{"id":14560,"date":"2025-01-07T00:39:31","date_gmt":"2025-01-07T05:39:31","guid":{"rendered":"https:\/\/ptp.cloud\/?p=14560"},"modified":"2025-08-12T03:24:14","modified_gmt":"2025-08-12T07:24:14","slug":"ptp-boosts-biotech-productivity-with-aws-sagemaker","status":"publish","type":"post","link":"https:\/\/ptp.cloud\/ptp-boosts-biotech-productivity-with-aws-sagemaker\/","title":{"rendered":"PTP Empowers Biotech Teams with AWS SageMaker, Boosting Productivity by 10%"},"content":{"rendered":"[et_pb_section fb_built=&#8221;1&#8243; custom_padding_last_edited=&#8221;on|tablet&#8221; admin_label=&#8221;Section&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;#3e489d&#8221; background_image=&#8221;https:\/\/ptp.cloud\/wp-content\/uploads\/2024\/10\/Square-Pattern-Hero-Background.png&#8221; custom_padding=&#8221;5px||||false|false&#8221; custom_padding_tablet=&#8221;40px||40px||true|false&#8221; custom_padding_phone=&#8221;40px||40px||true|false&#8221; da_disable_devices=&#8221;off|off|off&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221; da_is_popup=&#8221;off&#8221; da_exit_intent=&#8221;off&#8221; da_has_close=&#8221;on&#8221; da_alt_close=&#8221;off&#8221; da_dark_close=&#8221;off&#8221; da_not_modal=&#8221;on&#8221; da_is_singular=&#8221;off&#8221; da_with_loader=&#8221;off&#8221; da_has_shadow=&#8221;on&#8221;][et_pb_row column_structure=&#8221;2_3,1_3&#8243; make_equal=&#8221;on&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; width=&#8221;85%&#8221; max_width=&#8221;1380px&#8221; custom_padding=&#8221;63px||1px|||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;2_3&#8243; module_class=&#8221;col-vert-cent&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_heading title=&#8221;How PTP Boosted Biotech Productivity by 10% with AWS SageMaker* in a Regulated Environment&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; title_font=&#8221;&#8211;et_global_heading_font|700|||||||&#8221; title_text_color=&#8221;#ffffff&#8221; title_font_size=&#8221;3.5rem&#8221; title_line_height=&#8221;1.2em&#8221; max_width_tablet=&#8221;620px&#8221; max_width_phone=&#8221;620px&#8221; max_width_last_edited=&#8221;on|tablet&#8221; custom_padding=&#8221;40px||||false|false&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_heading][\/et_pb_column][et_pb_column type=&#8221;1_3&#8243; module_class=&#8221;col-vert-cent&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_image src=&#8221;https:\/\/ptp.cloud\/wp-content\/uploads\/2024\/10\/PTP-Hero-Section-Goat-Illustration-min.png&#8221; alt=&#8221;Illustration of Goat working on servers leading data to the cloud and to a proved treatment&#8221; title_text=&#8221;PTP Hero Section Goat Illustration&#8221; align=&#8221;center&#8221; disabled_on=&#8221;on|off|off&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; max_width_tablet=&#8221;75%&#8221; max_width_phone=&#8221;75%&#8221; max_width_last_edited=&#8221;on|tablet&#8221; custom_padding=&#8221;25px||25px||true|false&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row make_equal=&#8221;on&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; width=&#8221;85%&#8221; max_width=&#8221;1380px&#8221; custom_margin=&#8221;|auto|29px|auto||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; module_class=&#8221;col-vert-cent&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;&#8211;et_global_heading_font|300|||||||&#8221; text_text_color=&#8221;#ffffff&#8221; text_font_size=&#8221;1.5rem&#8221; text_line_height=&#8221;2.1rem&#8221; max_width_tablet=&#8221;620px&#8221; max_width_phone=&#8221;620px&#8221; max_width_last_edited=&#8221;on|tablet&#8221; global_colors_info=&#8221;{}&#8221;]\n<p data-pm-slice=\"1 1 []\"><span>For early and mid-stage Biotechnology and Life Sciences companies, easily accessing all sources of data\u2014whether on-prem from the lab or in the cloud\u2014is critical for rapidly analyzing new molecules and assessing the validity of new drugs or treatments. Data teams often work in hybrid, remote, and asynchronous environments, requiring tools that enable collaboration and maintain version control. Additionally, these companies frequently handle sensitive individual and private health information, demanding robust security and compliance measures.