imread("C:\\Users\\Arush\\ASL_Sign_Language_To_Speech_Translator\\CUSTOM_OBJECT_DETECTION_MODEL\\white.jpg") if hands: hand = hands[0] x, y, w, h = hand['bbox'] image = np.array(frame[y - offset:y + h + offset, x - offset:x + w + offset]) handz, imz = hd2.findHands(image, draw=True, flipType=True) if handz: hand = handz[0] pts = hand['lmList'] # x1,y1,w1,h1=hand['bbox'] os = ((400 - w) // 2) - 15 os1 = ((400 - h) // 2) - 15 # Drawing lines for the skeleton for t in range(0, 4, 1): cv2.line(white, (pts[t][0] + os, pts[t][1] + os1), (pts[t + 1][0] + os, pts[t + 1][1] + os1), (0, 255, 0), 3) for t in range(5, 8, 1): cv2.line(white, (pts[t][0] + os, pts[t][1] + os1), (pts[t + 1][0] + os, pts[t + 1][1] + os1), (0, 255, 0), 3) for t in range(9, 12, 1): cv2.line(white, (pts[t][0] + os, pts[t][1] + os1), (pts[t + 1][0] + os, pts[t + 1][1] + os1), (0, 255, 0), 3) for t in range(13, 16, 1): cv2.line(white, (pts[t][0] + os, pts[t][1] + os1), (pts[t + 1][0] + os, pts[t + 1][1] + os1), (0, 255, 0), 3) for t in range(17, 20, 1): cv2.line(white, (pts[t][0] + os, pts[t][1] + os1), (pts[t + 1][0] + os, pts[t + 1][1] + os1), (0, 255, 0), 3) cv2.line(white, (pts[5][0] + os, pts[5][1] + os1), (pts[9][0] + os, pts[9][1] + os1), (0, 255, 0), 3) cv2.line(white, (pts[9][0] + os, pts[9][1] + os1), (pts[13][0] + os, pts[13][1] + os1), (0, 255, 0), 3) cv2.line(white, (pts[13][0] + os, pts[13][1] + os1), (pts[17][0] + os, pts[17][1] + os1), (0, 255, 0), 3) cv2.line(white, (pts[0][0] + os, pts[0][1] + os1), (pts[5][0] + os, pts[5][1] + os1), (0, 255, 0), 3) cv2.line(white, (pts[0][0] + os, pts[0][1] + os1), (pts[17][0] + os, pts[17][1] + os1), (0, 255, 0), 3) skeleton0 = np.array(white) zz = np.array(white) for i in range(21): cv2.circle(white, (pts[i][0] + os, pts[i][1] + os1), 2, (0, 0, 255), 1) skeleton1 = np.array(white) cv2.imshow("1", skeleton1) frame = cv2.putText(frame, "dir=" + str(c_dir) + " count=" + str(count), (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 1, cv2.LINE_AA) cv2.imshow("frame", frame) interrupt = cv2.waitKey(1) if interrupt & 0xFF == 27: # esc key break if interrupt & 0xFF == ord('n'): c_dir = chr(ord(c_dir) + 1) if ord(c_dir) == ord('Z') + 1: c_dir = 'A' flag = False count = len(oss.listdir("C:\\Users\\Arush\\ASL_Sign_Language_To_Speech_Translator\\CUSTOM_OBJECT_DETECTION_MODEL\\AtoZ_3.1\\" + (c_dir) + "\\")) if interrupt & 0xFF == ord('a'): if flag: flag = False else: suv = 0 flag = True print("=====", flag) if flag == True: if suv == 180: flag = False if step % 3 == 0: cv2.imwrite("C:\\Users\\Arush\\ASL_Sign_Language_To_Speech_Translator\\CUSTOM_OBJECT_DETECTION_MODEL\\AtoZ_3.1\\" + (c_dir) + "\\" + str(count) + ".jpg", skeleton1) count += 1 suv += 1 step += 1 except Exception: print("==", traceback.format_exc()) capture.release() cv2.destroyAllWindows() submitted by /u/Queasy_Boss5998 [link] [comments]

  • Generative Diffusion Models explained step-by-step in 15 concepts! [D]
    by /u/AvvYaa (Machine Learning) on June 15, 2024 at 2:09 pm

    Sharing a video from my YT channel about latent diffusion models starting from the basics to some pretty advanced stuff. I also share my experiences implementing a simple diffusion model from scratch to generate human faces from text prompts. Enjoy! Link: https://youtu.be/w8YQcEd77_o submitted by /u/AvvYaa [link] [comments]

