Sweta Sinha
Director of AI Engineering, Ascend Learning
Kansas City, Missouri, United States
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Sweta Sinha is the Director of AI Engineering where she leads the design and delivery of enterprise-grade machine learning, generative AI, and agentic systems across large-scale healthcare and education platforms. She heads the AI Center of Excellence and guides teams through end-to-end AI initiatives, from early experimentation to production-ready AI systems, with a strong focus on reliability, evaluation, and governance.
Her work spans AI-native product development, agentic workflows, and platform architecture, with an emphasis on translating emerging AI technologies into scalable, repeatable engineering practices. Sweta also plays a key role in shaping AI governance across the organization, ensuring autonomy, safety, and accountability are enforced through concrete technical controls.
She focuses on helping teams move beyond experimentation to build trustworthy, AI-native systems that deliver real-world impact.
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Operationalizing AI models with ModelOps - Delivering value at scale
Description: Organizations are realizing the revolutionary potential of Artificial Intelligence In gaining competitive advantage. Though AI is in mainstream, many organizations are still struggling to move beyond proof of concept to operationalizing the models and optimize the ROI on AI investments. ModelOps takes principled approach to operationalizing AI models into production, thereby accelerating AI initiatives to deliver continuous business value. A well strategized ModelOps practices will automate and augment model life cycle management and make AI ready for DevOps. In this session you will learn about steps to take to operationalize AI/ML model with enterprise level ModelOps platform.
Role of Data Governance in building Responsible AI system
In today's digital age, organizations are amassing huge amounts of data from various sources. However, managing and utilizing this data effectively is becoming increasingly complex and challenging. The use of AI, particularly in regulated industries such as healthcare, finance, and law enforcement, has brought new ethical and legal considerations, raising concerns about data privacy, security, and potential biases in AI systems. Therefore, it is crucial for organizations to have clear policies and procedures in place to govern the collection, storage, and use of data being used by AI systems, and ensure that these systems are developed and used responsibly. In this session, you will learn about how organizations can implement proper governance and oversight of data used in AI systems, including issues of accuracy, bias, privacy, and security. Additionally, we will also explore the challenges and best practices for implementing data governance in organizations developing AI systems and the role of stakeholders in ensuring responsible AI development and use.
Generative AI: Why businesses should care?
Recent advancements in Generative AI have led to the development of powerful models such as GPT-3 and recently ChatGPT, which have proven to be capable of generating high-quality, human-like text, images, and audio. These developments have opened up a range of opportunities in industries such as content creation, natural language processing, and computer-aided design. In this session, we will delve into the benefits of implementing generative AI models in automating processes, uncovering insights, and driving innovation in businesses. Furthermore, we will examine practical ways in which businesses can harness the power of Generative AI to enhance efficiency, automate repetitive tasks and make data-driven decisions.
AI DevWorld 2021 Sessionize Event
KCDC 2021 Sessionize Event
Kansas City Women in Tech
Tech Talk: What Do Data Scientists Actually Do?
Data Science is one of the fastest growing career fields in the 21st century, but what do data scientists actually do? Learn all about the career at our October TechTalk on October 17 from 6pm to 8pm with a Q&A with data scientists who work in the field. Our panelists will share what it’s like to work in the field and how organizations use data to inform their processes.
Techweek Kansas City 2018
Topic: Data Science in ELearning
Abstract: E-Learning analytics makes use of huge wealth of data gathered from online learner digital footprint by employing various data science techniques and machine learning. This data can be used to improve the learner’s experience, knowledge acquired and skill gained and increase the effectiveness of learning resources.
Kansas City R Users Group
Talk Topic: Uses of Data Science at Ascend Learning
IIBA Kansas CIty
Presentation Description: Data Analytics? Business Intelligence? Data Science? Machine Learning? If you are confused about the world of data, please join the Kansas City Chapter of the IIBA to for an overview of the world of Data Analytics and Data Science, presented by Diana Alt and Sweta Sinha. Our presenters will give you a peek into the world of analytics - from descriptive to prescriptive analytics and share how product management and data science teams work together to create valuable analytics products.
Sweta Sinha
Director of AI Engineering, Ascend Learning
Kansas City, Missouri, United States
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