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Amazon Web Services (AWS) - SageMaker Studio for Data Scientists
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.
Sju anledningar till varför du bör välja din kurs med Firebrand Training
- Två utbildningsalternativ. Välj mellan boende på plats med klassrumsundervisning eller onlinekurser
- Du blir certifierad snabbt. Hos oss blir du utbildad rekordsnabbt
- Vår kurs är heltäckande. En engångsavgift täcker alla kursmaterial, examina**, boende* och måltider*. Inga dolda extra kostnader.
- Godkänn första gången eller träna om gratis. Detta är vår garanti. Vi är övertygade om att du kommer klara kursen på första försöket. Men om inte, kom tillbaka inom ett år och betala endast för boende, examina och tillkommande kostnader
- Du kommer lära dig mer. En dag med en traditionell utbildningsleverantör brukar generellt pågå från kl. 9 till 17, med en lång paus för lunch. Med Firebrand Training får du minst 12 timmar/dag av kvalitativ inlärningstid med din instruktör
- Du kommer lära dig snabbare. Sannolikheten är att du har en annan inlärningsstil än de omkring dig. Vi kombinerar visuella, auditiva och taktila stilar för att leverera materialet på ett sätt som säkerställer att du lär dig snabbare och enklare
- Du kommer studera med de bästa. Vi har varit med på Training Industrys lista "Top 20 IT Training Companies of the Year" varje år sedan 2010. Förutom många fler utmärkelser har vi utbildat och certifierat över 100 000 yrkesverksamma
- * Endast för boende på plats. Gäller inte för onlinekurser
- ** Vissa undantag gäller. Vänligen se Exam Track eller prata med våra experter
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).
Är du redo för din Firebrand kurs?
Vi intervjuar alla potentiella deltagare om deras bakgrund, utbildning, certifiering och personliga inställning. Om du kommer igenom denna process så har du riktigt goda möjligheter att klara din certifiering.
Firebrand Training erbjuder en ambitiös utbildningsmiljö som förutsätter att du verkligen dedikerar dig till kursen. Ovanstående förkunskaper är endast vägledande, många deltagare med mindre erfarenhet men med en annan bakgrund eller kunskap har framgångsrikt genomfört sin utbildning hos Firebrand Training.
Om du funderar på huruvida du uppfyller rekommenderade förkunskaper vänligen ring oss på (0)8 44 68 27 85 och prata med en av våra utbildningsrådgivare som kan hjälpa dig.
Kundreferenser
Här är Firebrand Training recension avsnittet. Sedan 2001 har vi utbildat exakt 134.561 studenter och proffs och bett dem alla att recensera vår Accelerated Learning. För närvarande har, 96,41% sagt att Firebrand överträffade deras förväntningar.
Läs recensioner från avslutade accelereradekurser nedan eller besök Firebrand Stories för skriftliga och videointervjuer med våra tidigare alumni.
"Very impressed with the dedication from the instructor and how well structured the sessions are. The course is very intense since it''s only 3 days long, however, the quality of the training makes it worthwhile. I''d recommend going in person since you will find it much easier to focus and likely get more out of the course overall."
Simon Brown, Softcat. (8/9/2023 (Fredag) till 10/9/2023 (Söndag))
"Enrolling in Firebrand Training''s AWS Solution Architect Associate course online instructor-led program was an outstanding decision for my career. The course was not only comprehensive but also delivered by highly knowledgeable instructors who made complex AWS concepts easy to grasp. The interactive nature of the online sessions and the practical hands-on labs ensured that I gained a deep understanding of AWS solutions and best practices. I can confidently say that Firebrand Training played a pivotal role in helping me achieve AWS Solution Architect Associate certification. I highly recommend this course to anyone looking to excel in AWS cloud architecture."
Omar Anwar. (4/9/2023 (Måndag) till 7/9/2023 (Torsdag))
"Course provided all the key knowledge required to prepare me for my organisation currently introducing AWS migrations"
AB, Marketaxess. (31/7/2023 (Måndag) till 1/8/2023 (Tisdag))
"Trainer was excellent, a lot of information, great experience"
Olawale Abdullahi, BPP University. (31/7/2023 (Måndag) till 1/8/2023 (Tisdag))
"The trainer provided excellent delivery and commitment to enabling me to understand the AWS platform, services and how they interact with each other. the topics where discussed in great depth with live demos and real world analogies and examples to help embed the knowledge rather than just reading from a slide deck. the course is extremely intense but the way the content delivery was structured with a mix of slides, live demos, labs and open classroom discussions helped enable me to gain a great insight into the AWS platform."
Matthew Bingham, Softcat PLC. (24/7/2023 (Måndag) till 27/7/2023 (Torsdag))
Kursdatum
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19/2/2024 (Måndag) |
20/2/2024 (Tisdag) |
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