AI Strategy and Architecture Don't Guarantee Successful Model

Hey You!

Today I am sharing a story about how we used a language model for a client 5 years ago and how we approach the problem.

AI Leadership👑

How We Used Language Models 5 years ago

In 2019, a client wanted GPT3-like results when it wasn’t released. (metaphorically saying)

They wanted to build a question-answering system, which should answer complex questions with reasoning.

They approached many teams but lacked: 

→ solid AI Strategy

→ high-quality dataset

→ budget to run Transformers

They reached out to us after learning about how we worked on different AI problems with other clients, we delivered:

→ an AI strategy for them

→ an Implementation plan with a timeline

→ an architecture of how the AI system will work

The architecture was quite promising.

We used BERT and DistilBERT

1. The model was giving good results on simple questions.

2. However, poor results in complex questions and reasoning.

3. Bidirectional models make predictions that consider both left and right context.

4. Hugging Face introduced DistilBERT, which was a smaller and faster alternative to BERT

Hard Truth:

Just because you created a decent AI strategy, spent countless hours on development, and have a solid architecture, doesn’t mean the AI model owes you accuracy.

The prime reason among N number of reasons an AI model fails is data quality.

We make sure

→ we focus on relevant information and avoid AI hype

→ workflow of the data team should be at ease for everyone

→ the data quality problem is solved right from the beginning

→ design data pipeline and AI models by keeping scalability in mind

To increase the ROI, we are raising the bar

→ Actionable AI Strategy sessions

→ making the process simple and fast with 1:1 AI Solutions Brainstorm

If you are building AI solutions, DM me “BUILD AI” and I will share a base AI Strategy with you.

PS: There are only 2 Build AI spots, brainstorm your idea https://topmate.io/himanshuramchandani/447594

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