oh, m1 canvas

_site/assets/js/search-data.json

@@ -148,7 +148,7 @@
   },"21": {
     "doc": "Milestone 1",
     "title": "Milestone 1",
-    "content": "Project Milestone 1 (the promytheus phase): Project Proposals, Team formation . Key dates: . | project proposals due: Sep 14th | staff feedback: Sep 19th | . Objectives: . For the first milestone, your team will propose a project that aligns with your personal, professional, and academic interests and passions. Allowing you to propose your own projects, will enhance your engagement and lead to better learning outcomes. This approach will also foster your independence, critical thinking skills, and creativity, preparing you for real-world scenarios where you may be required to initiate and lead your own projects. Call on your inner data scientist and take charge of your project experience. Step 1: Create Teams (Groups of 3-5 Students) . Platform for Team Formation: You may use the Ed platform to find your teammates. Alternatively, you may form teams independently. Team Registration: Once you have finalized your team, please enter your team name and the names of team members in this shared spreadsheet. Step 2: Submit Statement of Work (Project Proposal) . Your Statement of Work should act as a blueprint for your project. It doesn’t have to be extensive, but it should be clear and focused. Components of the Statement of Work: . Title and Authors: . | Title: An engaging, relevant, and informative title that captures the essence of your project. | Authors: Names of all team members and their respective email addresses. | . Background and Motivation: . Provide a brief background on the topic you have chosen. Explain why you find it interesting or important, and mention any previous background, research interests, or readings that have influenced your choice. Scope and Objectives: . Clearly outline the problem or question your project aims to solve. Make sure the scope is well-defined so that there is no ambiguity regarding your project’s objectives. Submission Guidelines: . | Length: 1-2 pages . | Format: PDF . | Submit via [TBD] . | . Your draft provides a good outline for what students need to consider regarding the data for their projects. However, it might be beneficial to structure it a bit more for clarity. Here’s a revised version with some suggestions: . Step 3: Discuss Data Sources . Data is the backbone of any data science project and therefore for any MLOps project, making it crucial to identify appropriate datasets for your endeavor. In your Statement of Work, you must address the following aspects regarding data: . Source of Data: . | Identify where the data comes from (e.g., public repository, generated by the team, etc.). | . Description of Dataset: . | Offer a brief overview of what the dataset contains. Is it time-series data, images, textual data, etc.? | . Key Attributes: . | Describe the variables or features that are most relevant to your problem. | . Relevance to the Project: . | Explain how the data is suited to solving the problem or question you’ve posed. Why is this data set useful or relevant? | . Data Quality Concerns: . | If applicable, indicate any potential challenges related to data quality that you foresee (e.g., missing data, inconsistencies, or the need to merge multiple datasets). Mention your preliminary plan to tackle these issues. | . Important Note: . Statements of Work that do not include information on available and relevant data will not be accepted. Step 4: Define Scope and Preliminary Design . The scope of your project is largely up to you and your team. Whether it’s simple or complex, the aim should be to align with the course’s learning objectives. However, for a project to be considered comprehensive, it should ideally include a few of the following minimum components: . Minimum Components for a Good Project: . | Large or Heterogeneous Data: Your project should involve a sizable or diverse dataset that requires careful handling and processing. | Scalability: Consider how your solution will scale for many users, particularly in the application you intend to build. | Complex Models: The project should explore models that are challenging to train, which will showcase your understanding of MLOps challenges. | Computationally Expensive Inference: If your project involves inference models, they should be computationally intensive to align with real-world challenges. | . Problem Statement: . | Clearly outline the problem or question your project addresses. | . Objectives: . | List the primary goals or outcomes, which should align with your problem statement and the minimum components outlined above. | . Learning Emphasis: . | Opt for models and methods that your team understands. The project should reflect your grasp of course concepts. | . Application Mock Design: . | Include a preliminary design or sketch for the application you intend to develop. This could range from simple wireframes to a more detailed, clickable prototype. | . Research and Development: . | Reference papers, blog posts, or other scholarly materials that aid your project and align with your objectives. | . Fun Factor: . | The project should also be a space for you to enjoy both the subject matter and the developmental process. | . Limitations and Risks: . | Discuss any anticipated challenges or limitations, such as data quality issues or technical constraints. | . Milestones: . | List key milestones for both your project and application development. Include tentative deadlines, if possible. | . Important Note: . Statements of Work that do not include both a well-defined scope and a preliminary design for the application will not be accepted. Deliverables: Submit a PDF or word document of your proposal on canvas by the end of the day on Sep 14th. Below are two samples SOW for such apps: . Sample Proposal . ",
