Our guest this week is Ines Montani, co-founder and CEO of Explosion, a company based out of Berlin that produce tools that you probably know and love like Spacy, a Python Natural Language Processing library and Prodigy, a data annotation tool.

I've always found Ines to be personally inspiring in the work that she and her team produce as well as how they present themselves to the world, so it was a real pleasure to get to dive into the weeds as to exactly how that happens. We also discuss how NLP works in production, what reproducibility means for ML projects and much more.

Special Guest: Ines Montani.

Links:

• ines.io
• Explosion · Makers of spaCy, Prodigy, and other AI and NLP developer tools
• Software · Explosion
• spaCy · Industrial-strength Natural Language Processing in Python
• explosion/spaCy: 💫 Industrial-strength Natural Language Processing (NLP) in Python
• Prodigy · An annotation tool for AI, Machine Learning & NLP
• Live Demo · Prodigy · An annotation tool for AI, Machine Learning & NLP
• Thinc · A refreshing functional take on deep learning
• explosion/thinc: 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
• ines/spacy-course: 👩‍🏫 Advanced NLP with spaCy: A free online course
• "Let Them Write Code" - Keynote - Ines Montani - YouTube

This week, we spoke with Danny Leybzon, currently working with WhyLabs to help data scientists monitor their models in production and prevent model performance from degrading. He previously worked as a kind of roving data scientist and engineer, helping companies put their models into production.

As such, we had a really interesting discussion of some of the ways that tooling and the general context for data science sometimes lets practitioners down,

Special Guest: Danny Leybzon.

Links:

• Danny D. Leybzon
• whylogs · PyPI
• Data and AI Observability Platform - enabling MLOps | WhyLabs
• SLCPython December 2020: Monitoring Machine Learning with Danny Leybzon - YouTube
• Monitoring ML Models - YouTube
• Monitoring ML Models in Production - YouTube
• Machine Learning Models in Production - YouTube
• Danny on LinkedIn
• Women's Clothes | Men's Clothes | Kid's Clothing Boxes | Stitch Fix
• zenml-io/zenml: ZenML 🙏: MLOps framework to create reproducible ML pipelines for production machine learning.
• Terraform by HashiCorp
• Zillow — A Cautionary Tale of Machine Learning - causaLens
• Cloud Monitoring as a Service | Datadog
• Prometheus - Monitoring system & time series database

Noah Gift is the founder of Pragmatic A.I. Labs and author of 'Practical MLOps'. We discuss the role of MLOps in an organisation, some deployment war stories from his career as well as what he considers to be 'best practices' in production machine learning.

Read the summary blogpost on the ZenML blog.

Special Guest: Noah Gift.

Links:

• Noah Gift
• Pragmatic AI Labs | Pragmatic AI Labs and Solutions
• Practical MLOps [Book]
• Pragmatic AI Labs - YouTube
• noahgift (Noah Gift)
• The Black Swan: Second Edition: The Impact of the Highly Improbable: With a new section: "On Robustness and Fragility" (Incerto): Taleb, Nassim Nicholas: 8601404990557: Amazon.com: Books
• Why the iBuying algorithms failed Zillow, and what it says about the business world’s love affair with AI - GeekWire
• Jenkins
• Amazon.com: Leonardo da Vinci eBook : Isaacson, Walter: Kindle Store
• Helicopter by Leonardo da Vinci
• Amazon SageMaker – Machine Learning – Amazon Web Services
• Beware the data science pin factory: The power of the full-stack data science generalist and the perils of division of labor through function | Stitch Fix Technology – Multithreaded
• Amazon Mechanical Turk
• DevOps - Wikipedia
• What is AutoML? | IBM
• Kubernetes
• Open Source MLOps Orchestration | MLRun
• Amazon EFS
• Amazon FSx | Feature-Rich & Highly-Performant File Systems | Amazon Web Services
• Cloud Computing Services | Microsoft Azure
• Azure ML Studio
• Serverless Computing - AWS Lambda - Amazon Web Services
• Google Cloud Platform - Getting Started

Adam and Hamza introduce themselves for the first episode of Pipeline Conversations. They discuss the world of MLOps, where ZenML sits within this space, and why it's such a complicated problem to solve.