<\/span><\/p>\n[\/et_pb_text][dsm_icon_list icon_font_size_tablet=&#8221;&#8221; icon_font_size_phone=&#8221;20px&#8221; icon_font_size_last_edited=&#8221;on|phone&#8221; module_class=&#8221;hero-stat&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font_size_tablet=&#8221;&#8221; text_font_size_phone=&#8221;13px&#8221; text_font_size_last_edited=&#8221;on|phone&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;][dsm_icon_list_child text=&#8221;*Resulted in a 10% productivity increase for data scientists by providing version-controlled, collaborative machine learning environments.&#8221; font_icon=&#8221;&#xf021;||fa||900&#8243; icon_padding=&#8221;1px&#8221; icon_font_size=&#8221;2rem&#8221; url_new_window=&#8221;on&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;&#8211;et_global_heading_font|500|on||||||&#8221; text_text_color=&#8221;#0ca8fc&#8221; text_font_size=&#8221;1.2rem&#8221; text_line_height=&#8221;1.25rem&#8221; text_font_size_tablet=&#8221;1.2rem&#8221; text_font_size_phone=&#8221;1rem&#8221; text_font_size_last_edited=&#8221;on|phone&#8221; global_colors_info=&#8221;{}&#8221;][\/dsm_icon_list_child][\/dsm_icon_list][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; admin_label=&#8221;section&#8221; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;32px||1px|||&#8221; da_disable_devices=&#8221;off|off|off&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221; da_is_popup=&#8221;off&#8221; da_exit_intent=&#8221;off&#8221; da_has_close=&#8221;on&#8221; da_alt_close=&#8221;off&#8221; da_dark_close=&#8221;off&#8221; da_not_modal=&#8221;on&#8221; da_is_singular=&#8221;off&#8221; da_with_loader=&#8221;off&#8221; da_has_shadow=&#8221;on&#8221;][et_pb_row column_structure=&#8221;1_2,1_2&#8243; make_equal=&#8221;on&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; width=&#8221;85%&#8221; max_width=&#8221;1380px&#8221; custom_padding=&#8221;8px||1px|||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;1_2&#8243; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;&#8211;et_global_heading_font||||||||&#8221; text_text_color=&#8221;gcid-secondary-color&#8221; text_font_size=&#8221;28px&#8221; text_line_height=&#8221;2rem&#8221; custom_margin=&#8221;45px||||false|false&#8221; custom_padding=&#8221;||||false|false&#8221; text_font_size_tablet=&#8221;28px&#8221; text_font_size_phone=&#8221;24px&#8221; text_font_size_last_edited=&#8221;on|phone&#8221; global_colors_info=&#8221;{%22gcid-secondary-color%22:%91%22text_text_color%22%93}&#8221;]\n<h2><span style=\"font-size: xx-large; color: #0c71c3;\"><strong>The Challenge<\/strong><\/span><\/h2>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=&#8221;1_2&#8243; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_column][\/et_pb_row][et_pb_row make_equal=&#8221;on&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; width=&#8221;85%&#8221; max_width=&#8221;1380px&#8221; custom_padding=&#8221;2px||63px|||&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; module_class=&#8221;col-vert-cent&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;&#8211;et_global_heading_font|300|||||||&#8221; text_text_color=&#8221;gcid-secondary-color&#8221; text_font_size=&#8221;28px&#8221; text_line_height=&#8221;2.3rem&#8221; custom_margin=&#8221;||7px|||&#8221; text_font_size_tablet=&#8221;28px&#8221; text_font_size_phone=&#8221;24px&#8221; text_font_size_last_edited=&#8221;on|phone&#8221; global_colors_info=&#8221;{%22gcid-secondary-color%22:%91%22text_text_color%22,%22text_text_color%22,%22text_text_color%22%93}&#8221;]\n<p><span style=\"color: rgba(0, 0, 0, 0.73);\">A PTP client, dedicated to modifying medicines by identifying validated targets and advancing multiple therapeutic modalities, sought an improved method for collaborative version control of data in <a href=\"https:\/\/aws.amazon.com\/\">AWS<\/a>. They needed a solution that could support dozens of computational biologists leveraging <a href=\"https:\/\/aws.amazon.com\/sagemaker\/\">Amazon SageMaker<\/a>\u2019s machine learning models to accelerate data analysis.