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

About Anaconda

Anaconda Distribution

With over 6 million users, the open source Anaconda Distribution is the fastest and easiest way to do Python and R data science and machine learning on Linux, Windows, and Mac OS X. It’s the industry standard for developing, testing, and training on a single machine.

Anaconda Enterprise 

Anaconda Enterprise is an AI/ML enablement platform that empowers organizations to develop, govern, and automate AI/ML and data science from laptop through training to production. It lets organizations scale from individual data scientists to collaborative teams of thousands, and to go from a single server to thousands of nodes for model training and deployment.

 Highlights

• 2 min 57 sec: What does Anaconda do?
  • Help data scientists be productive & enterprise AI / Data Science
• 3 min 36 sec: How do you interact with Anaconda?
  • About 2.5 million downloads a month of Anaconda Distribution
  • Install binary packages for data science to Python
• 5 min 55 sec: Who are data scientists?
  • Data wrangling and understanding
• 9 min 12 sec: Data Science as a verb
  • Understand how to turn data into actionable insight
• 10 min 47 sec: How learn to use the tools? Community!
  • Community around Anaconda open source to share packages, etc
• 13 min 26 sec: How does Anaconda change as AI/Machine Learning improve?
  • Python is standard language with R close behind for data science
• 14 min 58 sec: Reproducibility in results
  • 16 min 01 sec: Model training issue?
• 17 min 16 sec: Parking lot on Sam Charrington’s AI Bias Podcasts
  • TWiML & AI – https://twimlai.com/
• 17 min 43 sec: Training models for limited sets of data for reliability in Edge
  • Answer by example of Google ImageNet
  • 20 min 14 sec: Optimizations to reduce processing requirements
    • Hey Siri example on how iPhone works
  • 22 min 03 sec: Do models improve over time? Transfer learning
• 22 min 30 sec: Accelerative Learning in AI
  • Fashion example of layering learning
  • 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)
  • Moved on from Java and distributed computing to Kubernetes
  • 29 min 05 sec: Giving up data locality (Hadoop) & specialized hardware?
  • 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?
  • Data scientists interested in data problems not cloud architecture
  • Data science as a Service
  • Kubernetes & Docker installed for you by Anaconda
• 38 min 05 sec: WRAP UP
  • Anaconda Con Videos

Podcast Guest: Mathew Lodge, SVP of Products & Marketing of Anaconda

Mathew has well over 20 years’ diverse experience in cloud computing and product leadership. Prior to joining Anaconda, he served as Chief Operating Officer at Weaveworks, the container and microservices networking and management start-up; and previously as Vice President in VMware’s Cloud Services group. At VMware he was co-founder of what became its vCloud Air IaaS service.

Early in his career, Mathew built compilers and distributed systems for projects like the International Space Station, helped connect six countries to the Internet for the first time, and managed a $630m router product line at Cisco. At start-up CPlane he attempted to do SDN 10 years too early. Prior to VMware, Mathew was Senior Director at Symantec in its $1Bn+ information management group.