Open Data Science Conference West 2020

From Build To Monitor

Transforming the ML Model Journey

Oct 27 – 30, 2020


Do you have consistent methods to build ML code for production models? Are you following a standard process in deploying them in production? Do you know if your production models have drifted?

If the answer to any of these questions is no, you need to rethink your MLOps strategy.

The machine learning lifecycle has many moving parts. The good news is, most of these parts can be automated and standardized in the form of an end-to-end platform to build, deploy, and monitor ML pipelines. At Tredence, we have solved this problem for our client partners with our years of expertise in handling & scaling ML practice across industries, and in-house accelerators to help realize the goal faster.

Join us at our booth at the ODSC conference
and learn more about our capabilities.

A glimpse of our capabilities

Building & Deployment

Our industry-leading accelerators ML Easel & ML Works, are a collection of solutions developed to enable faster development of ML models. It provides the required boost to scale the ML practice for a business

Monitoring & Resolution

ML Works monitors your ML pipelines for you with custom alerts for drift, anomaly, or bias in the model. It packs the ability to detect failures in advance, thus saving millions for the business

Explainable AI

The Explainability library of MLWorks enables the user to understand the output of a model on the most granular level possible. ML models no longer need to be a black box


Use the power of the cloud to scale! All our solutions integrate seamlessly with the leading cloud platforms, enabling them to handle 100s of 1000s of models at a time

All the solutions are model agnostic and work with everything from simple regression models to time series models to NLP classification models.

session details

Automated Model
Management with ML Works

Pavan Nanjundaiah

October 29, 2020
10:30 AM PDT

ML Easel – Tredence’s Data Science and ML Engineering Workbench

Changa Reddy B

October 29, 2020
12:30 PM PDT

Predicting Model Failures
in Production

Aravind Chandramouli

October 30, 2020
10:30 AM PDT


for Attendees

Insight on how to streamline ML operations

Insight on how models degrade and how to mitigate it

A chance to see things in action with our product demo

About The Speakers

Changa Reddy

Principal, Data Engineering at Tredence Inc.

Changa leads the Data Engineering practice at Tredence. He has 20 years of Consulting experience implementing data and analytics solutions for cross-sector clients with a focus on reducing costs, improving operational efficiencies, and adoption across different business functions. He has seen the transition from database solutions, packaged analytics to self-service advanced analytics over the last two decades.

Pavan Nanjundaiah

Head of Engineering at Tredence Inc.

Pavan heads the Tredence Engineering team. He has 17 years of rich experience in software engineering and consulting. He leads a team that specializes in building analytical products for Enterprise customers.

Aravind Chandramouli

Head of Data Science at Tredence Inc.

Aravind leads the Data Science practice at Tredence. He has a Ph.D. in Computer Science from the University of Kansas with a focus on Information Retrieval and Machine Learning. His team works on the R&D of algorithms for new Data Science solutions and has fueled the core of new solutions built to help clients automate their practices around common pain points.

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