{"course":{"productid":30921,"modality":1,"active":true,"language":"de","title":"Amazon SageMaker Studio for Data Scientists","productcode":"ASSDS","vendorcode":"AW","vendorname":"Amazon Web Services","fullproductcode":"AW-ASSDS","courseware":{"has_ekit":true,"has_printkit":false,"language":""},"url":"https:\/\/portal.flane.ch\/course\/amazon-assds","objective":"<h4>In this course, you will learn to:<\/h4><ul>\n<li>Accelerate the process to prepare, build, train, deploy, and monitor ML solutions using Amazon SageMaker Studio<\/li><\/ul>","essentials":"<p>We recommend that all attendees of this course have:\n<\/p>\n<ul>\n<li>Experience using ML frameworks<\/li><li>Python programming experience<\/li><li>At least 1 year of experience as a data scientist responsible for training, tuning, and deploying models<\/li><li>AWS Technical Essentials digital or classroom training<\/li><\/ul>","audience":"<p>Experienced data scientists who are proficient in ML and deep learning fundamentals.<\/p>","outline":"<p><strong>Day 1<\/strong><\/p>\n\n<h5>Module 1: Amazon SageMaker Studio Setup<\/h5><ul>\n<li>JupyterLab Extensions in SageMaker Studio<\/li><li>Demonstration: SageMaker user interface demo<\/li><\/ul><h5>Module 2: Data Processing<\/h5><ul>\n<li>Using SageMaker Data Wrangler for data processing<\/li><li>Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler<\/li><li>Using Amazon EMR<\/li><li>Hands-On Lab: Analyze and prepare data at scale using Amazon EMR<\/li><li>Using AWS Glue interactive sessions<\/li><li>Using SageMaker Processing with custom scripts<\/li><li>Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker Python SDK<\/li><li>SageMaker Feature Store<\/li><li>Hands-On Lab: Feature engineering using SageMaker Feature Store<\/li><\/ul><h5>Module 3: Model Development<\/h5><ul>\n<li>SageMaker training jobs<\/li><li>Built-in algorithms<\/li><li>Bring your own script<\/li><li>Bring your own container<\/li><li>SageMaker Experiments<\/li><li>Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models<\/li><\/ul><p><strong>Day 2<\/strong><\/p>\n<h5>Module 3: Model Development (continued)<\/h5><ul>\n<li>SageMaker Debugger<\/li><li>Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger<\/li><li>Automatic model tuning<\/li><li>SageMaker Autopilot: Automated ML<\/li><li>Demonstration: SageMaker Autopilot<\/li><li>Bias detection<\/li><li>Hands-On Lab: Using SageMaker Clarify for Bias and Explainability<\/li><li>SageMaker Jumpstart<\/li><\/ul>\n<h5>Module 4: Deployment and Inference<\/h5><ul>\n<li>SageMaker Model Registry<\/li><li>SageMaker Pipelines<\/li><li>Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio<\/li><li>SageMaker model inference options<\/li><li>Scaling<\/li><li>Testing strategies, performance, and optimization<\/li><li>Hands-On Lab: Inferencing with SageMaker Studio<\/li><\/ul>\n<h5>Module 5: Monitoring<\/h5><ul>\n<li>Amazon SageMaker Model Monitor<\/li><li>Discussion: Case study<\/li><li>Demonstration: Model Monitoring<\/li><\/ul><p><strong>Day 3<\/strong><\/p>\n<h5>Module 6: Managing SageMaker Studio Resources and Updates<\/h5><ul>\n<li>Accrued cost and shutting down<\/li><li>Updates<\/li><\/ul>\n<h5>Capstone<\/h5><ul>\n<li>Environment setup<\/li><li>Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler<\/li><li>Challenge 2: Create feature groups in SageMaker Feature Store<\/li><li>Challenge 3: Perform and manage model training and tuning using SageMaker Experiments<\/li><li>(Optional) Challenge 4: Use SageMaker Debugger for training performance and model optimization<\/li><li>Challenge 5: Evaluate the model for bias using SageMaker Clarify<\/li><li>Challenge 6: Perform batch predictions using model endpoint<\/li><li>(Optional) Challenge 7: Automate full model development process using SageMaker Pipeline<\/li><\/ul>","summary":"<p>Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models quickly. It does this by bringing together a broad set of capabilities purpose-built for ML. This course prepares experienced data scientists to use the tools that are a part of SageMaker Studio, including Amazon CodeWhisperer and Amazon CodeGuru Security scan extensions, to improve productivity at every step of the ML lifecycle.