+    "content": "Project Milestone 1 (the promytheus phase): Project Proposals, Team formation . Key dates: . | project proposals due: Sep 14th | staff feedback: Sep 19th | . Objectives: . For the first milestone, your team will propose a project that aligns with your personal, professional, and academic interests and passions. Allowing you to propose your own projects, will enhance your engagement and lead to better learning outcomes. This approach will also foster your independence, critical thinking skills, and creativity, preparing you for real-world scenarios where you may be required to initiate and lead your own projects. Call on your inner data scientist and take charge of your project experience. Step 1: Create Teams (Groups of 3-5 Students) . Platform for Team Formation: You may use the Ed platform to find your teammates. Alternatively, you may form teams independently. Team Registration: Once you have finalized your team, please enter your team name and the names of team members in this shared spreadsheet. Step 2: Submit Statement of Work (Project Proposal) . Your Statement of Work should act as a blueprint for your project. It doesn’t have to be extensive, but it should be clear and focused. Components of the Statement of Work: . Title and Authors: . | Title: An engaging, relevant, and informative title that captures the essence of your project. | Authors: Names of all team members and their respective email addresses. | . Background and Motivation: . Provide a brief background on the topic you have chosen. Explain why you find it interesting or important, and mention any previous background, research interests, or readings that have influenced your choice. Scope and Objectives: . Clearly outline the problem or question your project aims to solve. Make sure the scope is well-defined so that there is no ambiguity regarding your project’s objectives. Submission Guidelines: . | Length: 1-2 pages . | Format: PDF . | Submit via Canvas . | . Your draft provides a good outline for what students need to consider regarding the data for their projects. However, it might be beneficial to structure it a bit more for clarity. Here’s a revised version with some suggestions: . Step 3: Discuss Data Sources . Data is the backbone of any data science project and therefore for any MLOps project, making it crucial to identify appropriate datasets for your endeavor. In your Statement of Work, you must address the following aspects regarding data: . Source of Data: . | Identify where the data comes from (e.g., public repository, generated by the team, etc.). | . Description of Dataset: . | Offer a brief overview of what the dataset contains. Is it time-series data, images, textual data, etc.? | . Key Attributes: . | Describe the variables or features that are most relevant to your problem. | . Relevance to the Project: . | Explain how the data is suited to solving the problem or question you’ve posed. Why is this data set useful or relevant? | . Data Quality Concerns: . | If applicable, indicate any potential challenges related to data quality that you foresee (e.g., missing data, inconsistencies, or the need to merge multiple datasets). Mention your preliminary plan to tackle these issues. | . Important Note: . Statements of Work that do not include information on available and relevant data will not be accepted. Step 4: Define Scope and Preliminary Design . The scope of your project is largely up to you and your team. Whether it’s simple or complex, the aim should be to align with the course’s learning objectives. However, for a project to be considered comprehensive, it should ideally include a few of the following minimum components: . Minimum Components for a Good Project: . | Large or Heterogeneous Data: Your project should involve a sizable or diverse dataset that requires careful handling and processing. | Scalability: Consider how your solution will scale for many users, particularly in the application you intend to build. | Complex Models: The project should explore models that are challenging to train, which will showcase your understanding of MLOps challenges. | Computationally Expensive Inference: If your project involves inference models, they should be computationally intensive to align with real-world challenges. | . Problem Statement: . | Clearly outline the problem or question your project addresses. | . Objectives: . | List the primary goals or outcomes, which should align with your problem statement and the minimum components outlined above. | . Learning Emphasis: . | Opt for models and methods that your team understands. The project should reflect your grasp of course concepts. | . Application Mock Design: . | Include a preliminary design or sketch for the application you intend to develop. This could range from simple wireframes to a more detailed, clickable prototype. | . Research and Development: . | Reference papers, blog posts, or other scholarly materials that aid your project and align with your objectives. | . Fun Factor: . | The project should also be a space for you to enjoy both the subject matter and the developmental process. | . Limitations and Risks: . | Discuss any anticipated challenges or limitations, such as data quality issues or technical constraints. | . Milestones: . | List key milestones for both your project and application development. Include tentative deadlines, if possible. | . Important Note: . Statements of Work that do not include both a well-defined scope and a preliminary design for the application will not be accepted. Deliverables: Submit a PDF or word document of your proposal on canvas by the end of the day on Sep 14th. Below are two samples SOW for such apps: . Sample Proposal . ",
     "url": "/milestone1/",
 
     "relUrl": "/milestone1/"
@@ -358,7 +358,7 @@
   },"51": {
     "doc": "Staff / Contact",
     "title": "Office Hours",
-    "content": "TBD . ",
+    "content": "(See Ed) . ",
     "url": "/staff/#office-hours",
 
     "relUrl": "/staff/#office-hours"