Last time I am going to ask for your honest opinion on how we are doing in the community. Please take a moment and fill out this feedback form. Next week I will announce the winners of the 108$ worth of gift cards for those who filled out the form.
In other news, congrats to everyone that won free TWIMLcon tickets! The event is kicking off this week and I will be leading a debate next Friday at 9am PST. Got any good
topics we should debate about? Right now I was thinking:
"A data scientist should learn K8S" and "one tool to rule them all or best in class solutions".
Past Meetup
Productize It!
Constant Learning
Adria was a great spot talking to us last week about his motivations behind why he created the educational resource Productize ML. The course is organized in four lectures that cover the full life-cycle of an AI & ML product from the research and formulation of the business idea, how initial and user data is collected, curated, and standardized, so it can be fed back to the system, what the tools that are
available to train and evaluate your ML models are, followed by best practices when deploying and monitoring your model.
Favorites One of the main pieces that I enjoy about the way Adria and the team set up this resource is that they make sure to break down the way that each stakeholder looks at ML. This gives a more holistic view of everyone involved and also what their needs are.
I also was really intrigued by where Adria wants to go next with this project and how he sees the space evolving over the next half year.
Links Check out the full conversation on youtube or podcast land. Also as extra credit, if you use or gain any value from the course let us know so we can hear what your story is!
After a holiday break, we are back with another "When Machine Learning meets privacy" episode. This is our 8th episode around data privacy and we hope you all have been enjoying the conversations Fabianna has with these
phenomenal guests. Only 1 more episode til the season finishes and we would love to hear all your feedback!
For this episode, we are coming at you with Federated Learning, a new development paradigm for Data Scientists and Machine Learning Engineers!
You probably have already seen some form of Federated Learning in action when using your smartphone to text someone, so how does it relate to privacy?
Ramen Dutta, a member of our community and founder of TensoAI, is here to introduce us to the concepts of Federated Learning, what he is doing in the #agtech industry and the challenges behind Federated Learning adoption.
Vishnu, David, and I recorded an epic panel around the hottest topic of the times; feature stores!
Tho Who We had an all-star cast composed of data engineers, data scientists, ML engineers, and project managers all chiming in on what and how they feel about this whole feature store paradigm and why it's worth it to have a deeper look.
The Why The whole reason for this conversation came from a conversation Mattias and I were having around the idea of if he and his company even needed a feature store. At what point does the architecture that you have in place need to evolve, and what are the pros and cons of that evolution.
The Where We will let you all decide if we had too many people on the call or you like the panel discussion format! Let us know if we should do more like this! Check out the video here and podcast here
It's not every day that we get to peek inside the inner working of a giant's ML system. It is just as uncommon that we get to interview someone on their birthday! In tomorrow's meetup, we can check both of those boxes talking with Manoj Agarwal, Software Architect on the Einstein Platform team at Salesforce.
What is this all about anyway? The conversation will center around Serving ML Models at a High Scale with Low Latency. Just to give you some context Salesforce Einstein was released back in 2016, integrated with all the major Salesforce clouds. Fast forward to today and Einstein is delivering 80+ billion predictions across Sales, Service, Marketing & Commerce Clouds per day. Well, that escalated quickly.
Oh yah and .... Bring some cake to the meetup and your best singing voice to wish him a happy birthday!
+ Some of you have been asking me how to get the meetup added to your calendar. If you use google calendar you can add it here and outlook here. Let me know if you have any problems.
+ See you at 5pm GMT / 9am PST tomorrow Wedensday by clicking the link below.
As AI adoption accelerates, Data Science teams and business
leaders are confronted with questions regarding the broader implications of AI systems: Are these systems trustworthy, transparent, and responsible? Are outcomes reliable over time? Is there bias built into models? Are models standing up to regulatory and compliance requirements?
At Fiddler, we did a research study and asked industry leaders and experts to uncover how to effectively implement AI in organizations and promote the responsible use of AI. Here are some of the key findings:
Explainability and Monitoring is considered mission-critical to success
Investment in Explainability and Monitoring solutions are on the horizon
Regulations have an impact on organizational decisions, but many companies don’t have dedicated systems yet
Download the “State of AI Explainability and Monitoring: Market Survey 2020” to read more.