<\/span><span style=\"color: rgba(0, 0, 0, 0.7);\"><\/span><\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; custom_padding_last_edited=&#8221;on|tablet&#8221; next_background_color=&#8221;#ffffff&#8221; admin_label=&#8221;Section&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;#3e489d&#8221; background_image=&#8221;https:\/\/ptp.cloud\/wp-content\/uploads\/2024\/10\/Square-Pattern-Hero-Background.png&#8221; custom_padding=&#8221;||52px||false|false&#8221; custom_padding_tablet=&#8221;40px||40px||true|false&#8221; custom_padding_phone=&#8221;40px||40px||true|false&#8221; bottom_divider_style=&#8221;arrow&#8221; bottom_divider_height=&#8221;83px&#8221; da_disable_devices=&#8221;off|off|off&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221; da_is_popup=&#8221;off&#8221; da_exit_intent=&#8221;off&#8221; da_has_close=&#8221;on&#8221; da_alt_close=&#8221;off&#8221; da_dark_close=&#8221;off&#8221; da_not_modal=&#8221;on&#8221; da_is_singular=&#8221;off&#8221; da_with_loader=&#8221;off&#8221; da_has_shadow=&#8221;on&#8221;][et_pb_row make_equal=&#8221;on&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; width=&#8221;85%&#8221; max_width=&#8221;1380px&#8221; custom_padding=&#8221;||99px|||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; module_class=&#8221;col-vert-cent&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;&#8211;et_global_heading_font||||||||&#8221; text_text_color=&#8221;gcid-secondary-color&#8221; text_font_size=&#8221;28px&#8221; text_line_height=&#8221;2rem&#8221; custom_margin=&#8221;||0px||false|false&#8221; custom_padding=&#8221;||16px|||&#8221; text_font_size_tablet=&#8221;28px&#8221; text_font_size_phone=&#8221;24px&#8221; text_font_size_last_edited=&#8221;on|phone&#8221; global_colors_info=&#8221;{%22gcid-secondary-color%22:%91%22text_text_color%22%93}&#8221;]\n<h2><span style=\"font-size: xx-large; color: #ffffff;\"><strong>The Solution<\/strong><\/span>\u00a0<\/h2>\n[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;&#8211;et_global_heading_font|300|||||||&#8221; text_text_color=&#8221;#ffffff&#8221; text_font_size=&#8221;28px&#8221; text_line_height=&#8221;2.3rem&#8221; custom_margin=&#8221;||0px|||&#8221; text_font_size_tablet=&#8221;28px&#8221; text_font_size_phone=&#8221;24px&#8221; text_font_size_last_edited=&#8221;on|phone&#8221; global_colors_info=&#8221;{}&#8221;]\n<p>PTP architected a tailored solution to address the client\u2019s challenges, deploying a collaborative environment powered by Amazon SageMaker:<\/p>\n<p><strong>Custom Docker Images and PyPI Packages<br \/><\/strong>Deployed custom Docker images with private Python Package Index (PyPI) packages into SageMaker to enable secure collaboration.<\/p>\n<p>Created pre-defined environments with specific Python packages for seamless integration.<\/p>\n<p><strong>Shared Storage Access<br \/><\/strong>Integrated shared storage for on-prem, third-party CRO, and Amazon S3 data, enabling seamless access to Jupyter notebooks across users.<\/p>\n<p><strong>Custom IPython Kernels<br \/><\/strong>Built custom IPython kernels for interacting with SageMaker Studio\u2019s Jupyter notebooks, ensuring standardized working environments.<\/p>\n<p>Kernels were deployed in Docker containers, automatically pulled by SageMaker during notebook instance creation.<\/p>\n<p><strong>Enhanced Data Security<br \/><\/strong>Designed and deployed a private PyPI server to secure Personal Identifiable Information (PII) and ensure regulatory compliance.<\/p>\n<p>Configured the environment to integrate with the company\u2019s Single Sign-On (SSO) and Multi-Factor Authentication (MFA), following best security practices.<\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; admin_label=&#8221;section&#8221; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;32px||1px|||&#8221; da_disable_devices=&#8221;off|off|off&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221; da_is_popup=&#8221;off&#8221; da_exit_intent=&#8221;off&#8221; da_has_close=&#8221;on&#8221; da_alt_close=&#8221;off&#8221; da_dark_close=&#8221;off&#8221; da_not_modal=&#8221;on&#8221; da_is_singular=&#8221;off&#8221; da_with_loader=&#8221;off&#8221; da_has_shadow=&#8221;on&#8221;][et_pb_row make_equal=&#8221;on&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; width=&#8221;85%&#8221; max_width=&#8221;1380px&#8221; custom_margin=&#8221;36px|auto||auto||&#8221; custom_padding=&#8221;2px||42px|||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;&#8211;et_global_heading_font|300|||||||&#8221; text_text_color=&#8221;#474747&#8243; text_font_size=&#8221;27px&#8221; text_line_height=&#8221;2rem&#8221; custom_margin=&#8221;||3px|||&#8221; custom_padding=&#8221;||25px|||&#8221; text_font_size_tablet=&#8221;28px&#8221; text_font_size_phone=&#8221;24px&#8221; text_font_size_last_edited=&#8221;on|phone&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;]<em><img decoding=\"async\" src=\"https:\/\/ptp.cloud\/wp-content\/uploads\/2025\/01\/aws-sagemaker-collaborative-architecture.png\" alt=\"Diagram illustrating an AWS SageMaker architecture featuring SageMaker Studio Lab, EFS-backed storage, Docker containers from ECR, and secure user authentication with AWS Identity Center and Okta.