think2019

The core platform that enables enterprises to deploy models anywhere is a data-science CI/CD toolchain that can serve to any target device, node, hardware, container, and runtime environment. The "securely" requires tight access and integrity controls throughout.

the best resource for deploying models anywhere, securely is a IT or technology team that is knowledgable, experienced and responsive!

John Thomas, IBM, had a good discussion of operationalized deployment of ML models recently on theCUBE: #MachineLearning use case: augmentation of call center operations

End-to-end security from development, deployment, and updating is important, and not yet at all common!

A built in governance for these models is critical as well.. so you really need data engineers, data scientest, data stewards need to colloborate

Using Watson Machine learning really gives you ability to train. deploy and monitor your models.. This really gives you model portability so you can train and deploy anywhere..

Data Governance Policies + Data Governance Skills + Stated Policies. That covers all people processes and tech aspects.

well said Sarbjeet

@madhu_kochar In my experience, IT and operations teams are very important when you need to confirm that certain governance is in place within an #analytics system or need a new policy to be put in place

this is perhaps the core of organization's journey to AI or even to a successful Data Lake, Data Science

In area of Data Science, typically we include a Data Engineer who work side by side with Data Scientist and are critical to take findings and put into Catalog as well as provide key features needed to modeling phase.

there is an excellent session at THINK, hosted by Jay @jaylimburn - https://myibm.ibm.com/events/think/all-sessions/se...

it’s mainly done at LOB level in most of the companies I have worked with in advisory capacity. Central tools, policies and procedures need to be built for data governance. I believe the WHAT of data cleansing and cataloging must stay with LOB and HOW with IT.

as usual, there are multiple solutions too handle this, but ICP for Data is a platform that includes and enforces these capabilities by default .. Learn more @ this THINK session : https://myibm.ibm.com/events/think/all-sessions/se...

I have yet to see a organization that has this process streamlined. Most established companies have many, many meetings about how data set is going to be used internally and the logistics around it.

This an important requirement in the maturing of AI/advanced analytics. Solutions should support distributed and multi-cloud data, and ideally support orchestration and optimization of moving code to data or vice versa.

You need a combination of a cross frictional team, the right access to data and tools to build your AI foundation.

some organizations refer to this as Data Preparation or Data Curation..

one of the advantages of building cutting edge tech and creating new data products/services is that this is dealt with further down the line

Besides Profiling, cleansing, cataloging, Data classification is another critical attribute. Here is where Ml automation can go a long way. IBM Information Server provides complete solution

Model bias is something we are very focused on, especially from a dev ops perspective. Understanding this is important and critical to your organizations future as you incorporate key decisions using AI. So Trust AI but verify :)

Would agree if you think about IoT use cases with massive volumes of data points continuously produced. Aggregation and storage can happen at the edge. It's not just data warehousing though.

It's not clear to me how you can argue that DWs are a good fit with edge computing. DW is the heart of data governance, which tends to require centralized data storage/control. Please clarify.

E. g. an Edge Computing Device can outsource simple computing tasks to a cloud using a Function-As-A-Service concept. Here, the cloud does not store anything and no backend is set up on it. The cloud only offers computing power for any functions that are transmitted on the fly

Clean data in ---> great ML and AI; not clean data in --> lots of cleanup. Just say no to data pollution!!

Yes .. here is a 3rd party listing of vendors offering cleansing tools : https://www.analyticsindiamag.com/10-best-data-cle...

Yes we in IBM have good solutions around Data Quality, ML and rule enabled. This is very critical part of Tusting your data

Here's a good session at THINK, in case you are interested: https://myibm.ibm.com/events/think/all-sessions/se...