MLOps

ML and Data Science are about extracting knowledge from a given dataset. It all starts with data, you need lots of data and it has to be meaningful data. You can't expect good results if the data isn't any good.

the technology and tools are interesting, but organizing teams and processes for successful business outcomes is key

Do I have permission to use the data?

@JeffGMKE CrowdChat is an open community platform - you can embed and link the entire event or individual pieces

How does one measure or quantify the business value?

Understanding the business outcome you are hoping to achieve. I've seen all too often organizations try to jump right in without a clear cut goal. Is it improving customer experience? Predicting future outcomes? Improving sales? Marketing? Maybe all of the above?

Starts with a team. Having the right team in place. Providing the necessary toolset with minimum friction. Secure access to data

@fvha Not to put you on the spot but tell everyone how long you've been involved in this space?

In a recent whitepaper from Futurum 5 key considerations are listed with recommendations @jameskobielus, #HPEAI, #MLOps https://www.hpe.com/us/en/resources/services/explo...

@MikeLeone_IT And the more granular the goals the better. You want to be set up for success to show you're making incremental progress with established metrics to measure successful progression.

@stu I was talking about "the data" from the data science perspective. One of the considerations is...do I have permission to use the data?

Then a place to store artifacts and reuse them without having to create word documents for tracking

@CalvinZito My background is in Speech Recognition. Collecting speech data is more than a hobby.

@jameskobwielus HPE has the framework to be able to help cusomters define theiur success https://www.hpe.com/us/en/services/consulting/big-...

At a high level there are 3 key considerations: 1. business problem - what are you looking to improve 2. Technology - data, tools, infrastructure 3. people - do I have the right skills and how to train employees to use AI

@fvha and did that for over 16 years.

@jameskobielusLastly taking models to production in a meaningful way.

@fvha So you were doing #MLOps before anyone put a name on it!

@fvha yes, well before it was called MLops

@fvha ... when you are working on stuff and there is no name yet for it you know you are in early

Data scientists often get frustrated because they have to spend a lot of time getting access to environments & data and have to install tools and libraries for each project from scratch. Self-service flexible environments with secure & governed access to enterprise data is key

@csnow_data_uk True that, I am currently in the phase of getting access to data!

@razatweets you could not be more right about training the employees to use AI, or to help them ask the right questions about the huge data they have been collecting so far!

ACP CreativIT Buffalo Grove, IL

Boston home of the reigning World Championship Patriots and Red Sox and former World Champions Bruins and Celtics

Thanks for joining - reply in a thread or post comments/questions.

Thanks @stu for the great interview