\" style=\"display: block; margin-left: auto; margin-right: auto;\" \/><\/em><em>The AWS SageMaker architecture deployed for collaborative machine learning environments in a biotech use case, including shared storage, secure authentication with Okta, and integration with Docker containers from ECR.<\/em><\/p>\n<h2><span style=\"font-size: xx-large; color: #0c71c3;\"><strong><\/strong><\/span><\/h2>\n<p><span style=\"font-size: xx-large; color: #0c71c3;\"><strong><\/strong><\/span><\/p>\n<h2><span style=\"font-size: xx-large; color: #0c71c3;\"><strong>The Outcome<\/strong><\/span><\/h2>\n<p><span style=\"font-size: xx-large; color: #0c71c3;\"><strong><\/strong><\/span><\/p>\n<p>PTP\u2019s expertise in biotechnology workflows, Amazon SageMaker, and machine learning allowed for the successful deployment of a collaborative data science environment:[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;&#8211;et_global_heading_font|300|||||||&#8221; text_text_color=&#8221;rgba(0,0,0,0.66)&#8221; text_font_size=&#8221;28px&#8221; text_line_height=&#8221;2.3rem&#8221; custom_padding=&#8221;34px|25px|34px|25px|true|true&#8221; text_font_size_tablet=&#8221;28px&#8221; text_font_size_phone=&#8221;24px&#8221; text_font_size_last_edited=&#8221;on|phone&#8221; border_radii=&#8221;on|2px|2px|2px|2px&#8221; border_width_all=&#8221;1px&#8221; global_colors_info=&#8221;{}&#8221;]\n<p><strong>10% Productivity Increase<\/strong><br \/>Improved productivity for data scientists through version-controlled, collaborative machine learning environments.<\/p>\n<p><strong>Accelerated Medicine Development<\/strong><br \/>Faster analysis and iteration supported the development of neurogenetic medicines.<\/p>\n<p><strong>Robust Security and Compliance<\/strong><br \/>Ensured data integrity and privacy through stringent security measures, meeting regulatory requirements.<\/p>\n[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;&#8211;et_global_heading_font|300|||||||&#8221; text_text_color=&#8221;#474747&#8243; text_font_size=&#8221;27px&#8221; text_line_height=&#8221;2rem&#8221; custom_margin=&#8221;||3px|||&#8221; custom_padding=&#8221;||25px|||&#8221; text_font_size_tablet=&#8221;28px&#8221; text_font_size_phone=&#8221;24px&#8221; text_font_size_last_edited=&#8221;on|phone&#8221; 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for rapidly analyzing new molecules and assessing the validity of new drugs or treatments. Data teams often work in hybrid, remote, and asynchronous environments, requiring tools that enable collaboration and maintain [&hellip;]<\/p>\n","protected":false},"author":445,"featured_media":16341,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"<p>In the fascinating and high-stakes domain of biotechnology, research validation and acquisition of the next wave of funding stand as crucial checkpoints in the journey of a startup. Amid an environment characterized by escalating scrutiny and disturbing reports of manipulated outcomes, it becomes critical to build an infrastructure that is not only efficient but also transparent and reliable. Our client in the startup life sciences space needed to build out their environment for informatics on AWS while requiring PTP help significantly expedite the research process and, most importantly, validate its authenticity.