Amazon Web Services (AWS) - SageMaker Studio for Data Scientists

Looptijd

Looptijd:

Slechts 2 dagen

Methode

Methode:

Klas / Online / Hybride

Volgende datum

Volgende datum:

24/6/2024 (Maandag)

Overview

On this accelerated Amazon SageMaker Studio for Data Scientists course, you will learn to boost productivity at every step of the ML lifecycle with Amazon SageMaker Studio for Data Scientists from an expert AWS instructor.

This 2 day, advanced level course helps experienced data scientists build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows to reduce training time from hours to minutes with optimized infrastructure. This course includes presentations, demonstrations, discussions, labs, and at the end of the course, you’ll practice building an end-to-end tabular data ML project using SageMaker Studio and the SageMaker Python SDK.

Accelerate the preparation, building, training, deployment, and monitoring of ML solutions by using Amazon SageMaker Studio Use the tools that are part of SageMaker Studio to improve productivity at every step of the ML lifecycle And much more

At the end of this course, you’ll achieve your Amazon SageMaker Studio for Data Scientists certification.

Through Firebrand’s Lecture | Lab | Review methodology, you’ll get certified at twice the speed of the traditional training and get access to courseware, learn from certified instructors, and train in a distraction-free environment.

Audience

This course is ideal for:

  • Experienced data scientists who are proficient in ML and deep learning fundamentals.
  • People with relevant experience includes using ML frameworks, Python programming, and the process of building, training, tuning, and deploying models.

Zeven redenen waarom jij voor jouw cursus voor Firebrand kiest:

  1. Jij zal in slechts 2 dagen gecertificeerd zijn. Doordat onze cursussen residentieel zijn kunnen wij langere lesdagen aanbieden en zal je tijdens je verblijf volledig gefocust zijn op jouw cursus
  2. Onze cursus is all-inclusive. Cursusmaterialen, accommodatie en maaltijden zijn inbegrepen.
  3. Slaag de eerste keer voor of train gratis opnieuw.Op basis van onze certificeringsgarantie kun je voor het geval je de eerste keer niet slaagt binnen een jaar terugkomen en opnieuw trainen. Je betaalt dan alleen voor accommodatie en examens. De andere kosten zijn inbegrepen.
  4. Je zal meer over leren. Waar opleidingen elders doorgaans van 9:00 tot 17:00 duren, kan je bij Firebrand Training rekenen op 12 uur training per dag!
  5. Je zal sneller beheersen. Doordat onze cursussen residentieel zijn word je in korte tijd ondergedompeld in de theorie. Hierdoor zal je volledig gefocust zijn op de cursus en zal je sneller de theorie en praktijk beheersen.
  6. Je zal voor studeren bij de beste training provider. Firebrand heeft het Q-For kwaliteitlabel, waarmee onze standaarden en professionaliteit op het gebied van training erkend worden. We hebben inmiddels 134561 professionals getraind en gecertificeerd!
  7. Je gaat meer doen dan alleen de cursusstof van bestuderen. We maken gebruik van laboratoria, case-studies en oefentests, om ervoor te zorgen dat jij jouw nieuwe kennis direct in jouw werkomgeving kan toepassen.

Benefits

Curriculum

Module 1: Amazon SageMaker Setup and Navigation

  • Launch SageMaker Studio from the AWS Service Catalog.
  • Navigate the SageMaker Studio UI.
  • Demo 1: SageMaker UI Walkthrough
  • Lab 1: Launch SageMaker Studio from AWS Service Catalog

 

Module 2: Data Processing Use Amazon SageMaker Studio to collect, clean, visualize, analyze, and transform data.

  • Set up a repeatable process for data processing.
  • Use SageMaker to validate that collected data is ML ready.
  • Detect bias in collected data and estimate baseline model accuracy.
  • Lab 2: Analyze and Prepare Data Using SageMaker Data Wrangler
  • Lab 3: Analyze and Prepare Data at Scale Using Amazon EMR
  • Lab 4: Data Processing Using SageMaker Processing and the SageMaker Python SDK
  • Lab 5: Feature Engineering Using SageMaker Feature Store

 

Module 3: Model Development Use Amazon SageMaker Studio to develop, tune, and evaluate an ML model against business objectives and fairness and explainability best practices.

  • Fine-tune ML models using automatic hyperparameter optimization capability.
  • Use SageMaker Debugger to surface issues during model development.
  • Demo 2: Autopilot Lab 6: Track Iterations of Training and Tuning Models Using SageMaker Experiments
  • Lab 7: Analyze, Detect, and Set Alerts Using SageMaker Debugger
  • Lab 8: Identify Bias Using SageMaker Clarify

 

Module 4: Deployment and Inference Use Model Registry to create a model group; register, view, and manage model versions; modify model approval status; and deploy a model.

