When working with data it’s important to understand when it is correct. If there is a time dimension, then it can be difficult to know when variation is normal. Anomaly detection is a useful tool to address these challenges, but a difficult one to do well. In this episode Smit Shah and Sayan Chakraborty share the work they have done on Luminaire to make anomaly detection easier to work with. They explain the complexities inherent to working with time series data, the strategies that they have incorporated into Luminaire, and how they are using it in their data pipelines to identify errors early. If you are working with any kind of time series then it’s worth giving Luminaure a look.
Did you know Data science is a fast-growing career field, with a 650% growth in jobs since 2012 and a median salary of around $125,000? Springboard has identified that data careers are going to shape the future, and has responded to that need by creating the Springboard School of Data, comprehensive, end-to-end data career programs that encompass data science, data analytics, data engineering, and machine learning.
Each Springboard course is 100% online and remote, and each course curriculum is tailored to fit the schedule of working professionals. This means flexible hours and a project-based methodology designed to get real world experience: every Springboard student graduates with a portfolio of projects to showcase their skills to potential employers. Springboard’s unique approach to learning is centered on the very simple idea that mentorship and one-on-one human support is the fastest and most efficient way to learn new skills. That’s why all of Springboard’s data courses are supported by a vast network of industry expert mentors, who are carefully vetted to ensure the right fit for each program. Mentors provide valuable guidance, coaching, and support to help keep Springboard students motivated through weekly, 1:1 video calls for the duration of the program.
Before graduation, Springboard’s career services team supports students in their job search, helping prepare them for interviews and networking, and facilitates their transition in the tech or data industry. Springboard’s tuition-back guarantee allows students to secure the role of their dreams and invest in themselves without risk. Meaning students are not charged if they don’t get a job offer in the field they study. Springboard’s support does not end when students graduate. All Springboard graduates benefit from an extensive support network encompassing career services, 1:1 career coaching, networking tips, resume assistance, interview prep, and salary negotiation.
Since Springboard was founded in 2013, around 94% of eligible graduates secured a job within one year, earning an average salary increase of $26,000. Want to learn more? Springboard is exclusively offering up to 20 scholarships of $500 to listeners of Podcast.__init__. Simply go to pythonpodcast.com/springboard for more information.
Do you want to try out some of the tools and applications that you heard about on Podcast.__init__? Do you have a side project that you want to share with the world? With Linode’s managed Kubernetes platform it’s now even easier to get started with the latest in cloud technologies. With the combined power of the leading container orchestrator and the speed and reliability of Linode’s object storage, node balancers, block storage, and dedicated CPU or GPU instances, you’ve got everything you need to scale up. Go to pythonpodcast.com/linode today and get a $100 credit to launch a new cluster, run a server, upload some data, or… And don’t forget to thank them for being a long time supporter of Podcast.__init__!
- Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
- When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
- Python has become the default language for working with data, whether as a data scientist, data engineer, data analyst, or machine learning engineer. Springboard has launched their School of Data to help you get a career in the field through a comprehensive set of programs that are 100% online and tailored to fit your busy schedule. With a network of expert mentors who are available to coach you during weekly 1:1 video calls, a tuition-back guarantee that means you don’t pay until you get a job, resume preparation, and interview assistance there’s no reason to wait. Springboard is offering up to 20 scholarships of $500 towards the tuition cost, exclusively to listeners of this show. Go to pythonpodcast.com/springboard today to learn more and give your career a boost to the next level.
- Your host as usual is Tobias Macey and today I’m interviewing Smit Shah and Sayan Chakraborty about Luminaire, a machine learning based package for anomaly detection on timeseries data
- How did you get introduced to Python?
- Can you start by describing what Luminaire is and how the project got started?
- Where does the name come from?
- How does Luminaire compare to other frameworks for working with timeseries data such as Prophet?
- What are the main use cases that Luminaire is powering at Zillow?
- What are some of the complexities inherent to anomaly detection that are non-obvious at first glance?
- How are you addressing those challenges in Luminaire?
- Can you describe how Luminaire is implemented?
- How has the design of the project evolved since it was first started?
- What was the motivation for releasing Luminaire as open source?
- For someone who is using Luminaire, what is the process for training and deploying a model with it?
- What are some common ways that it is used within a larger system?
- How do sustained anomalies such as the current pandemic affect the work of identifying other sources of meaningful outliers?
- What are some of the most interesting, innovative, or unexpected ways that you have seen Luminaire being used?
- What are some of the most interesting, unexpected, or challening lessons that you have learned while building and using Luminaire?
- When is Luminaire the wrong choice?
- What do you have planned for the future of the project?
Keep In Touch
- Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you’ve learned something or tried out a project from the show then tell us about it! Email firstname.lastname@example.org) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
- Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
- Anomaly Detection
- Facebook Prophet
- IEEE Big Data Conference
- Unsupervised Learning
- ARIMA (Autoregressive Integrated Moving Average) Model