Sagemaker Expert Help

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. connect with our expert to customize details, finalize the price,

Hire Sagemake Expert

Codersarts machine learning experts are experienced with sagemaker and will help you to  build, train, tune, deploy, and manage large-scale machine learning (ML) models.

What Is Amazon SageMaker?

Amazon SageMaker is a fully managed machine learning service. With SageMaker, data scientists and developers can quickly and easily build and train machine learning 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 you don't have to manage servers. It also provides common machine learning algorithms that are optimized to run efficiently against extremely large data in a distributed environment. With native support for bring-your-own-algorithms and frameworks, SageMaker offers flexible distributed training options that adjust to your specific workflows. Deploy a model into a secure and scalable environment by launching it with a few clicks from SageMaker Studio or the SageMaker console. Training and hosting are billed by minutes of usage, with no minimum fees and no upfront commitments. SageMaker MLOps Project Walkthrough

Example Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using Amazon SageMaker.

Sagemaker Services we offer

  • Deploy Machine Learning models Into Cloud.

  • Train Machine Learning Models on SageMaker.

  • Lex For Chatbot creation

  • Set up Kubernetes Clusters on Amazon EC@ Spot.

  • Build Recommendation Systems.

  • Launch Serverless Web App using:AWS Amplify,Amazon Cognito, Amazon API Gateway. AWS Lambda, AWS DynamoDB

  • Automatic model tuning and operations with SageMaker

  • Amazon SageMaker's built-in machine learning algorithms (XGBoost, BlazingText, Object Detection, etc.)

  • High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition

  • Exploratory data analysis with Scikit_learn, Athena, Apache Spark, EMR, Tensorflow, MXNet, pytorch

  • Deep learning and hyperparameter tuning of deep neural networks

Feature engineering techniques including

  • Imputation

  • Outliers

  •  Binning

  •  Normalization

Deploying Supervised Learning models

  • Factorization Machines Algorithm

  • K-Nearest Neighbors (k-NN) Algorithm

  • Linear Learner Algorithm

  • XGBoost Algorithm

  • DeepAR Forecasting Algorithm

Deploying Unsupervised Learning Models in the cloud.

  • Principal Component Analysis (PCA) Algorithm

  • Random Cut Forest (RCF) Algorithm

  • IP Insights

  • Anomaly detection

  • Object2Vec Algorithm

  • Embeddings: convert high-dimensional objects into low-dimensional space.

  • Clustering or grouping(k-means)

  • Topic modeling

  • Latent Dirichlet Allocation (LDA) Algorithm

  • Neural Topic Model (NTM) Algorithm

Deploying Natural Language Processing models in cloud.

  • BlazingText algorithm

  • Sequence-to-Sequence Algorithm

  • Machine Learning translation

  • Text summarization

  • Speech-to-text

Deploying Computer vision models in the cloud

  • Image and multi-label classification 

  • Object detection and classification

  • Video stream classification

  • Face recognition

  • Self-driving cars[lane detection]