Agenda
November 8th
Join The Journey: ML DataOps for Advancing AI
iMerit Founder and CEO Radha Basu welcome thousands of data scientists, engineers, and ML professionals to join her on the journey to explore the machine learning data operations landscape. Attendees will gain insights on the road ahead for advancing AI with data intelligence, ML DataOps readiness for enterprise AI, solving edge cases in production AI and the latest trends happening in the ML DataOps.
Pushing the Frontiers in AI For Billions Around the World
At Meta, AI is pervasive and critical to the proliferation of our products for billions of users worldwide. Join Meta AI’s Manohar Paluri to learn a simple but effective framework for pushing the frontiers in AI research, while advancing technology that is impactful to the product end game. Attendees will gain insights into the structure for developing AI applications including scaling ML models, adopting a multi-modal understanding, pairing tools and human intelligence to accelerate AI and more. Wrapping the session, iMerit’s Founder and CEO Radha Basu and Manohar will delve into a discussion about the power of human intelligence to improve AI.
The Stakes Are High: Best Practices For Deploying Responsible AI
AI is sparking significant change across industries worldwide, with projections to add more than $15 trillion to the global economy by 2030. By 2022, more than 60% of enterprises will have implemented machine learning, big data analytics, and related AI tools into their operations. Enterprises navigating the complexities of artificial intelligence– from data operations to full-scale commercialization–must do it with a focused, practical lens. Join this session to hear from IBM’s Former Chief AI Officer and newly appointed President of the Responsible AI Institute to gain insight into the best practices for deploying artificial intelligence responsibly.
Overcoming the Obstacles of Achieving High Quality Data
Ready to reinvent ML DataOps 2.0? Join iMerit experts as they talk industry trends, insights, and challenges that hundreds of companies face as they work towards production ready AI. Learn about the 6 obstacles holding companies back from achieving high quality data and the innovative solutions needed to overcome them
Convergence of ML Ops and Data Pipelines
Machine learning has grown exponentially in the last decade, drastically transforming machine learning data operation efforts. Data scientists and professionals alike realize the daunting effort of converging healthy data pipelines with machine learning models to produce successful AI applications. Join this session to hear ML experts share how they are transforming their data pipelines across collection, preparation, management and development to better manage their data, improve ML throughput and create meaningful AI applications for their business.
Role of Automation in ML DataOps
Integrating intelligent automation across machine learning data operations simplifies data preparation, optimizes data workflows and saves significant time and resources. In this session, attendees will hear AI experts reveal the role automation plays in machine learning data operations today, and in the foreseeable future.
Navigating Data Tooling and Expertise To Achieve High-Quality AI Training Data
As enterprises navigate the data tooling ecosystem, many realize the need to activate two vital components to achieve high-quality AI training data – data tooling and a talented tooling workforce. Join this session to hear from data tooling experts as they unveil what makes enterprise-grade worthy tools, why data tools require super users, realization that one tool isn’t a fit for all and what lies ahead in the data tooling ecosystem.
It’s a Duo: Human-in-the-Loop and High-Quality Data Are Catalysts in ML and AI
With the proliferation of production AI, enterprises realize that when machine learning can’t solve the problem, humans need to intervene. To account for this gap, enterprises are integrating robust human-in–the-loop programs to improve their AI training data and precision of their models. Attendees will get a front-row seat to better understand why human-in-the-loop and high-quality data go hand-in-hand to achieve widespread production AI applications.
Why Solving Data Edge Cases is Key to Accelerating AI
It’s no secret that AI systems are being used in more and more high-stakes applications – self-driving cars, robotic surgery and more. As AI advances, it’s becoming critical to ensure that AI systems navigate real-world anomalies successfully. Join this session to hear veteran machine learning experts discuss what it takes to identify and solve data edge cases to maximize AI performance and achieve widespread adoption.
Mastering Anomaly Detection and Test Case Generation in ML
Enterprises building AI applications recognize that mastering anomaly detection and test case generation brings incredible value to improving machine learning model performance. Join this session to hear ML experts unveil the challenges and opportunities of identifying data anomalies and generating test cases to optimize your machine learning.
Unforeseen Challenges and Opportunities in Commercializing AI
AI powers a plethora of real-world applications, ranging from facial recognition and image detection to language translators and assistants like Siri and Alexa. As more companies harness the power of AI, many are navigating unforeseen challenges and opportunities in commercializing AI. Hear from AI experts as they dive into overcoming the obstacles – good and bad – that enable the widespread adoption of their AI applications.
Current and Future State of ML DataOps Landscape
As enterprises dive deeper into commercializing AI applications to improve business efficiencies, many realize the massive transformation and increasing complexity of the machine learning data operations landscape. Catch this session to hear seed stage and growth stage venture capitalists share their perspectives on the current and future state of the machine learning data operations landscape.
The Journey Wraps: ML DataOp Summit Key Takeaways
iMerit Founder and CEO Radha Basu wraps the day by sharing several key takeaways attendees can use as tools to move forward in deploying their enterprise AI application.