Together with Amazon Web Services (AWS), we have designed a solution for you, based on Amazon SageMaker, that allows you to develop, train and implement machine learning (ML) models in the AWS cloud for almost any use case.<\/p>\n<\/div>\n
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![\"\"](\"\/wp-content\/uploads\/2022\/03\/AWS_premier_tier_training_partner_badge.png\")
<\/p>\nAws Premium Consuting Partner<\/h3>\n<\/li>\n
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![\"\"](\"\/wp-content\/uploads\/2022\/03\/AWS_advanced_tier_consulting_partner_badge.png\")
<\/p>\nAws Advanced Training Partner<\/h3>\n<\/li>\n
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![\"aws](\"\/wp-content\/uploads\/2023\/02\/200-Cert-Light-English-1.png\")
<\/p>\nAws Cert<\/h3>\n<\/li>\n<\/ul>\n
![\"\"](\"\/wp-content\/uploads\/2022\/06\/stock_ML.png\")
<\/p>\nChallenges<\/h3>\n
\n\nOn-prem Machine Learning (ML)-Umgebungen sind komplex, ressourcenhungrig und nicht skalierbar.<\/p>\n
Auch k\u00f6nnen Data-Science-Teams oft nicht mit den stetig steigenden ML-Anforderungen der Gesch\u00e4ftsbereiche mithalten, da sie daf\u00fcr sorgen m\u00fcssen, dass die On-prem-ML-Infrastruktur immer betriebsbereit ist. Das bindet Zeit und Ressourcen, die dann f\u00fcr die eigentlichen ML-Projekte nicht mehr zur Verf\u00fcgung stehen.\u00a0<\/p>\n
F\u00fcr die Teams ist es oft ein Balanceakt Kosten und Leistung f\u00fcr die On-prem ML-Infrastruktur in einem ausgeglichenen Verh\u00e4ltnis zu halten.<\/p>\n<\/div>\n
Our Consulting Services<\/h3>\n
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- We are happy to support you in all phases of your cloud journey – based on AWS best practices and proven ML tools such as Amazon SageMaker.<\/li>\n
- A structured approach and a clear AWS cloud strategy are essential for the success of ML modernization.<\/li>\n
- And to accelerate ML modernization, companies should rely on experienced professionals like AWS and tecRacer right from the start.<\/li>\n<\/ul>\n<\/div>\n
AWS and tecRacer – OUR SOLUTION YOU CAN RELY ON<\/h3>\n
\n\nTo address the mentioned challenges, data science teams can standardize ML in the AWS cloud while also adopting ML best practices across the enterprise.<\/p>\n
ML modernization with Amazon SageMaker helps data science teams move their self-managed on-prem ML infrastructures to the AWS cloud.<\/p>\n
Amazon SageMaker provides a comprehensive and deep set of capabilities across the entire ML lifecycle (Prepare | Build | Train & Tune | Deploy | Manage). And it does so with infrastructure fully managed by AWS and services seamlessly integrated so that data science teams can focus 100% on their project work.<\/p>\n<\/div>\n
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YOUR BENEFITS FROM OUR SOLUTION<\/h3>\n<\/h3>\n
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![\"\"](\"\/wp-content\/uploads\/2022\/02\/tecracer_kosten_sparen_icon.png\")
<\/p>\nAffordable<\/h2>\n
\nHigh availability at lower costs (pay-per-use – no license costs)<\/p>\n<\/div>\n
Read More<\/a><\/p>\n<\/li>\n
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![\"\"](\"\/wp-content\/uploads\/2022\/09\/Scalability_Icon.png\")
<\/p>\nScalable<\/h2>\n
\nAmazon SageMaker enables access, tagging, and processing of even very large amounts of data, whether structured (such as tabular data), or unstructured (such as photos, video, and audio) for ML use cases.<\/p>\n<\/div>\n<\/li>\n
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![\"\"](\"\/wp-content\/uploads\/2022\/02\/tecracer_know_how_icon.png\")
<\/p>\nInnovation<\/h2>\n
\nIntegrated ML services for data scientists and no-code interfaces for business analysts enable innovative ML projects to be implemented across departments.<\/p>\n<\/div>\n
Read More<\/a><\/p>\n<\/li>\n
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![\"\"](\"\/wp-content\/uploads\/2022\/03\/tecracer_ressourcen_icon-1.png\")
<\/p>\nFOCUS ON CORE COMPETENCIES<\/h2>\n
\nData science teams can fully focus on use case development and ML projects instead of managing ML infrastructure.<\/p>\n<\/div>\n
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<\/p>\n![\"\"](\"\/wp-content\/uploads\/2022\/02\/tecracer_maximale_sicherheit_icon.png\"){kind=icon}
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