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Machine Learning In The Enterprise
2019-02-11 · 19:00
Kevin Dewalt
– founder
@ Prolego
,
Tobias Macey
– host
Summary Machine learning is a class of technologies that promise to revolutionize business. Unfortunately, it can be difficult to identify and execute on ways that it can be used in large companies. Kevin Dewalt founded Prolego to help Fortune 500 companies build, launch, and maintain their first machine learning projects so that they can remain competitive in our landscape of constant change. In this episode he discusses why machine learning projects require a new set of capabilities, how to build a team from internal and external candidates, and how an example project progressed through each phase of maturity. This was a great conversation for anyone who wants to understand the benefits and tradeoffs of machine learning for their own projects and how to put it into practice. Introduction Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Kevin Dewalt about his experiences at Prolego, building machine learning projects for Fortune 500 companies Interview Introduction How did you get involved in the area of data management? For the benefit of software engineers and team leaders who are new to machine learning, can you briefly describe what machine learning is and why is it relevant to them? What is your primary mission at Prolego and how did you identify, execute on, and establish a presence in your particular market? How much of your sales process is spent on educating your clients about what AI or ML are and the benefits that these technologies can provide? What have you found to be the technical skills and capacity necessary for being successful in building and deploying a machine learning project? When engaging with a client, what have you found to be the most common areas of technical capacity or knowledge that are needed? Everyone talks about a talent shortage in machine learning. Can you suggest a recruiting or skills development process for companies which need to build out their data engineering practice? What challenges will teams typically encounter when creating an efficient working relationship between data scientists and data engineers? Can you briefly describe a successful project of developing a first ML model and putting it into production? What is the breakdown of how much time was spent on different activities such as data wrangling, model development, and data engineering pipeline development? When releasing to production, can you share the types of metrics that you track to ensure the health and proper functioning of the models? What does a deployable artifact for a machine learning/deep learning application look like? What basic technology stack is necessary for putting the first ML models into production? How does the build vs. buy debate break down in this space and what products do you typically recommend to your clients? What are the major risks associated with deploying ML models and how can a team mitigate them? Suppose a software engineer wants to break into ML. What data engineering skills would you suggest they learn? How should they position themselves for the right opportunity? Contact Info Email: Kevin Dewalt [email protected] and Russ Rands [email protected] Connect on LinkedIn: Kevin Dewalt and Russ Rands Twitter: @kevindewalt Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Prolego Download our book: Become an AI Company in 90 Days Google Rules Of ML AI Winter Machine Learning Supervised Learning O’Reilly Strata Conference GE Rebranding Commercials Jez Humble: Stop Hiring Devops Experts (And Start Growing Them) SQL ORM Django RoR Tensorflow PyTorch Keras Data Engineering Podcast Episode About Data Teams DevOps For Data Teams – DevOps Days Boston Presentation by Tobias Jupyter Notebook Data Engineering Podcast: Notebooks at Netflix Pandas Podcast Interview Joel Grus JupyterCon Presentation Data Science From Scratch Expensify Airflow James Meickle Interview Git Jenkins Continuous Integration Practical Deep Learning For Coders Course by Jeremy Howard Data Carpentry The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast |
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Putting Airflow Into Production With James Meickle - Episode 43
2018-08-13 · 01:00
James Meickle
– guest
,
Tobias Macey
– host
Summary The theory behind how a tool is supposed to work and the realities of putting it into practice are often at odds with each other. Learning the pitfalls and best practices from someone who has gained that knowledge the hard way can save you from wasted time and frustration. In this episode James Meickle discusses his recent experience building a new installation of Airflow. He points out the strengths, design flaws, and areas of improvement for the framework. He also describes the design patterns and workflows that his team has built to allow them to use Airflow as the basis of their data science platform. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing James Meickle about his experiences building a new Airflow installation Interview Introduction How did you get involved in the area of data management? What was your initial project requirement? What tooling did you consider in addition to Airflow? What aspects of the Airflow platform led you to choose it as your implementation target? Can you describe your current deployment architecture? How many engineers are involved in writing tasks for your Airflow installation? What resources were the most helpful while learning about Airflow design patterns? How have you architected your DAGs for deployment and extensibility? What kinds of tests and automation have you put in place to support the ongoing stability of your deployment? What are some of the dead-ends or other pitfalls that you encountered during the course of this project? What aspects of Airflow have you found to be lacking that you would like to see improved? What did you wish someone had told you before you started work on your Airflow installation? If you were to start over would you make the same choice? If Airflow wasn’t available what would be your second choice? What are your next steps for improvements and fixes? Contact Info @eronarn on Twitter Website eronarn on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Quantopian Harvard Brain Science Initiative DevOps Days Boston Google Maps API Cron ETL (Extract, Transform, Load) Azkaban Luigi AWS Glue Airflow Pachyderm Podcast Interview AirBnB Python YAML Ansible REST (Representational State Transfer) SAML (Security Assertion Markup Language) RBAC (Role-Based Access Control) Maxime Beauchemin Medium Blog Celery Dask Podcast Interview PostgreSQL Podcast Interview Redis Cloudformation Jupyter Notebook Qubole Astronomer Podcast Interview Gunicorn Kubernetes Airflow Improvement Proposals Python Enhancement Proposals (PEP) The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast |
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