Different stages of ML / DL Learning

I want to learn ML -> Take a course 
I know the basics from the course -> Try the code examples 
I tried but I don't know what's next -> Find a use case 
I found a use case -> Collect data 
I collected the data -> Model the ML problem 
I built an ML model -> Create an API to consume it 
I built an API -> Dockerize it 
Is the API scalable? -> Check options such as serverless functions, Endpoint providers like Anyscale / SageMaker Endpoints, GCP, Azure Inferencing 
I deployed the model -> Version your models using MLFlow 
When to update -> Audit / Track data 
What tools to learn -> Align with what your organization uses and cloud vendors

Learn to walk before you try to fly. Everything is incremental learning. Keep going!!!

Keep Exploring!!!

Experience = Domain + Data + Tech + Persistence

I could deeply relate to this post. I believe that having a deep understanding of the data and domain knowledge is essential for producing the best results. I have experienced this firsthand in my work, such as rewriting warranty engines, designing solutions to meet the needs of the business, coming up with approaches for warranty corrections, and supporting on-the-fly changes for different markets.

The relevance and completeness of the solution is the result of production support, familiarity with the tables and columns, and an awareness of any data issues.

In order to achieve the best results, it is important to have a comprehensive understanding of the data and domain knowledge. This requires consistency and perseverance, and it is only through this that the big picture can be seen.

Having a thorough knowledge of the data and domain can be difficult to acquire, but the rewards are worth it. Not only will it help to produce better results, but it will also give you a better understanding of the project as a whole. It is also beneficial to have a good understanding of the different markets and the changes that may be required for them.

Having a deep understanding of the data and domain knowledge is essential for producing the best results. It requires consistency and perseverance, and it is only through this that the big picture can be seen. It is important to have a comprehensive understanding of the data and the domain knowledge, as this will help to produce better results and give you a better understanding of the project as a whole. Additionally, it is beneficial to have a good understanding of the different markets and the changes that may be required for them. With the right knowledge and understanding, you will be able to produce the best results for any project.

Learning's as part of current roles

• Discuss with both between business and technical audiences.
• “Hands-on” and “agile” to fix where it breaks
• Technically hand-holding data scientists, engineers, and architects depending on solution needs. Drive back to first principles
• Collaborating with colleagues from different cultures and time-zones across the globe

Keep Exploring!!!

Good Read - A Roadmap To Supply Chain Reinvention

• Customers repurchase as customers who were satisfied
• Engaging in circular economy achieving sustainability outcomes
• Real-time decision execution

From - A Roadmap To Supply Chain Reinvention

Supply Chain Insights

• Centralized planning - people, process, system
• Strike end to end flow of procurement, distribution, logistics
• Integrated supply chain
• Mindset, Skillset, Toolsset
• Global Supplier Ecosystem
• Provide end-to-end optimization

Keep Reading!!!

My Perspective of Data Science

Data Analysis

• Find #Insights, Turn it into Why-questions
• Seek Surprises sudden #peaks, #lows, Harness it into How-Questions
• Plot data in different dimensions #Month / #Year / #Sales / #Products / #Divisions, Find #Insights in every #perspective

Learn the story behind the numbers!!!

Mindset and Skill
#Skill is not just how fast you solve a problem but how many different #perspectives/ #techniques / #approach you can find to solve the problem

Domain Knowledge
If you don't understand your #domain, you won't understand your #data, you will miss the #insights and your model will not be built based on the domain needs. It is easy to do .fit, .predict but its harder to find the hidden feature variables before building the model. Understand your #data before building your ML use case. #perspective #machinelearning #datascience

General Guideline - How I evaluate data science candidates?

• Different business problems solved and their ML lessons learned, Deep Dive on Implementation, Algo used, Features Evaluated
• Data pipeline set up challenges faced to deploy in production
• How do you keep track of new papers / evaluating and learning different frameworks
• How much do you code on a daily basis for work / personal learning
• Ability to bring different perspective/techniques solving problems
• How Algos works, Learning's with Overfitting, Underfitting, variable selection

#DataScience Solutioning #Lessons Learnt

Step -1 - Understand / Solving a problem from business #perspective is first move

Step -2 - Scaling the solution is next milestone

Step -3 - Hosting it / Porting it is the last milestone

All the steps are important but priority relies on completion of each task in sequence so that we do not go back to fix them again. Hire talent accordingly #machinelearning #vision #perspectives

Analyze -> Solution -> Scale -> Deploy (Talent pool varies accordingly, Get the mix to excel)

The field is evolving on a daily basis. We need passionate, curious learners with an experimentation mindset!!!

Document can be downloaded from link

Happy Learning!!!