Global AI and Data Science

Blog Editorial Guidance:  

• Focus on Enterprise problems and how we solve them.
• Don't pitch products! Share your knowledge.
• You don't have to write about the hottest trending topics/frameworks. We don't want our community to be an inferior Kaggle site.
• Write about topics you love to read about as a developer/data scientist.

Developer Topics 

How Do You Build Applications for Data & AI? 

• Start with the problem you're trying to solve
• Discuss why you built what you did for data scientists
• How did you integrate an Open Source toolkit with your application?

Development Practices

• API Management
• Dev Ops
• Continuous Delivery 
• Continuous Integration
• Governance
• Software Development

The Day in the Life of an Enterprise Developer:

• What problems/bugs are you experiencing?
• How did you code it?
• How does it fit in your environment?
• How did a customer use it?

Data Scientist Topics

• Natural Language Processing
• Computer Vision
• Speech-to-Text
• Deep Learning
• ML Ops
• Auto ML
• Model Selection/ Hyperparameter Tuning
• Data Visualization
• AI Trust (Bias, Explainablity, Adversarial Robustness)
• IBM OpenSource Projects (i.e. AIX360, Kubeflow, MAX, Spark)

How To Write A Technical Blog (Write Using the Steps Below):

Title & Tagging

• The title is one of the most critical components to getting someone to read your blog. Make the reader want to take action or click. Here are some titles of top blogs:
  • 4 Reasons Why Your Machine Learning Code is Probably Bad
  • Asking Great Questions as a Data Scientist
  • How to do Everything in Computer Vision
  • Simple Yet Practical Data Cleaning Codes
• Make sure to tag your blog so people can find it! 

Why Is This Topic Important? (No Product Pitching)

• Avoid pitching products or your business unit (You can promote it at the bottom of the blog)
• What use case can this be applied to?
• How will it help a data scientists do their job better?
• How is this topic important to the field of data science?

Define The Fundamentals of the Concept

• Example: What is Principle Components Analysis? What is Dimensionality Reduction?

How Do You Code It? Remember Customer Privacy

• Include Screenshots/Snippets of Code
• Cut customer data/column from snippets
• Provide the link to the code on Github

What Are the Pluses & Minuses of This Approach?

• Provide reasons why this approach works and when it may not
• Provide your stance on why this approach is better

Provide a Brief Conclusion & Include References

• Write a brief 1 paragraph conclusion/summary

List Upcoming Events & Where To Learn More

• Register for the IBM DS Community https://www.ibm.com/community/datascience/ 
• Get started with Watson Studio: LINK 
• Links to future Events or Meetups https://community.ibm.com/community/user/datascience/events/upcomingevents 

Run your post through Hemingway Editor - It will provide checks on sentence complexity, grammar, etc. http://www.hemingwayapp.com/ 

• Having others proof your article is required in parallel to make sure that anyone is able to understand your content. Make sure you have 2+ persona's proof it (technical and non-technical)

• Make sure your blog post is at a 9-10th grade reading level or less. The easier it is to read, the more views it will get. 

• Not everyone is an IBMer, please dont assume people know acronyms or product specific terminology. 

• Hemingway, will tell you when to remove adverbs, choose simpler phrases, remove passive voice etc.

Include Your Bio

• Decide whether to write your bio in first or third person
  • List your current position
  • Identify your company or employer.
  • Identify your professional and personal philosophy. 
  • Consider whether to include personal details.