This is in reference to the HydPy program that is scheduled to be held at
Epam India for Feb 22. I'm writing on behalf of my organization Verizon.
We are interested to explore speaking opportunities at HydPy. Please do let
me know what would be a good time to have a quick call regarding this.
We hope you are doing well and are healthy in the midst of everything
that's happening around us. We got a really awesome mini-conf planned with
the help of our friends from PyDelhi this Sunday (4:00 pm - 8:30 pm IST)
and we would be absolutely thrilled to see you there.
- The schedule has been updated, you can RSVP @HydPy Meetup 
- Mark your calendar with Google Calendar invite 
- Share + Retweet the meetup to your colleagues and friends 
We got quizzes, we got prizes, we got workshops, and we got a bunch of
talks to help you get started on Azure and celebrate this Azure PyDay.
Hoping to catch up with you all there.
Sanchit (On behalf of HydPy)
 - https://www.meetup.com/HydPyGroup/events/271125338/
 - https://bit.ly/pydelhicalendarinvite
 - https://twitter.com/hydPython/status/1276405366369157120?s=20
This is a follow-up to my discussion with Kalyan Prasad regarding
potential IBM speaker involvement at one of your upcoming (virtual) events.
The IBM Data Science Community
<https://community.ibm.com/community/user/datascience/> is a digital venue
for more than 11,000 data scientists, AI developers, machine learning
engineers and like-minded technical practitioners to learn, share, and
engage. We support leading non-IBM meetups in key tech hubs around the
world that have a strong focus on data science and AI and support them
through speakers, sponsorships and (occasionally) venue space.
Our team currently has four topics on offer (please see abstracts below):
- AI Fairness
- AI Explainability
- ML Ops
Other topics include:
- Geospatial Libs
- Privacy-Preserving Machine Learning
- Customizing JupyterLab Using Extensions
- AI Pipelines Powered by Jupyter Notebooks
Talks are usually 30-45 minutes but can be customized to fit lightning talk
slots or include more hands-on workshop type elements.
Hope you find some of these interesting. Let me know if you have any
*Removing Unfair Bias in Machine Learning*
"Extensive evidence has shown that AI can embed human and societal bias and
deploy them at scale. And many algorithms are now being reexamined due to
illegal bias. So how do you remove bias & discrimination in the machine
learning pipeline? In this webinar you’ll learn the debiasing techniques
that can be implemented by using the open source toolkit AI Fairness 360.
AI Fairness 360 (AIF360) is an extensible, open source toolkit for
measuring, understanding, and removing AI bias. AIF360 is the first
solution that brings together the most widely used bias metrics, bias
mitigation algorithms, and metric explainers from the top AI fairness
researchers across industry & academia.
In this webinar you’ll learn:
- How to measure bias in your data sets & models
- How to apply the fairness algorithms to reduce bias
- How to apply a practical use case of bias measurement & mitigation in
a data-driven medical care management scenario"
*Explainable Workflows using Python*
This talk approaches the typical data science workflow with a focus on
explainability. Simply put, it focuses on skills and tactics used to help
data scientists articulate their findings to end-users, stake-holders, and
other data scientists. From data ingestion, cleaning and feature selection,
and ultimately model selection, explainability can be incorporated into a
data scientists workflow. Using a combination of semi-automated and open
source software, this talk walks you through an explainable workflow.
*AutoML, a Review*
AutoML is a term that appears increasingly in tech industry articles and
vendor product claims, and is also a hot topic within AI research in
academia. Consider how nearly all of the public cloud vendors promote some
form of AutoML service. The tech “unicorns” are developing AutoML services
for their data platforms, many of which have been made open source. A
flurry of smaller tech startups promise to “democratize” ML and relieve
AI-related hiring pains for enterprise customers. Given all the buzz, what
does “AutoML” mean? This meetup is a review of the current space and what
we have to expect of AutoML.
*ML Ops, a Meetup*
Consider how the software development life cycle (SLDC) is well-defined at
this point: planning, creating, testing, deploying, maintaining – or some
variant, depending on your software methodology. The gist remains
consistent. Computer software runs “logic” in hardware, the test suites are
repeatable, it’s a relatively deterministic process. Consider how, with
machine learning, we’re working with probabilistic systems instead. Instead
of writing code as instructions, we’re guiding these systems to learn from
data. IBM has a mission to help bring machine learning capabilities to all,
so we can all participate in the AI economy responsibly. Consequently,
there are many different participants and stakeholders in this emerging
field of ML Ops. This meetup will review the current state of what it takes
to build successful pipelines in this probabilistic setting so that we can
build a shared understanding of why we treat our models as living products
and ask the right questions: Are they healthy? Are the representative? Are
E' fondamentale sensibilizzare l'opinione pubblica sull'opportunità della registrazione di un marchio. Infatti, in questa maniera, si valorizza il patrimonio immateriale nazionale, a difesa del Made in Italy, quale forma efficace di contrasto alla contraffazione e al plagio in tutti gli ambiti merceologici.