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@heatherklus @jyotsna_sai Use machine learning to improve customer experience at @TMobile. Need to prepare customer care agent before the first response from customer. What impacts the customer, show status so agent can be more effective and helpful. #cascadiarconfPermalink On twitter.com
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@heatherklus @jyotsna_sai @TMobile Expert assistant: get conversation topic, aggregate customer data, pull up relevant documentation as fast as possible. This runs is real time. Wow. #cascadiarconfPermalink On twitter.com
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@heatherklus @jyotsna_sai @TMobile Used by 600 t-mobile messaging agents, 5 million hits/a week. Help care representative to help solve problems faster. #cascadiarconfOn twitter.com
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@heatherklus @jyotsna_sai @TMobile Started out small. Data engineer/Data science language issues. How can you speak better to engineers as a data scientist? #cascadiarconfPermalink On twitter.com
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@heatherklus @jyotsna_sai @TMobile What are the words can you use across data sci/engineer teams? Talk about your feelings, and understand value conflicts. #cascadiarconfPermalink On twitter.com
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@heatherklus @jyotsna_sai @TMobile Values are different btw data engineers/scientists. Engineers value performance/scalability, Scientists value accurate insights. #cascadiarconfPermalink On twitter.com
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@heatherklus @jyotsna_sai @TMobile Try sharing incremental results, rather than hoarding results until the end. Definition of done - what do you need to consider a task done? Can help reduce ambiguity and friction. #cascadiarconfPermalink On twitter.com
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@heatherklus @jyotsna_sai @TMobile Share initial working version of model, so engineers can work on getting it into production. #cascadiarconfPermalink On twitter.com
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@heatherklus @jyotsna_sai @TMobile How do we measure success? Varies between scientists/engineers. Scientists: understand patterns in data, Engineers: value code working. What are dependencies of model? #cascadiarconfPermalink On twitter.com
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@heatherklus @jyotsna_sai @TMobile Understand each other's needs. Not just results, but also the inputs and requirements for a model. #cascadiarconfPermalink On twitter.com
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Documentation that is useful for both scientists/engineers is important. What it does, rather than talking just about process. Ask what the other team is looking for #cascadiarconfPermalink On twitter.com
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Shortcuts: convince one engineer that your idea is important, they can convince other engineers your idea is great. #cascadiarconfPermalink On twitter.com
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Establish communication rituals, such as disciplines. Groups meet every week - product, engineering, data science, architecture. Knowledge transfer sessions to share knowledge and work. #cascadiarconfPermalink On twitter.com
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Informal sessions to talk are important. Collaborative environment and co-location of all groups important. Talk to each other! Treat each other with respect. Data scientists/engineers are each other's customers. #cascadiarconfPermalink On twitter.com
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In conclusion:Permalink On twitter.com
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Talk here: Http://bit.ly/r-cascadia-tmo #cascadiaRconfPermalink On twitter.com