<\/p><p>The primary goal in fortifying the validity of research is in streamlining and automating the data processing pipelines. The science required the expertise to support scaling homegrown pipelines, industry-leading solutions such as Nextflow, which provides a scalable and reproducible scientific workflow using software containers, as well as open-source conglomerates like Cell Ranger, Seurat, Picard, and Star Aligner, which have shown proven results in genomics and computational biology. This client demanded a cloud pipeline solution that was automated, repeatable, easily changeable and fully documents to ensure research validation. These solutions, when interlaced with robust AWS offerings like EC2, ELB, Auto Scaling, Lambda, and Fargate, create a scalable, cost-efficient, and high-throughput data processing solution that stands all the major test of validation.<\/p><p>PTP leveraged EC2 Image Builder and Service Catalogs to produce images in a controlled and repeatable manner. This allows for scientists and informaticians to independently launch pipelines through Service Catalog. These users have limited permissions to just launch Service Catalog everything else is controlled through the code process and permissions are minimized by the security group for control.<\/p><p>PTP centralized the building of images in one account and that account shares across the organization into those required accounts which exchange information between accounts with Amazon Parameter Store.<\/p><p>Image building was automated using EC2 Image Builder allowing PTP to build different standard images for different functions. From there the team created a recipe in Image Builder containing the software components that make up the image and defines the ownership of the component. This provides complete documentation on what software and versions are installed, which in life sciences is essential for controlling variables and seeking research validation. This Build account has access to private and controlled code repositories so that software version can be frozen or recreated from any point in time<\/p><p>These builds were all written into Terraform to maintain the image files and component lists and version controlled by AWS Code Commit. As components change in Terraform, for example a software update to \u201cversion 4.2\u201d, Terraform will know the file has changed and will deploy a new version of the component which then creates a new version of the recipe in Image Builder.<\/p><p>For cost optimization, the Service Catalog services are tied to Cloudwatch events that trigger when devices go idle, then SQS queue and Lambda are used to terminate resources they go idle for a period of time. When services\/images are recreated, they are automatically reconnected to persistent storage. \u00a0Going forward, PTP is working with this client to incorporate Amazon WorkSpaces and AWS Managed AD to further isolate data and create additional levels of control and security.<\/p><p>The result of this design and infrastructure-as-code implementation is a data management platform that will aid in the effort of research validation due to the limitation of variables and changes. The team also estimates between a 50-75% savings reduction driven through the automated deployment and tear-down of resources for use only when called upon verses building a traditional cloud computing environment. Lastly, the least-privilege access configurations enhance the protection of sensitive data which aligns with the consistent approach to the build of a Well Architected AWS environment.<\/p><p>\u00a0<\/p><h3>Purchase PTP's <a href=\"https:\/\/aws.amazon.com\/marketplace\/pp\/prodview-it7fjq6rqix74?sr=0-13&ref_=beagle&applicationId=AWSMPContessa\">CloudOps Offer<\/a> on AWS Marketplace!<\/h3><p>\u00a0<\/p><h3>Learn More about PTP's CloudOps <a href=\"https:\/\/ptp.cloud\/cloud-ops\/\">HERE<\/a><\/h3>","_et_gb_content_width":"","content-type":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[23,12,14],"tags":[76],"table_tags":[],"class_list":["post-14560","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-aws-archive","category-aws-for-life-sciences-archive","category-case-studies-archive","tag-aws"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.1.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Increasing Biotech Productivity by 10% With AWS SageMaker<\/title>\n<meta name=\"description\" content=\"Discover how PTP leveraged AWS SageMaker to increase biotech productivity by 10% with secure, collaborative data science workflows.\" \/>\n<meta name=\"robots\" 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