Word Embeddings - Machine Learning Model Architecture
Attention Layer - Machine Learning Model Architecture
Bi -Directional LSTM - Machine Learning Model Architecture
Ticket Summary app
Why spending a lot of time in resolving the tickets??? When Machine Learning is here:) One of the major place where the agents spend a lot of time relies in reading the tickets. What if we use our Machine Learning model to summarise all the text present in the ticket body?? We strongly believe that it would reduce the resolution time of the agents.
What it does
Our machine learning technique would roll out a summary of each conversation in the text(every note or reply) . The text could be from any one - either the agent, or the end-user. No worries- our machine learning algorithm will roll out a summary for every conversation. We would also give out the overall summary of the entire conversation. If the agent likes the answer by our algorithm ,he can use the summary and update the ticket, or he could write out his own summary. Remember humans are intelligent than machines . :)
How I built it
Challenges I ran into
Training the model - Had to use CPU to finish the training, would be better if we had GPU.
Dataset - We didn't have freshdesk data to train the model and give the real time summary.
Accomplishments that I'm proud of
Was able to train a tensor-flow model without the support of GPU I am sure that if we fine tune the model with a better Dataset and with a support of GPU it would create a moment of WOW for everyone. Was able to build something that would add a lot of value to the business and save a lot of time. Cleaning the dataset.
What I learned
How word embeddings works. How Freshdesk marketplace works. Tensorflow serving
What's next for Text Summarization Using Machine Learning - Data Pirates
We are planning to fine tune the model using our freshdesk data which would result in better accuracy and results.