Data Transformers Podcast
Data Transformers Podcast
Monetizing Machine Learning - Vin Vashishta

Episode Title: Monetizing Machine Learning  – Vin Vashishta

Guest Title: Chief Data Scientist, Data by V Squared | Editor,  The ML Rebellion, LinkedIn Top Voice 2019

Episode Summary:

Vin Vashishta is passionate about many things but most important things are: (1) Exhorting and consulting with leaders of the organizations to use Machine Learning strategically to monetize (2) Influencing the broader community using social media to make them more data science aware in many aspects (3) Delivering decision support products/services to accelerate the ML adoption.

Topics discussed in this episode:

Vin Vashishta’s 3 ring circus (01:38): Vin’s focus is 3 fold (1) Strategic discussions with senior execs about monetizing Machine Learning for their own business (2) Engaging the community/social media about Machine Learning (3) Implementing decision support systems based on machine learning.

Assessing organizations’ monetization capability (04:55): An organization has to go through the assessment of whether they are a machine learning organization first or are they going to use ML to monetize their existing products/services. An example is Netflix where Netflix uses ML (ex: recommendation engine) to drive engagement . 

Predictions based on the amount of data (09:50): There are 3 factors to ML. (1) Models and their effectiveness (2) Data and its context (3) Effectiveness of models against the datasets. If there is insufficient data or data of lower quality, models should be able to flag that out. Taking Covid as an example, in many cases, the models’ performance against pre-covid data may be still Ok within some context. But when the context changes, good models should be able to flag and say they can’t make predictions.

Using ML models for hiring practices (15:17): Large companies like Facebook, Amazon, Google, IBM want to diversity in their workforce and are figuring out if they can use ML along the way. Unfortunately, these companies may have to undo years of infrastructure laid out first. They may initially say the candidate pool is not big enough. But that’s because they are looking at the problem incorrectly.

Checking all boxes to get hired (23:58): There is this feeling from the candidate pool that they have to check all boxes to get hired. This is definitely a work in progress and the issue can’t be ignored.

Resources mentioned in this episode:

The ML Rebellion blog:

Data By V-Squared: 

Call to action:

Connect with Vin Vashishta: 

Follow Data Transformers on Twitter: @DataTransforme2 

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