{"id":238,"date":"2025-07-07T01:19:45","date_gmt":"2025-07-07T01:19:45","guid":{"rendered":"https:\/\/www.phaseglobaldatascientific.com\/?p=238"},"modified":"2025-07-17T15:48:17","modified_gmt":"2025-07-17T15:48:17","slug":"aws-data-science-analysis-and-ml-pipeline-platform-databricks-spark-sagemaker","status":"publish","type":"post","link":"https:\/\/www.phaseglobaldatascientific.com\/index.php\/2025\/07\/07\/aws-data-science-analysis-and-ml-pipeline-platform-databricks-spark-sagemaker\/","title":{"rendered":"AWS Data Science Analysis and ML Pipeline Platform: Databricks Spark, Sagemaker"},"content":{"rendered":"\n<details class=\"wp-block-details is-layout-flow wp-block-details-is-layout-flow\" open><summary>Objectives<\/summary>\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Cohesive environment for time-effective Data Science experiments on big data<\/li>\n\n\n\n<li>Production env capable ML pipelining<\/li>\n<\/ul>\n<\/details>\n\n\n\n<details class=\"wp-block-details is-layout-flow wp-block-details-is-layout-flow\" open><summary>Existing Challenges<\/summary>\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>MLops challenges. i.e. IAM policies, resource configs for model type, feature stores<\/li>\n\n\n\n<li>Long experimentation cycle time<\/li>\n\n\n\n<li>Data Scientists lack independence in data procuration and library setup<\/li>\n\n\n\n<li>Lack of Production env capable processing<\/li>\n\n\n\n<li>Disparate data sets<\/li>\n<\/ul>\n<\/details>\n\n\n\n<details class=\"wp-block-details is-layout-flow wp-block-details-is-layout-flow\" open><summary>Solutions<\/summary>\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Exploratory work and development of models are done via SageMaker Studio by Data Scientists<\/li>\n\n\n\n<li>SageMaker Studio SSO and MFA integration and isolated S3 paths satisfy enterprise dev ops compliance<\/li>\n\n\n\n<li>Databricks for big-data batch processing and S3 for training dataset storage<\/li>\n\n\n\n<li>Productionized jobs via Sagemaker py API deployable via traditional existing CICD<\/li>\n<\/ul>\n<\/details>\n\n\n\n<details class=\"wp-block-details is-layout-flow wp-block-details-is-layout-flow\" open><summary>Benefits<\/summary>\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Time-effective data science work<\/li>\n\n\n\n<li>Productionized models maintainable by staff DE and Ops teams<\/li>\n<\/ul>\n<\/details>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":51,"comment_status":"open","ping_status":"open","sticky":false,"template":"single-case-study","format":"standard","meta":{"footnotes":""},"categories":[6,17,7],"tags":[],"class_list":["post-238","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analysis-data-science","category-finance-banking","category-ml-engineering"],"_links":{"self":[{"href":"https:\/\/www.phaseglobaldatascientific.com\/index.php\/wp-json\/wp\/v2\/posts\/238","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.phaseglobaldatascientific.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.phaseglobaldatascientific.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.phaseglobaldatascientific.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.phaseglobaldatascientific.com\/index.php\/wp-json\/wp\/v2\/comments?post=238"}],"version-history":[{"count":1,"href":"https:\/\/www.phaseglobaldatascientific.com\/index.php\/wp-json\/wp\/v2\/posts\/238\/revisions"}],"predecessor-version":[{"id":239,"href":"https:\/\/www.phaseglobaldatascientific.com\/index.php\/wp-json\/wp\/v2\/posts\/238\/revisions\/239"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.phaseglobaldatascientific.com\/index.php\/wp-json\/wp\/v2\/media\/51"}],"wp:attachment":[{"href":"https:\/\/www.phaseglobaldatascientific.com\/index.php\/wp-json\/wp\/v2\/media?parent=238"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.phaseglobaldatascientific.com\/index.php\/wp-json\/wp\/v2\/categories?post=238"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.phaseglobaldatascientific.com\/index.php\/wp-json\/wp\/v2\/tags?post=238"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}