  • [D] How to network at a conference
    by /u/SherlockGPT (Machine Learning) on June 15, 2024 at 10:00 am

    How to network in a conference Hi everyone! I'm attending my first big conference next week- CVPR. Everyone mentioned that I should spend a lot of time networking with other students and senior researchers. I have also managed to secure invites to socials of Google and Meta. I suck at all things social. How do I approach other researchers and talk with them about potential collaborations or research internships without sounding needy? Also appreciate any general advice on how to maximize my time at CVPR. Thanks! submitted by /u/SherlockGPT [link] [comments]

  • [R] CFG++ : A simple fix for addressing the flaws of CFG in diffusion models
    by /u/Fit_Entrepreneur_588 (Machine Learning) on June 15, 2024 at 4:16 am

    The classifier-free guidance (CFG) is widely used for text-guidance in diffusion models, but notorious for its challenges, such as difficulty in DDIM inversion and ambiguity in selecting a large guidance scale. This paper demonstrates that these limitations of CFG stem from inherent design flaws in the original CFG, and introduce CFG++, a simple yet powerful fix in the *re\*nosing process. This adjustment facilitates smaller guidance scales, significantly improved invertibility, and much better alignment between images and text. Project page: https://cfgpp-diffusion.github.io/ Github: https://github.com/CFGpp-diffusion/CFGpp Paper: https://arxiv.org/abs/2406.08070 https://preview.redd.it/lph7aufcvn6d1.png?width=854&format=png&auto=webp&s=5e4bfeb1af9563bb30b29848386f02b11f13fbdc submitted by /u/Fit_Entrepreneur_588 [link] [comments]

  • [D] Discussing Apple's Deployment of a 3 Billion Parameter AI Model on the iPhone 15 Pro - How Do They Do It?
    by /u/BriefAd4761 (Machine Learning) on June 14, 2024 at 11:50 am

    Hey everyone, So, I've been working with running the Phi-3 mini locally, and honestly, it's been a bit of a ok . Despite all the tweaks and structured prompts in model files, it was normal, especially considering the laggy response times on a typical GPU setup. I was recently checking Apple's recent on -device model, they've got a nearly 3 billion parameter AI model running on an iPhone 15 Pro! It's a forward in what's possible with AI on mobile devices. They’ve made up some tricks to make this work, and I just wanted to have discussion to dive into these with you all: Optimized Attention Mechanisms: Apple has significantly reduced computational overhead by using a grouped-query-attention mechanism. This method batches queries, cutting down the necessary computations. Shared Vocabulary Embeddings: Honestly I don't have much idea about this - I need to understand it more Quantization Techniques: Adopting a mix of 2-bit and 4-bit quantization for model weights has effectively lowered both the memory footprint and power consumption. Efficient Memory Management: dynamic loading of small, task-specific adapter are that can be loaded into the foundation model to specialize its functions without retraining the core parameters. These adapters are lightweight and used only when needed, flexibility and efficiency in memory use. Efficient Key-Value (KV) Cache Updates: Even I don't know how this works Power and Latency Analysis Tools: they were using tools like Talaria to analyze and optimize the model’s power consumption and latency in real-time. This allows them to make decisions about trade-offs between performance, power use, and speed, customizing bit rate selections for optimal operation under different conditions.: Talaria demo video Model Specialization via Adapters: Instead of retraining the entire model, only specific adapter layers are trained for different tasks. maintaining high performance without the overhead of a full model retraining. Apple’s adapters let the AI switch gears on the fly for different tasks, all while keeping things light and fast. For more detailed insights, check out Apple’s official documentation here: Introducing Apple Foundation Models Discussion Points: How feasible is it to deploy such massive models on mobile devices? What are the implications of these techniques for future mobile applications? How do these strategies compare to those used in typical desktop GPU environments like my experience with Phi-3 mini? submitted by /u/BriefAd4761 [link] [comments]

  • Build a custom UI for Amazon Q Business
    by Ennio Pastore (AWS Machine Learning Blog) on June 12, 2024 at 4:44 pm

    Amazon Q is a new generative artificial intelligence (AI)-powered assistant designed for work that can be tailored to your business. Amazon Q can help you get fast, relevant answers to pressing questions, solve problems, generate content, and take actions using the data and expertise found in your company’s information repositories and enterprise systems. When you

  • Scalable intelligent document processing using Amazon Bedrock
    by Venkata Kampana (AWS Machine Learning Blog) on June 12, 2024 at 4:32 pm

    In today’s data-driven business landscape, the ability to efficiently extract and process information from a wide range of documents is crucial for informed decision-making and maintaining a competitive edge. However, traditional document processing workflows often involve complex and time-consuming manual tasks, hindering productivity and scalability. In this post, we discuss an approach that uses the