  • Design and implement a deployment solution that meets inference use case requirements.
  • Create, automate, and manage end-to-end ML workflows using Amazon SageMaker Pipelines.
  • Lab 9: Inferencing with SageMaker Studio
  • Lab 10: Using SageMaker Pipelines and the SageMaker Model Registry with SageMaker Studio

 

Module 5: Monitoring Configure a SageMaker Model Monitor solution to detect issues and initiate alerts for changes in data quality, model quality, bias drift, and feature attribution (explainability) drift.

  • Create a monitoring schedule with a predefined interval.
  • Demo 3: Model Monitoring

 

Module 6: Managing SageMaker Studio Resources and Updates List resources that accrue charges.

  • Recall when to shut down instances.
  • Explain how to shut down instances, notebooks, terminals, and kernels.
  • Understand the process to update SageMaker Studio.
  • Capstone:

The Capstone lab will bring together the various capabilities of SageMaker Studio discussed in this course. Students will be given the opportunity to prepare, build, train, and deploy a model using a tabular dataset not seen in earlier labs. Students can choose among basic, intermediate, and advanced versions of the instructions. Capstone Lab: Build an End-to-End Tabular Data ML Project Using SageMaker Studio and the SageMaker Python SDK.

Exam Track

At the end of this accelerated course, you’ll achieve your Amazon SageMaker Studio for Data Scientists.

What's Included

Prerequisites

Before attending this accelerated course, you should have:

  • Completed the following AWS course prior to attending this course: AWS Technical Essentials (AWSE)
  • Students who are not experienced data scientists complete the following two courses followed by 1-year on-the-job experience building models prior to taking this course: Machine Learning Pipeline on AWS (ML-PIPE) Deep Learning on AWS (AWSDL).

Weet je niet zeker of je aan de vereisten voldoet? Maak je geen zorgen. Jouw trainingsadviseur bespreekt jouw achtergrond met je om te begrijpen of deze cursus geschikt is voor je.

Beoordelingen

Wereldwijd heeft Firebrand in haar 10-jarig bestaan al 134561 studenten opgeleid! We hebben ze allemaal gevraagd onze versnelde opleidingen te evalueren. De laatste keer dat we onze resultaten analyseerden, bleek 96.41% ons te beoordelen als 'boven verwachting'


"Firebrand training is worth every penny. The courses are nicely organized, the instructors are authorized instructors with lots of experience. The arrangements regarding hotel and food are also managed well. Its a bootcamp, so be prepared with that mindset. The only room for improvement i would say, is add extra day or two for professional exams"
MK. (12/5/2023 (Vrijdag) t/m 14/5/2023 (Zondag))

"Quick dive in AWS ! Loved it!"
Daniel Penninck, smals. (26/10/2020 (Maandag) t/m 29/10/2020 (Donderdag))

"I can say that I definitely improved and expanded my AWS knowledge with this course. Loved the discussions and the depth, as well as the focus on achieving the Associate Certification by taking a look at real exam questions."
A.S. (26/10/2020 (Maandag) t/m 29/10/2020 (Donderdag))

"Really focused on the exam, with many examples and labs to dive deep on the content. With Instructor, this amount of services and offerings from AWS are much more likely easy to understand and distinguish."
A.R. (26/10/2020 (Maandag) t/m 29/10/2020 (Donderdag))

"The instructor was very knowledgeable and very friendly. He explains things really well!"
Abdul Hasnath, Dynatrace. (26/10/2020 (Maandag) t/m 29/10/2020 (Donderdag))

Cursusdata

Start datum

Eind datum

Status

Locatie

Nu boeken

19/2/2024 (Maandag)

20/2/2024 (Dinsdag)

Beëindigde cursus - Geef feedback

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24/6/2024 (Maandag)

25/6/2024 (Dinsdag)

Wachtlijst

Landelijk

 

5/8/2024 (Maandag)

6/8/2024 (Dinsdag)

Beperkte beschikbaarheid

Landelijk

 

16/9/2024 (Maandag)

17/9/2024 (Dinsdag)

Open

Landelijk

 

28/10/2024 (Maandag)

29/10/2024 (Dinsdag)

Open

Landelijk

 

9/12/2024 (Maandag)

10/12/2024 (Dinsdag)

Open

Landelijk

 

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