<\/p>","objective_plain":"In this course, you will learn to:\n\n\n- Accelerate the process to prepare, build, train, deploy, and monitor ML solutions using Amazon SageMaker Studio","essentials_plain":"We recommend that all attendees of this course have:\n\n\n\n- Experience using ML frameworks\n- Python programming experience\n- At least 1 year of experience as a data scientist responsible for training, tuning, and deploying models\n- AWS Technical Essentials digital or classroom training","audience_plain":"Experienced data scientists who are proficient in ML and deep learning fundamentals.","outline_plain":"Day 1\n\n\nModule 1: Amazon SageMaker Studio Setup\n\n\n- JupyterLab Extensions in SageMaker Studio\n- Demonstration: SageMaker user interface demo\nModule 2: Data Processing\n\n\n- Using SageMaker Data Wrangler for data processing\n- Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler\n- Using Amazon EMR\n- Hands-On Lab: Analyze and prepare data at scale using Amazon EMR\n- Using AWS Glue interactive sessions\n- Using SageMaker Processing with custom scripts\n- Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker Python SDK\n- SageMaker Feature Store\n- Hands-On Lab: Feature engineering using SageMaker Feature Store\nModule 3: Model Development\n\n\n- SageMaker training jobs\n- Built-in algorithms\n- Bring your own script\n- Bring your own container\n- SageMaker Experiments\n- Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models\nDay 2\n\nModule 3: Model Development (continued)\n\n\n- SageMaker Debugger\n- Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger\n- Automatic model tuning\n- SageMaker Autopilot: Automated ML\n- Demonstration: SageMaker Autopilot\n- Bias detection\n- Hands-On Lab: Using SageMaker Clarify for Bias and Explainability\n- SageMaker Jumpstart\n\nModule 4: Deployment and Inference\n\n\n- SageMaker Model Registry\n- SageMaker Pipelines\n- Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio\n- SageMaker model inference options\n- Scaling\n- Testing strategies, performance, and optimization\n- Hands-On Lab: Inferencing with SageMaker Studio\n\nModule 5: Monitoring\n\n\n- Amazon SageMaker Model Monitor\n- Discussion: Case study\n- Demonstration: Model Monitoring\nDay 3\n\nModule 6: Managing SageMaker Studio Resources and Updates\n\n\n- Accrued cost and shutting down\n- Updates\n\nCapstone\n\n\n- Environment setup\n- Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler\n- Challenge 2: Create feature groups in SageMaker Feature Store\n- Challenge 3: Perform and manage model training and tuning using SageMaker Experiments\n- (Optional) Challenge 4: Use SageMaker Debugger for training performance and model optimization\n- Challenge 5: Evaluate the model for bias using SageMaker Clarify\n- Challenge 6: Perform batch predictions using model endpoint\n- (Optional) Challenge 7: Automate full model development process using SageMaker Pipeline","summary_plain":"Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models quickly. It does this by bringing together a broad set of capabilities purpose-built for ML. This course prepares experienced data scientists to use the tools that are a part of SageMaker Studio, including Amazon CodeWhisperer and Amazon CodeGuru Security scan extensions, to improve productivity at every step of the ML lifecycle.","skill_level":"Intermediate","version":"1.0","duration":{"unit":"d","value":3,"formatted":"3 Tage"},"pricelist":{"List Price":{"DE":{"country":"DE","currency":"EUR","taxrate":19,"price":1990},"AT":{"country":"AT","currency":"EUR","taxrate":20,"price":1990},"SE":{"country":"SE","currency":"EUR","taxrate":25,"price":1990},"US":{"country":"US","currency":"USD","taxrate":null,"price":2025},"IT":{"country":"IT","currency":"EUR","taxrate":20,"price":1650},"AE":{"country":"AE","currency":"USD","taxrate":5,"price":1800},"GB":{"country":"GB","currency":"GBP","taxrate":20,"price":2655},"NL":{"country":"NL","currency":"EUR","taxrate":21,"price":2195},"CH":{"country":"CH","currency":"CHF","taxrate":8.1,"price":2480},"CA":{"country":"CA","currency":"CAD","taxrate":null,"price":2795},"SI":{"country":"SI","currency":"EUR","taxrate":20,"price":1990}}},"lastchanged":"2026-03-25T13:01:57+01:00","parenturl":"https:\/\/portal.flane.ch\/swisscom\/json-courses","nexturl_course_schedule":"https:\/\/portal.flane.ch\/swisscom\/json-course-schedule\/30921","source_lang":"de","source":"https:\/\/portal.flane.ch\/swisscom\/json-course\/amazon-assds"}}