  • Use weather data to improve forecasts with Amazon SageMaker Canvas
    by Charles Laughlin (AWS Machine Learning Blog) on June 12, 2024 at 3:53 pm

    Photo by Zbynek Burival on Unsplash Time series forecasting is a specific machine learning (ML) discipline that enables organizations to make informed planning decisions. The main idea is to supply historic data to an ML algorithm that can identify patterns from the past and then use those patterns to estimate likely values about unseen periods

  • Reimagining software development with the Amazon Q Developer Agent
    by Christian Bock (AWS Machine Learning Blog) on June 11, 2024 at 4:52 pm

    Amazon Q Developer uses generative artificial intelligence (AI) to deliver state-of-the-art accuracy for all developers, taking first place on the leaderboard for SWE-bench, a dataset that tests a system’s ability to automatically resolve GitHub issues. This post describes how to get started with the software development agent, gives an overview of how the agent works, and discusses its performance on public benchmarks. We also delve into the process of getting started with the Amazon Q Developer Agent and give an overview of the underlying mechanisms that make it a state-of-the-art feature development agent.

  • Get started quickly with AWS Trainium and AWS Inferentia using AWS Neuron DLAMI and AWS Neuron DLC
    by Niithiyn Vijeaswaran (AWS Machine Learning Blog) on June 11, 2024 at 2:47 pm

    Starting with the AWS Neuron 2.18 release, you can now launch Neuron DLAMIs (AWS Deep Learning AMIs) and Neuron DLCs (AWS Deep Learning Containers) with the latest released Neuron packages on the same day as the Neuron SDK release. When a Neuron SDK is released, you’ll now be notified of the support for Neuron DLAMIs

  • Sprinklr improves performance by 20% and reduces cost by 25% for machine learning inference on AWS Graviton3
    by Sunita Nadampalli (AWS Machine Learning Blog) on June 11, 2024 at 2:44 pm

    This is a guest post co-written with Ratnesh Jamidar and Vinayak Trivedi from Sprinklr. Sprinklr’s mission is to unify silos, technology, and teams across large, complex companies. To achieve this, we provide four product suites, Sprinklr Service, Sprinklr Insights, Sprinklr Marketing, and Sprinklr Social, as well as several self-serve offerings. Each of these products are

  • How Wiz is empowering organizations to remediate security risks faster with Amazon Bedrock
    by Shaked Rotlev (AWS Machine Learning Blog) on June 11, 2024 at 2:36 pm

    Wiz is a cloud security platform that enables organizations to secure everything they build and run in the cloud by rapidly identifying and removing critical risks. Over 40% of the Fortune 100 trust Wiz’s purpose-built cloud security platform to gain full-stack visibility, accurate risk prioritization, and enhanced business agility. Organizations can connect Wiz in minutes

  • Code generation using Code Llama 70B and Mixtral 8x7B on Amazon SageMaker
    by Shikhar Kwatra (AWS Machine Learning Blog) on June 10, 2024 at 2:16 pm

    In the ever-evolving landscape of machine learning and artificial intelligence (AI), large language models (LLMs) have emerged as powerful tools for a wide range of natural language processing (NLP) tasks, including code generation. Among these cutting-edge models, Code Llama 70B stands out as a true heavyweight, boasting an impressive 70 billion parameters. Developed by Meta

  • Build RAG applications using Jina Embeddings v2 on Amazon SageMaker JumpStart
    by Francesco Kruk (AWS Machine Learning Blog) on June 6, 2024 at 3:00 pm

    Today, we are excited to announce that the Jina Embeddings v2 model, developed by Jina AI, is available for customers through Amazon SageMaker JumpStart to deploy with one click for running model inference. This state-of-the-art model supports an impressive 8,192-tokens context length. You can deploy this model with SageMaker JumpStart, a machine learning (ML) hub

  • Detect email phishing attempts using Amazon Comprehend
    by Ajeet Tewari (AWS Machine Learning Blog) on June 5, 2024 at 7:10 pm

    Phishing is the process of attempting to acquire sensitive information such as usernames, passwords and credit card details by masquerading as a trustworthy entity using email, telephone or text messages. There are many types of phishing based on the mode of communication and targeted victims. In an Email phishing attempt, an email is sent as

  • How Skyflow creates technical content in days using Amazon Bedrock
    by Manny Silva (AWS Machine Learning Blog) on June 5, 2024 at 3:57 pm

