m4 template

_site/assets/js/search-data.json

@@ -169,7 +169,7 @@
   },"24": {
     "doc": "Milestone 4",
     "title": "Milestone 4",
-    "content": "Milestone 4 (internal combustion engine): Midterm Presentation: Optimization and Deployment of Scalable Data Solutions . Note: This milestone serves as the midterm presentation for AC215. The technical components of this milestone focus on the optimization and workflow orchestration aspects of a complex data-driven project. It emphasizes efficiency and scalability, utilizing advanced techniques and tools like TensorFlow Lite for model optimization and Kubeflow for machine learning workflows. The fourth milestone builds on the foundational work of the first three, driving the project towards completion and ensuring readiness for real-world application. This will provide students with exposure to industry-standard best practices and hands-on experience with cutting-edge tools and methodologies. Finally, the milestone serves as an opportunity to practice presenting information to a technical audience in an engaging and concise manner. Key dates: . | Presentation: Tues, Oct 24th or Thurs, Oct 26th (see Ed post for further details) | Slides due : Noon EST - Tues, Oct 24th or Thurs, Oct 26th (see Ed post for further details) | Repo due date: Friday, Oct 27th 9PM EST | . Objectives: . | Distillation/Quantization/Compression, TF lite: Implement methods for model optimization such as distillation, quantization, and compression, using TensorFlow Lite. This will enable deployment in resource-constrained environments. | Kubeflow and Cloud Functions Integration: Utilize Kubeflow for machine learning workflows and integrate cloud functions to automate and scale various processes within the project, aligning with cloud-native practices. | Presenting a Technical Project: Create a presentation that concisely covers what has been accomplished up to this point, and what the plan is for next steps. Here are some useful questions to think about when creating the presentation: Who is the audience (technical or non-technical), and what information can you expect them to know (and not know) going into the presentation? What’s the story that you are trying to tell? How are you planning to tell that story (e.g. slide structure, visuals, visuals, demo)? What do you want the audience to take away from the presentation? . | . Deliverables: . | Optimized Models: Models that have been distilled, quantized, or compressed using TensorFlow Lite, complete with performance benchmarks and analysis. | Kubeflow & Cloud Functions Implementation: Documentation and code showcasing the successful integration of Kubeflow for machine learning orchestration and cloud functions for process automation. | Presentation: A 4 minute presentation that includes a brief overview of the project to help orient the audience, a walk-through of the work that has been completed, and a brief outline of next steps. The walk-through does not need to be a live demo (it can be a set of well-made, visually-pleasing slides), but it should make sure to showcase and highlight the various components that have been built. Be prepared for 1 additional minute of questions at the end for a total of 5 minutes on stage. | . ",
+    "content": "Milestone 4 (internal combustion engine): Midterm Presentation: Optimization and Deployment of Scalable Data Solutions . Note: This milestone serves as the midterm presentation for AC215. The technical components of this milestone focus on the optimization and workflow orchestration aspects of a complex data-driven project. It emphasizes efficiency and scalability, utilizing advanced modeling techniques like distillation, quantization, and compression as well as tools like Vertex AI Pipelines (Kubeflow) for machine learning workflows. The fourth milestone builds on the foundational work of the first three, driving the project towards completion and ensuring readiness for real-world application. This will provide students with exposure to industry-standard best practices and hands-on experience with cutting-edge tools and methodologies. Finally, the milestone serves as an opportunity to practice presenting information to a technical audience in an engaging and concise manner. Key dates: . | Presentation: Tues, Oct 24th or Thurs, Oct 26th (see Ed post for further details) | Slides due : Noon EST - Tues, Oct 24th or Thurs, Oct 26th (see Ed post for further details, Please submit slides via Canvas ) | Repo due date: Friday, Oct 27th 9PM EST | . Template Repository . Submission Instructions: . | Please submit slides via Canvas | . Objectives: . | Distillation/Quantization/Compression: Implement methods for model optimization such as distillation, quantization, and compression. This will enable deployment in resource-constrained environments. | Vertex AI Pipelines (Kubeflow) and Cloud Functions Integration: Utilize Vertex AI Pipelines (Kubeflow) for machine learning workflows and integrate cloud functions to automate and scale various processes within the project, aligning with cloud-native practices. | Presenting a Technical Project: Create a presentation that concisely covers what has been accomplished up to this point, and what the plan is for next steps. Here are some useful questions to think about when creating the presentation: Who is the audience (technical or non-technical), and what information can you expect them to know (and not know) going into the presentation? What’s the story that you are trying to tell? How are you planning to tell that story (e.g. slide structure, visuals, visuals, demo)? What do you want the audience to take away from the presentation? . | . Deliverables: . | Optimized Models: Models that have been distilled, quantized, or compressed, complete with performance benchmarks and analysis. | Vertex AI Pipelines (Kubeflow) & Cloud Functions Implementation: Documentation and code showcasing the successful integration of Vertex AI Pipelines (Kubeflow) for machine learning orchestration and cloud functions for process automation. | Presentation: A 4 minute presentation that includes a brief overview of the project to help orient the audience, a walk-through of the work that has been completed, and a brief outline of next steps. The walk-through does not need to be a live demo (it can be a set of well-made, visually-pleasing slides), but it should make sure to showcase and highlight the various components that have been built. Be prepared for 1 additional minute of questions at the end for a total of 5 minutes on stage. | . ",
     "url": "/milestone4/",
 
     "relUrl": "/milestone4/"
@@ -295,7 +295,7 @@
   },"42": {
     "doc": "Schedule and Calendar",
     "title": "Week 7 - ML Workflow Management",
-    "content": "Oct 17 Kubeflow, cloud functions Lecture 11 Oct 19 Hands on Mega Pipeline App Lecture 12 ",
+    "content": "Oct 17 Cloud functions, Cloud Run, Vertex AI Pipelines Lecture 11 Oct 19 Hands on Mushroom App Pipelines Lecture 12 ",
     "url": "/schedule/#week-7-ml-workflow-management",
 
     "relUrl": "/schedule/#week-7-ml-workflow-management"