Machine Learning with Amazon SageMaker Training

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Instructor-Led Training Parameters

Course Highlights

  • Instructor-led Online Training
  • Project Based Learning
  • Certified & Experienced Trainers
  • Course Completion Certificate
  • Lifetime e-Learning Access
  • 24x7 After Training Support

Machine Learning with Amazon SageMaker Training Course Overview

Multisoft Systems is now offering Machine Learning course with Amazon SageMaker which is especially designed by global subject matter experts to gain experience in designing an end-to-end solution using Azure Synapse Analytics. Multisoft Systems provides one-on-one and corporate training so that candidates can get hands-on experience through real-life assignments and projects to earn globally recognized certificate.

Machine Learning with Amazon SageMaker course is for Cloud Architects and IT professionals who are interested in learning more about Azure and Azure services and have architectural expertise in infrastructure and solution design in cloud technologies. This workshop is designed for anyone interested in learning about Microsoft Azure, as well as those experienced in other non-Microsoft cloud technologies.

In Multisoft Systems, candidates get one-on-one and corporate training by global subject matter experts of Machine Learning with Amazon SageMaker course. In Machine Learning with Amazon SageMaker course, a team of professionals guide candidates to gain hands-on experience through real-world assignments and projects which will help candidates to advance their skills. Once candidates enroll for themselves for Multisoft System’s Machine Learning with Amazon SageMaker course then will be getting lifetime access to the online learning environment, digital course materials, round-the-clock after-training support, and video recordings, and once the complete the course successfully candidates will earn globally recognized certificate.

Machine Learning with Amazon SageMaker Course Objective
  • After the completion of this course, candidates will be able to design and build a complete end-to-end advanced analytics solution using Azure Synapse Analytics.
Machine Learning with Amazon SageMaker Online Training
  • Recorded Videos After Training
  • Digital Learning Material
  • Course Completion Certificate
  • 24x7 After Training Support
Target Audience
  •  
Machine Learning with Amazon SageMaker Course Prerequisites
  • Candidates should have Microsoft Azure Essentials Knowledge
  • Candidates should be experienced in other non-Microsoft cloud technologies.
Machine Learning with Amazon SageMaker Course Certification
  • Multisoft Systems provides a training certification after successful completion of Machine Learning with Amazon SageMaker course.

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Machine Learning with Amazon SageMaker Training Course Content

Module 1: Introduction

  • Overview of key Machine Learning and Deep Learning concepts
  • Getting about in AWS
  • Overview of SageMaker features
  • Taking your first look at SageMaker studio

 Module 2: Preparing your dataset

  • Identifying your data and articulating your problem
  • Format data for consistency
  • Cleaning and validating your data
  • Uploading to SageMaker

 Module 3: Data Analysis

  • Clustering
  • Trend analysis
  • Finding other relationships between different types of data

 Module 4: Data Visualisation

  • Frequency tables
  • Cross-tabulation tables
  • Bar charts
  • Line graphs
  • Pie charts
  • Heat Maps
  • Scatter graphs

 Module 5: Training your Model

  • Creating a Training job
  • Assigning Compute resources
  • Selecting the right algorithm
  • Overview of using custom code (Python, TensorFlow)

 Module 6: Deploying your Model

  • SageMaker Hosting Services
  • Configuring and creating an HTTPS endpoint

 Module 7: SageMaker Batch Transforms

  • Making inferences from your dataset
  • Indexing and real-time indices
  • Using Batch Transform to preprocess data to train a new model

Module 8: Validating your Model

  • SageMaker Debugger
  • Offline testing
  • Online testing
  • Validating using a holdout set

 Module 9: Model Tuning

  • Defining metrics
  • Hyperparameter tuning
  • Automatic model tuning

 Module 10: Deploying and sharing using SageMaker Feature Store

  • Creating, Storing and Sharing Features
  • Online / Offline
  • Feature Groups
  • Discovery
  • Batch Inference
  • Feature Data Ingestion

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Machine Learning with Amazon SageMaker Training (MCQ) Assessment

This assessment tests understanding of course content through MCQ and short answers, analytical thinking, problem-solving abilities, and effective communication of ideas. Some Multisoft Assessment Features :

  • User-friendly interface for easy navigation
  • Secure login and authentication measures to protect data
  • Automated scoring and grading to save time
  • Time limits and countdown timers to manage duration.
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Machine Learning with Amazon SageMaker Corporate Training

Employee training and development programs are essential to the success of businesses worldwide. With our best-in-class corporate trainings you can enhance employee productivity and increase efficiency of your organization. Created by global subject matter experts, we offer highest quality content that are tailored to match your company’s learning goals and budget.

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Customized Training

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360º Learning Solution

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Learning Assessment

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Certification Training Achievements: Recognizing Professional Expertise

Multisoft Systems is the “one-top learning platform” for everyone. Get trained with certified industry experts and receive a globally-recognized training certificate. Some Multisoft Training Certificate Features :

  • Globally recognized certificate
  • Course ID & Course Name
  • Certificate with Date of Issuance
  • Name and Digital Signature of the Awardee
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Machine Learning with Amazon SageMaker Training FAQ's

The limitless analytics tool known as Azure Synapse Analytics includes big data analytics, enterprise data warehousing, and data integration. You can use serverless or dedicated options to do data queries at scale as you see suitable.

Azure Synapse Analytics is essentially a development of Azure SQL Data Warehouse. A massively parallel processing (MPP) cloud-based, a scale-out relational database called Azure SQL Data Warehouse was created to analyze and store vast volumes of data on the Microsoft Azure cloud platform.

In the branch of technology known as "machine learning," computers are taught to do a range of activities, including forecasts, suggestions, guesses, etc., based on prior knowledge or historical data. Machine learning teaches computers to behave like people by utilizing past data and projected data.

A branch of computer science called artificial intelligence (AI) is focused on building intelligent machines that behave and act like people. Data science is not a subfield of AI like machine learning and deep learning are. Data science is mainly concerned with concluding data to create solid IT and business strategies. Additionally, it handles the collection, handling, analysis, and visualization of data. Building models for decision-making is the emphasis of AI, ML, and DL. To address an issue, data science uses mathematical, statistical, and probabilistic methods as well as model optimization. This is where data science and AI interact.

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