Joining us this week is Mathew Lodge, SVP of Products & Marketing of Anaconda.

Highlights

• 2 min 57 sec: What does Anaconda do?

o Help data scientists be productive & enterprise AI / Data Science

• 3 min 36 sec: How do you interact with Anaconda?

o About 2.5 million downloads a month of Anaconda Distribution

o Install binary packages for data science to Python

• 5 min 55 sec: Who are data scientists?

o Data wrangling and understanding

• 9 min 12 sec: Data Science as a verb

o Understand how to turn data into actionable insight

• 10 min 47 sec: How learn to use the tools? Community!

o Community around Anaconda open source to share packages, etc

• 13 min 26 sec: How does Anaconda change as AI/Machine Learning improve?

o Python is standard language with R close behind for data science

• 14 min 58 sec: Reproducibility in results

o 16 min 01 sec: Model training issue?

• 17 min 16 sec: Parking lot on Sam Charrington’s AI Bias Podcasts

o TWiML & AI - https://twimlai.com/

• 17 min 43 sec: Training models for limited sets of data for reliability in Edge

o Answer by example of Google ImageNet

o 20 min 14 sec: Optimizations to reduce processing requirements

 Hey Siri example on how iPhone works

o 22 min 03 sec: Do models improve over time? Transfer learning

• 22 min 30 sec: Accelerative Learning in AI

o Fashion example of layering learning

o Issues around lack of data for training

• 26 min 01 sec: Portability of models via Anaconda

• 26 min 48 sec: Cloud Native Model of AI (no longer 2004)

o Moved on from Java and distributed computing to Kubernetes

o 29 min 05 sec: Giving up data locality (Hadoop) & specialized hardware?

o 32 min 42 sec: Cloud model gives private and public options

• 34 min 23 sec: How Anaconda play into the Cloud Native data science model?

o Data scientists interested in data problems not cloud architecture

o Data science as a Service

o Kubernetes & Docker installed for you by Anaconda

• 38 min 05 sec: WRAP UP

o Anaconda Con Videos