In this session, Yaron Haviv, Iguazio's Co-Founder and CTO, discussed how to enable continuous delivery of machine learning to production using Git-based ML pipelines (Github Actions) with hosted training and model serving environments. He touched upon how to leverage Git to solve rigorous MLOps needs: automating workflows, reviewing models, storing versioned models as artifacts, and running CI/CD for ML. He also covered how to enable controlled collaboration across ML teams using Git review processes and how to implement an MLOps solution based on available open-source tools and hosted ML platforms. The session includes a live demo.

The data science team at IHS Markit will be sharing practical advice on building sophisticated NLP pipelines that work at scale. Using a robust and automated MLOps process, they run complex models that make massive amounts of unstructured data searchable and indexable. In this session, they will share their journey with MLOps and provide practical advice for other data science teams looking to.

MLOps holds the key to accelerating the development, deployment and management of AI, so that enterprises can derive real business value from their AI initiatives. Deploying and managing deep learning models in production carries its own set of complexities. In this talk, we will discuss real-life examples from customers that have built MLOps pipelines for deep learning use cases. For example, predicting rainfall from CCTV footage to prevent flooding.

MLOps holds the key to accelerating the development and deployment of AI, so that enterprises can derive real business value from their AI initiatives. From the first model deployed to scaling data science across the organization. The foundation you set will enable your team to build and monitor a growing amount of AI applications in production. In this talk, we will share best practices from our experience with enterprise customers who have effectively built and deployed composite machine and deep learning pipelines.

Given the growing number of AI projects and the complexities associated with bringing these projects to production, and specifically the challenges associated with feature engineering, the industry needs a way to standardize and automate the core of feature engineering. Feature stores provide enterprises with a competitive edge, as they enable them to expedite and simplify the path from lab to production. They enable sharing and re-use of features across teams and projects to save time and effort and ensure consistency across training and inference.

The data science team at IHS Markit has been hard at work building sophisticated NLP pipelines that work at scale using the Iguazio MLOps platform and open-source MLRun framework. Today they will share their journey and provide advice for other data science teams looking to: Nick (IHS Markit) and Yaron (Iguazio) will share their approach to automating the NLP pipeline end to end. They’ll also provide details on leveraging capabilities such as Spot integration and Serving Graphs to reduce costs and improve the data science process.

Watch this video to learn more about model serving, model monitoring, and how to overcome the challenge of getting data pipeline to run in production.

Watch this video from MLOps NYC to learn about where online feature stores come in to play and how ML teams can solve operational challenges in a simple and reproducible way with Iguazio's solutions.

With the growing business demand for real-time use cases such as NLP, fraud prediction, predictive maintenance and real-time recommendations, ML teams are feeling immense pressure to solve the operational challenges of real-time feature engineering for machine learning, in a simple and reproducible way. This is where online feature stores come in. An online feature store accelerates the development and deployment of online AI applications by automating feature engineering and providing a single pane of glass to build, share and manage features across the organization.