    This guest post is co-written with Manny Silva, Head of Documentation at Skyflow, Inc. Startups move quickly, and engineering is often prioritized over documentation. Unfortunately, this prioritization leads to release cycles that don’t match, where features release but documentation lags behind. This leads to increased support calls and unhappy customers. Skyflow is a data privacy

  • Streamline custom model creation and deployment for Amazon Bedrock with Provisioned Throughput using Terraform
    by Josh Famestad (AWS Machine Learning Blog) on June 4, 2024 at 5:58 pm

    As customers seek to incorporate their corpus of knowledge into their generative artificial intelligence (AI) applications, or to build domain-specific models, their data science teams often want to conduct A/B testing and have repeatable experiments. In this post, we discuss a solution that uses infrastructure as code (IaC) to define the process of retrieving and

  • Boost productivity with video conferencing transcripts and summaries with the Amazon Chime SDK Meeting Summarizer solution
    by Adam Neumiller (AWS Machine Learning Blog) on June 4, 2024 at 4:48 pm

    Businesses today heavily rely on video conferencing platforms for effective communication, collaboration, and decision-making. However, despite the convenience these platforms offer, there are persistent challenges in seamlessly integrating them into existing workflows. One of the major pain points is the lack of comprehensive tools to automate the process of joining meetings, recording discussions, and extracting

  • Implement serverless semantic search of image and live video with Amazon Titan Multimodal Embeddings
    by Thorben Sanktjohanser (AWS Machine Learning Blog) on June 3, 2024 at 5:47 pm

    In today’s data-driven world, industries across various sectors are accumulating massive amounts of video data through cameras installed in their warehouses, clinics, roads, metro stations, stores, factories, or even private facilities. This video data holds immense potential for analysis and monitoring of incidents that may occur in these locations. From fire hazards to broken equipment,

  • Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas
    by Rushabh Lokhande (AWS Machine Learning Blog) on June 3, 2024 at 5:34 pm

    In today’s fast-paced corporate landscape, employee mental health has become a crucial aspect that organizations can no longer overlook. Many companies recognize that their greatest asset lies in their dedicated workforce, and each employee plays a vital role in collective success. As such, promoting employee well-being by creating a safe, inclusive, and supportive environment is

  • Pre-training genomic language models using AWS HealthOmics and Amazon SageMaker
    by Shamika Ariyawansa (AWS Machine Learning Blog) on May 31, 2024 at 4:15 pm

    Pre-train HyenaDNA, a transformer model exceeding 1M tokens, using HealthOmics storage and SageMaker's managed training environment to catalyze breakthroughs in precision medicine, agriculture, and biotechnology.

  • Falcon 2 11B is now available on Amazon SageMaker JumpStart
    by Supriya Puragundla (AWS Machine Learning Blog) on May 31, 2024 at 3:55 pm

    Today, we are excited to announce that the first model in the next generation Falcon 2 family, the Falcon 2 11B foundation model (FM) from Technology Innovation Institute (TII), is available through Amazon SageMaker JumpStart to deploy and run inference. Falcon 2 11B is a trained dense decoder model on a 5.5 trillion token dataset

  • Implementing Knowledge Bases for Amazon Bedrock in support of GDPR (right to be forgotten) requests
    by Yadukishore Tatavarthi (AWS Machine Learning Blog) on May 31, 2024 at 3:06 pm

    The General Data Protection Regulation (GDPR) right to be forgotten, also known as the right to erasure, gives individuals the right to request the deletion of their personally identifiable information (PII) data held by organizations. This means that individuals can ask companies to erase their personal data from their systems and from the systems of

  • CBRE and AWS perform natural language queries of structured data using Amazon Bedrock
    by Surya Rebbapragada (AWS Machine Learning Blog) on May 30, 2024 at 7:45 pm

    This is a guest post co-written with CBRE. CBRE is the world’s largest commercial real estate services and investment firm, with 130,000 professionals serving clients in more than 100 countries. Services range from financing and investment to property management. CBRE is unlocking the potential of artificial intelligence (AI) to realize value across the entire commercial

  • Dynamic video content moderation and policy evaluation using AWS generative AI services
    by Lana Zhang (AWS Machine Learning Blog) on May 30, 2024 at 6:24 pm

    Organizations across media and entertainment, advertising, social media, education, and other sectors require efficient solutions to extract information from videos and apply flexible evaluations based on their policies. Generative artificial intelligence (AI) has unlocked fresh opportunities for these use cases. In this post, we introduce the Media Analysis and Policy Evaluation solution, which uses AWS

Download AWS machine Learning Specialty Exam Prep App on iOs

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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

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AWS Data analytics DAS-C01 Exam Preparation

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