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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
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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
- Enable or disable ACL crawling safely in Amazon Q Businessby Rajesh Kumar Ravi (AWS Machine Learning Blog) on October 11, 2024 at 12:23 am
Amazon Q Business recently added support for administrators to modify the default access control list (ACL) crawling feature for data source connectors. Amazon Q Business is a fully managed, AI powered assistant with enterprise-grade security and privacy features. It includes over 40 data source connectors that crawl and index documents. By default, Amazon Q Business
- SK Telecom improves telco-specific Q&A by fine-tuning Anthropic’s Claude models in Amazon Bedrockby Sungmin Hong (AWS Machine Learning Blog) on October 10, 2024 at 4:38 pm
In this post, we share how SKT customizes Anthropic Claude models for telco-specific Q&A regarding technical telecommunication documents of SKT using Amazon Bedrock.
- Scaling Rufus, the Amazon generative AI-powered conversational shopping assistant with over 80,000 AWS Inferentia and AWS Trainium chips, for Prime Dayby James Park (AWS Machine Learning Blog) on October 10, 2024 at 3:39 pm
In this post, we dive into the Rufus inference deployment using AWS chips and how this enabled one of the most demanding events of the year—Amazon Prime Day.
- Exploring alternatives and seamlessly migrating data from Amazon Lookout for Visionby Tim Westman (AWS Machine Learning Blog) on October 10, 2024 at 3:33 pm
In this post we discuss how you can maintain access to Lookout for Vision after it is closed to new customers, some alternatives to Lookout for Vision, and how you can export your data from Lookout for Vision to migrate to an alternate solution.
- Unlock the knowledge in your Slack workspace with Slack connector for Amazon Q Businessby Roshan Thomas (AWS Machine Learning Blog) on October 9, 2024 at 10:03 pm
In this post, we will demonstrate how to set up Slack connector for Amazon Q Business to sync communications from both public and private channels, reflective of user permissions.
- Transitioning off Amazon Lookout for Metrics by Nirmal Kumar (AWS Machine Learning Blog) on October 9, 2024 at 8:02 pm
In this post, we provide an overview of the alternate AWS services that offer anomaly detection capabilities for customers to consider transitioning their workloads to.
- Efficient Pre-training of Llama 3-like model architectures using torchtitan on Amazon SageMakerby Roy Allela (AWS Machine Learning Blog) on October 8, 2024 at 10:10 pm
In this post, we collaborate with the team working on PyTorch at Meta to showcase how the torchtitan library accelerates and simplifies the pre-training of Meta Llama 3-like model architectures. We showcase the key features and capabilities of torchtitan such as FSDP2, torch.compile integration, and FP8 support that optimize the training efficiency.
- Time series forecasting with Amazon SageMaker AutoMLby Davide Gallitelli (AWS Machine Learning Blog) on October 8, 2024 at 5:39 pm
In this blog post, we explore a comprehensive approach to time series forecasting using the Amazon SageMaker AutoMLV2 Software Development Kit (SDK). SageMaker AutoMLV2 is part of the SageMaker Autopilot suite, which automates the end-to-end machine learning workflow from data preparation to model deployment.
- Automate user on-boarding for financial services with a digital assistant powered by Amazon Bedrockby Anup Ravindranath (AWS Machine Learning Blog) on October 8, 2024 at 5:39 pm
In this post, we present a solution that harnesses the power of generative AI to streamline the user onboarding process for financial services through a digital assistant.
- Build a generative AI Slack chat assistant using Amazon Bedrock and Amazon Kendraby Kruthi Jayasimha Rao (AWS Machine Learning Blog) on October 7, 2024 at 8:48 pm
In this post, we describe the development of a generative AI Slack application powered by Amazon Bedrock and Amazon Kendra. This is designed to be an internal-facing Slack chat assistant that helps answer questions related to the indexed content.
- Create your fashion assistant application using Amazon Titan models and Amazon Bedrock Agentsby Akarsha Sehwag (AWS Machine Learning Blog) on October 4, 2024 at 7:53 pm
In this post, we implement a fashion assistant agent using Amazon Bedrock Agents and the Amazon Titan family models. The fashion assistant provides a personalized, multimodal conversational experience.
- How Aviva built a scalable, secure, and reliable MLOps platform using Amazon SageMakerby Dean Steel (AWS Machine Learning Blog) on October 3, 2024 at 6:18 pm
In this post, we describe how Aviva built a fully serverless MLOps platform based on the AWS Enterprise MLOps Framework and Amazon SageMaker to integrate DevOps best practices into the ML lifecycle. This solution establishes MLOps practices to standardize model development, streamline ML model deployment, and provide consistent monitoring.
- Visier’s data science team boosts their model output 10 times by migrating to Amazon SageMakerby Kinman Lam (AWS Machine Learning Blog) on October 3, 2024 at 5:31 pm
In this post, we learn how Visier was able to boost their model output by 10 times, accelerate innovation cycles, and unlock new opportunities using Amazon SageMaker.
- Implement model-independent safety measures with Amazon Bedrock Guardrailsby Michael Cho (AWS Machine Learning Blog) on October 3, 2024 at 5:28 pm
In this post, we discuss how you can use the ApplyGuardrail API in common generative AI architectures such as third-party or self-hosted large language models (LLMs), or in a self-managed Retrieval Augmented Generation (RAG) architecture.
- How Schneider Electric uses Amazon Bedrock to identify high-potential business opportunitiesby Anthony Medeiros (AWS Machine Learning Blog) on October 2, 2024 at 8:23 pm
In this post, we show how the team at Schneider collaborated with the AWS Generative AI Innovation Center (GenAIIC) to build a generative AI solution on Amazon Bedrock to solve this problem. The solution processes and evaluates each requests for proposal (RFP) and then routes high-value RFPs to the microgrid subject matter expert (SME) for approval and recommendation.
- Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrockby Akarsha Sehwag (AWS Machine Learning Blog) on October 2, 2024 at 7:40 pm
In this post, we discuss scaling up generative AI for different lines of businesses (LOBs) and address the challenges that come around legal, compliance, operational complexities, data privacy and security.
- Elevate workforce productivity through seamless personalization in Amazon Q Businessby James Jory (AWS Machine Learning Blog) on October 2, 2024 at 7:09 pm
In this post, we explore how Amazon Q Business uses personalization to improve the relevance of responses and how you can align your use cases and end-user data to take full advantage of this capability
- Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1by Maira Ladeira Tanke (AWS Machine Learning Blog) on October 2, 2024 at 7:06 pm
In this post, we show you how to create accurate and reliable agents. Agents helps you accelerate generative AI application development by orchestrating multistep tasks. Agents use the reasoning capability of foundation models (FMs) to break down user-requested tasks into multiple steps.
- AWS recognized as a first-time Leader in the 2024 Gartner Magic Quadrant for Data Science and Machine Learning Platformsby Susanne Seitinger (AWS Machine Learning Blog) on October 1, 2024 at 6:29 pm
AWS has been recognized as a Leader in the 2024 Gartner Magic Quadrant for Data Science and Machine Learning Platforms. The post highlights how AWS's continued innovations in services like Amazon Bedrock and Amazon SageMaker have enabled organizations to unlock the transformative potential of generative AI.
- Build a serverless voice-based contextual chatbot for people with disabilities using Amazon Bedrockby Michael Shapira (AWS Machine Learning Blog) on October 1, 2024 at 5:45 pm
In this post, we presented how to create a fully serverless voice-based contextual chatbot using Amazon Bedrock with Anthropic Claude.
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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Download AWS Machine Learning Specialty Exam Prep App on Android/Web/Amazon
