Machine in Production = Data Engineering + ML + Software Engineering // Satish Chandra Gupta // MLOps Coffee Sessions #16

Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠//BioSatish built compilers, profilers, IDEs, and other dev tools for over a decade. At Microsoft Research, he saw his colleagues solving hard program analysis problems using Machine Learning. That is when he got curious and started learning. His approach to ML is influenced by his software engineering background of building things for production.   He has a keen interest in doing ML in production, which is a lot more than training and tuning the models. The first step is to understand the product and business context, then build an efficient pipeline, train models, and finally monitor its efficacy and impact on the business.  He considers ML as another tool in the software engineering toolbox, albeit a very powerful one.  He is a co-founder of Slang Labs, a Voice Assistant as a Service platform for building in-app voice assistants.  // Talk Takeaways ML-driven product features will grow manifold. Organizations take an evolutionary approach to absorb tech innovations. ML will be no exception. How Organizations adopted the cloud can offer useful lessons.ML/DS folks who invest in an understanding business context and tech environment of the org will make a bigger impact.Organizations that invest in data infrastructure will be more successful in extracting value from machine learning.  //Other links you can check Satish onAn Engineer’s Trek into Machine Learning:  https://scgupta.link/ml-intro-for-developersArchitecture for High-Throughput Low-Latency Big Data Pipeline on Cloud:https://scgupta.link/big-data-pipeline-architectureData pipeline article:https://scgupta.link/big-data-pipeline-architecture orhttps://towardsdatascience.com/scalable-efficient-big-data-analytics-machine-learning-pipeline-architecture-on-cloud-4d59efc092b5Tips for software engineers based on my experience of getting into ML:https://scgupta.link/ml-intro-for-developers or https://towardsdatascience.com/software-engineers-trek-into-machine-learning-46b45895d9e0Twitter:https://twitter.com/scguptaPersonal Website:http://scgupta.meCompany Website:https://slanglabs.inVoice Assistants info:https://www.slanglabs.in/voice-assistants----------- Connect With Us ✌️-------------Join our Slack community: ⁠https://go.mlops.community/slack⁠Follow us on Twitter: ⁠@mlopscommunit⁠ySign up for the next meetup: ⁠https://go.mlops.community/register⁠Connect with Demetrios on LinkedIn: ⁠https://www.linkedin.com/in/dpbrinkm/Connect with Satish on LinkedIn:⁠https://www.linkedin.com/in/scguptaTimestamps: 0:00 - Intro to Satish Chandra Gupta 1:05 - Background of Satish on Machine Learning 3:29 - Satish's background on what he's doing now 5:34 - Why were you interested in the challenges of the workload? 9:53 - As you're looking at the data pipeline, do you see much overlap there? 15:38 - Relationships between engineering pipeline characteristics and how they relate to data. 20:24 - Tips for saving when you're building these pipelines. 24:44 - First point of engagement: Collection 31:26 - Possibilities of Data Architecture 38:03 - Why is it beneficial to save money? 44:22 - Learnings of Satish with his current project, Voice Assistant as a service.