Bridging The Gap Between Machine Learning And Operations At Iguazio

Summary The process of building and deploying machine learning projects requires a staggering number of systems and stakeholders to work in concert. In this episode Yaron Haviv, co-founder of Iguazio, discusses the complexities inherent to the process, as well as how he has worked to democratize the technologies necessary to make machine learning operations maintainable. Announcements 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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today 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! Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask. RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. Your host is Tobias Macey and today I’m interviewing Yaron Haviv about Iguazio, a platform for end to end automation of machine learning applications using MLOps principles. Interview Introduction How did you get involved in the area of data science & analytics? Can you start by giving an overview of what Iguazio is and the story of how it got started? How would you characterize your target or typical customer? What are the biggest challenges that you see around building production grade workflows for machine learning? How does Iguazio help to address those complexities? For customers who have already invested in the technical and organizational capacity for data science and data engineering, how does Iguazio integrate with their environments? What are the responsibilities of a data engineer throughout the different stages of the lifecycle for a machine learning application? Can you describe how the Iguazio platform is architected? How has the design of the platform evolved since you first began working on it? How have the industry best practices around bringing machine learning to production changed? How do you approach testing/validation of machine learning applications and releasing them to production environments? (e.g. CI/CD) Once a model is in production, what are the types and sources of information that you collect to monitor their performance? What are the factors that contribute to model drift? What are the remaining gaps in the tooling or processes available for managing the lifecycle of machine learning projects? What are the most interesting, innovative, or unexpected ways that you have seen the Iguazio platform used? What are the most interesting, unexpected, or challenging lessons that you have learned while building and scaling the Iguazio platform and business? When is Iguazio the wrong choice? What do you have planned for the future of the platform? Contact Info LinkedIn @yaronhaviv on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Iguazio MLOps Oracle Exadata SAP HANA Mellanox NVIDIA Multi-Model Database Nuclio MLRun Jupyter Notebook Pandas Scala Feature Imputing Feature Store Parquet Spark Apache Flink Podcast Episode Apache Beam NLP (Natural Language Processing) Deep Learning BERT Airflow Podcast.__init__ Episode Dagster Data Engineering Podcast Episode Podcast.__init__ Episode Kubeflow Argo AWS Step Functions Presto/Trino Podcast Episode Dask Podcast Episode Hadoop Sagemaker Tecton Podcast Episode Seldon DataRobot RapidMiner H2O.ai Grafana Storey The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast