Over the past several years, MLOps has taken on several different meanings depending on whom you are talking to. Building a strong understanding of what it is, and what it can be, can help you effectively employ MLOps to help your organization achieve its goals in this day and age. At ODSC Europe, there will be several talks on this important topic to help you learn MLOps, some of which you can find below.
Ariel Biller, PhD｜Evangelist｜ClearML
Take a deep dive into exactly what MLOps means today. During this session you will identify the essential practices that even a solo researcher should adopt, distill the demands for reproducibility and deployments to a bare minimum shared by all stakeholders in the operational ML process., discover the fundamental charter of MLOPs, and draft basic recipes for incrementally crafting functional MLOps units.
Model Governance: A Checklist for Getting AI Safely to Production
David Talby, PhD｜CTO｜John Snow Labs
Join this talk to learn MLOps and catch up on current best practices and freely available tools for storing, versioning, collaborating, securing, testing, and building AI models so that your team can go beyond experimentation to successful deployment.
MLOps Will Change Machine Learning
Magdalena Stenius｜Full Stack Developer｜Valohai
Machine learning has evolved from the experimenting stage to real-world production systems with a need for automated quality assurance and delivery, reproducibility and deployment consistency. This talk will seek to answer the question: How can we extend our DevOps processes into MLOps and how will that impact the machine learning systems we use today?
MLOps Orchestration: Your Highway to Accelerating Deployment of AI
Yaron Haviv｜Co-Founder and CTO｜Iguazio
In this workshop, we will explore the concept of MLOps Orchestration and how it can simplify the process of getting data science to production in any environment, from the step of data collection and preparation, through automated model training to model deployment and monitoring through a live demo and real customer case studies across use cases such as fraud prevention, real-time recommendation engines, and NLP.
Building Real-Time ML Pipelines the Easy Way
Yaron Haviv｜Co-Founder and CTO｜Iguazio
Review the challenges of handling real-time data in research and production environments and solutions that exist to enable you to build a real-time operational ML pipeline that can handle events arriving in ultra-high velocity and high volume, calculate and trigger an action in seconds.
Production Machine Learning: Monitoring Principles, Patterns, and Techniques
Alejandro Saucedo｜Chief Scientist｜The Institute for Ethical AI & Machine Learning
In this talk, you will take a practical deep dive into the best practices, principles, patterns, and techniques around production monitoring of machine learning models, including standard microservice monitoring techniques applied into deployed machine learning models, as well as more advanced paradigms to monitor machine learning models through concept drift, outlier detection, and explainability.
Introduction to Data Analysis Using Pandas
Stefanie Molin｜Data Scientist, Software Engineer, Author of Hands-On Data Analysis with Pandas｜Bloomberg
Working with data can be challenging: it often doesn’t come in the best format for analysis, and understanding it well enough to extract insights requires both time and the skills to filter, aggregate, reshape, and visualize it. This session will equip you with the knowledge you need to effectively use pandas – a powerful library for data analysis in Python – to make the process of filtering, aggregating, reshaping, and visualizing data easier.
Enterprise Ready ML: MOdel Training on Hybrid Cloud Leveraging Kubernetes
Saurya Das｜Senior Product Manager｜Microsoft
Join this session to learn about the unified AML Kubernetes native agent that will allow you to seamlessly train ML models on Kubernetes currently being developed. It will allow you to centrally manage and govern all your Kubernetes resources in one place and use capacity flexibly for all workloads including AML.
Build an ML Pipeline with Airflow and Kubernetes
Luis Blanche｜Lead Data Scientist｜Dataswati
Learn how Airflow, a leading open-source workflow orchestrator that offers a very wide range of possibilities, can be integrated with Kubernetes using the KubernetesPodOperator to create pipelines that are extremely customizable.
Register for ODSC Europe 2021 Now and Learn MLOps
Join us next week at ODSC Europe to learn more about MLOps, NLP, Data Visualization, Deep Learning, and much more from our expert speakers. Get your pass here.