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We're here to help you find itThe Practical Data Science with Amazon SageMaker course is a comprehensive program designed to teach learners the ins and outs of machine learning (ML) with a focus on using Amazon SageMaker, a fully managed service that enables developers and data scientists to build, train, and deploy machine learning models at scale. The course covers various aspects of ML, from the basics to more advanced techniques.
Starting with an introduction to the types of ML, job roles, and the ML Pipeline, the course progresses to Data Preparation and the use of SageMaker. Learners will engage in practical exercises, such as launching a Jupyter Notebook and preparing datasets, and delve into Data Analysis and visualization. The course emphasizes hands-on learning with exercises for Model Training, evaluation, and Hyperparameter Tuning.
Modules on deployment, Production Readiness, and Cost Analysis of errors are also included, ensuring that students understand the full lifecycle of ML model development. With a focus on Practical Data Science with Amazon SageMaker, this course is ideal for those looking to apply ML in real-world scenarios, leveraging SageMaker's architecture and features to streamline the process.
Successfully delivered 9 sessions for over 14 professionals
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| Course Fee | 675 |
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Total Fees |
675 (USD) |
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♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
Certainly! To ensure that students are adequately prepared for the Practical Data Science with Amazon SageMaker course and can get the most out of the training, the following minimum prerequisites are suggested:
Students who meet these prerequisites are more likely to successfully grasp the course content and apply the skills learned in the Practical Data Science with Amazon SageMaker training.
Practical Data Science with Amazon SageMaker is a comprehensive course designed for professionals seeking to leverage ML in cloud environments.
In the Practical Data Science with Amazon SageMaker course, students will learn to build, train, tune, and deploy machine learning models using AWS SageMaker.
Amazon SageMaker is a cloud service from Amazon Web Services that helps data scientists and developers build, train, and deploy machine learning models quickly. It offers a wide range of tools and capabilities within a fully managed environment, simplifying the entire process of practical data science. By removing many of the complexities associated with machine learning, such as coding the algorithms from scratch, SageMaker enables users to focus more on solving the problem at hand rather than managing technical configurations and infrastructure. This makes it easier to apply machine learning to real business challenges effectively.
An ML Pipeline in data science is a series of steps automated to transform raw data into valuable insights. This process involves data gathering, cleaning, and preparation, followed by model training and evaluation. Each step is interconnected and automates the flow from data input to result output, ensuring efficiency and consistency in the creation of machine learning models. This set-up simplifies the deployment of models and management of multiple experiments, important for scaling and refining machine learning applications in practical scenarios.
Data Preparation is the process of cleaning and transforming raw data before it is used in analytics or machine learning models. This step involves handling missing values, correcting errors, and standardizing data formats to enhance data quality and ensure accuracy in predictive modeling. Effective data preparation improves data analysis efficiency and is crucial for generating reliable, actionable insights. This foundational task establishes a robust baseline that facilitates practical data science applications.
Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. It is widely used by data scientists and researchers to perform data cleaning, statistical computation, visualization, and machine learning tasks. Jupyter supports multiple programming languages, allowing users to seamlessly integrate code into their documents. This environment is particularly practical for interactive data science projects and can be effectively used with Amazon SageMaker to enhance model building and deployment.
Data analysis involves examining raw data to extract meaningful insights, identify patterns, and make informed decisions. It includes collecting data, processing it, and applying statistical methods to understand trends or anomalies. Data analysis is used in various fields such as business, healthcare, and science to improve performance, streamline operations, and enhance products or services based on data-driven conclusions. The process is crucial for organizations to adapt and stay competitive in today's data-rich environment.
Model training is a process in machine learning where a computer program learns from data to make or improve predictions. In this phase, a model uses historical data to learn patterns or behaviors. These learned patterns are then used to make predictions about new, unseen data. Effective model training minimizes errors in predictions, improving over time through various techniques and algorithms. The goal is to create models that are both accurate and generalizable, which means they perform well on new datasets, not just the ones they trained on.
Hyperparameter tuning is a process in machine learning where the settings that control the behavior of the algorithm, known as hyperparameters, are adjusted to improve how well the model performs. Unlike model parameters that are learned during training, hyperparameters are set before training begins. This tuning can be crucial for optimizing a model's accuracy, efficiency, and overall performance. Methods for tuning include grid search, random search, and automated approaches like Bayesian optimization, each method providing a different strategy to find the optimal set of hyperparameters for the best model performance.
Deployment in a technology context refers to the process of distributing and enabling a software application or update to be accessible and functional in a live production environment. This involves taking completed software from development stages and ensuring it works as expected for users, whether it’s on physical servers, in a cloud computing environment, or across various devices. The goal of deployment is to make software operational with minimal disruption, ensuring that all functions perform correctly and efficiently after the software is released to end-users. It includes tasks like installation, configuration, running, and testing of software systems.
Production readiness refers to the stage where a software product or system is fully prepared for deployment in a live or production environment. This entails ensuring the software is stable, scalable, secure, and can handle expected load without performance issues. It involves rigorous testing, quality assurance, performance tuning, security checks, and documentation to ensure the product meets all the specifications and requirements. The focus is on minimizing risks and maximizing system availability and user satisfaction, ensuring that the software can operate smoothly and efficiently in real-world conditions.
Cost analysis is a financial evaluation method used to determine the total cost of a project or a product. It includes identifying all the expenses involved, such as materials, labor, and overhead costs, and comparing these against the benefits or revenues expected. The purpose of performing a cost analysis is to assess whether a project or a product is financially viable, to optimize spending, and to help in making informed budgeting and investment decisions. By understanding the full economic impact, businesses can ensure that resources are used efficiently to maximize profitability and strategic outcomes.
Practical Data Science with Amazon SageMaker is a comprehensive course designed for professionals seeking to leverage ML in cloud environments.
In the Practical Data Science with Amazon SageMaker course, students will learn to build, train, tune, and deploy machine learning models using AWS SageMaker.
