The promise of AI in enterprise settings is enormous, but so are the privacy and security challenges. How do you harness AI's capabilities while keeping sensitive data protected within your organization's boundaries? Private AI—using your own models, data, and infrastructure—offers a solution, but implementation isn't straightforward. What governance frameworks need to be in place? How do you evaluate non-deterministic AI systems? When should you build in-house versus leveraging cloud services? As data and software teams evolve in this new landscape, understanding the technical requirements and workflow changes is essential for organizations looking to maintain control over their AI destiny. Manasi Vartak is Chief AI Architect and VP of Product Management (AI Platform) at Cloudera. She is a product and AI leader with more than a decade of experience at the intersection of AI infrastructure, enterprise software, and go-to-market strategy. At Cloudera, she leads product and engineering teams building low-code and high-code generative AI platforms, driving the company’s enterprise AI strategy and enabling trusted AI adoption across global organizations. Before joining Cloudera through its acquisition of Verta, Manasi was the founder and CEO of Verta, where she transformed her MIT research into enterprise-ready ML infrastructure. She scaled the company to multi-million ARR, serving Fortune 500 clients in finance, insurance, and capital markets, and led the launch of enterprise MLOps and GenAI products used in mission-critical workloads. Manasi earned her PhD in Computer Science from MIT, where she pioneered model management systems such as ModelDB — foundational work that influenced the development of tools like MLflow. Earlier in her career, she held research and engineering roles at Twitter, Facebook, Google, and Microsoft. In the episode, Richie and Manasi explore AI's role in financial services, the challenges of AI adoption in enterprises, the importance of data governance, the evolving skills needed for AI development, the future of AI agents, and much more. Links Mentioned in the Show: ClouderaCloudera Evolve ConferenceCloudera Agent StudioConnect with ManasiCourse: Introduction to AI AgentsRelated Episode: RAG 2.0 and The New Era of RAG Agents with Douwe Kiela, CEO at Contextual AI & Adjunct Professor at Stanford UniversityRewatch RADAR AI  New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The enterprise adoption of AI agents is accelerating, but significant challenges remain in making them truly reliable and effective. While coding assistants and customer service agents are already delivering value, more complex document-based workflows require sophisticated architectures and data processing capabilities. How do you design agent systems that can handle the complexity of enterprise documents with their tables, charts, and unstructured information? What's the right balance between general reasoning capabilities and constrained architectures for specific business tasks? Should you centralize your agent infrastructure or purchase vertical solutions for each department? The answers lie in understanding the fundamental trade-offs between flexibility, reliability, and the specific needs of your organization. Jerry Liu is the CEO and Co-founder at LlamaIndex, the AI agents platform for automating document workflows. Previously, he led the ML monitoring team at Robust Intelligence, did self-driving AI research at Uber ATG, and worked on recommendation systems at Quora. In the episode, Richie and Jerry explore the readiness of AI agents for enterprise use, the challenges developers face in building these agents, the importance of document processing and data structuring, the evolving landscape of AI agent frameworks like LlamaIndex, and much more. Links Mentioned in the Show: LlamaIndexLlamaIndex Production Ready Framework For LLM AgentsTutorial: Model Context Protocol (MCP)Connect with JerryCourse: Retrieval Augmented Generation (RAG) with LangChainRelated Episode: RAG 2.0 and The New Era of RAG Agents with Douwe Kiela, CEO at Contextual AI & Adjunct Professor at Stanford UniversityRewatch RADAR AI  New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The line between generic AI capabilities and truly transformative business applications often comes down to one thing: your data. While foundation models provide impressive general intelligence, they lack the specialized knowledge needed for domain-specific tasks that drive real business value. But how do you effectively bridge this gap? What's the difference between simply fine-tuning models versus using techniques like retrieval-augmented generation? And with constantly evolving models and technologies, how do you build systems that remain adaptable while still delivering consistent results? Whether you're in retail, healthcare, or transportation, understanding how to properly enrich, annotate, and leverage your proprietary data could be the difference between an AI project that fails and one that fundamentally transforms your business. Wendy Gonzalez is the CEO — and former COO — of Sama, a company leading the way in ethical AI by delivering accurate, human-annotated data while advancing economic opportunity in underserved communities. She joined Sama in 2015 and has been central to scaling both its global operations and its mission-driven business model, which has helped over 65,000 people lift themselves out of poverty through dignified digital work. With over 20 years of experience in the tech and data space, Wendy’s held leadership roles at EY, Capgemini, and Cycle30, where she built and managed high-performing teams across complex, global environments. Her leadership style blends operational excellence with deep purpose — ensuring that innovation doesn’t come at the expense of integrity. Wendy is also a vocal advocate for inclusive AI and sustainable impact, regularly speaking on how companies can balance cutting-edge technology with real-world responsibility. Duncan Curtis is the Senior Vice President of Generative AI at Sama, where he leads the development of AI-powered tools that are shaping the future of data annotation. With a background in product leadership and machine learning, Duncan has spent his career building scalable systems that bridge cutting-edge technology with real-world impact. Before joining Sama, he led teams at companies like Google, where he worked on large-scale personalization systems, and contributed to AI product strategy across multiple sectors. At Sama, he's focused on harnessing the power of generative AI to improve quality, speed, and efficiency — all while keeping human oversight and ethical practices at the core. Duncan brings a unique perspective to the AI space: one that’s grounded in technical expertise, but always oriented toward practical solutions and responsible innovation. In the episode, Richie, Wendy, and Duncan explore the importance of using specialized data with large language models, the role of data enrichment in improving AI accuracy, the balance between automation and human oversight, the significance of responsible AI practices, and much more. Links Mentioned in the Show: SamaConnect with WendyConnect with DuncanCourse: Generative AI ConceptsRelated Episode: Creating High Quality AI Applications with Theresa Parker & Sudhi Balan, Rocket SoftwareRegister for RADAR AI New to DataCamp? Learn on the go...

Retrieval Augmented Generation (RAG) continues to be a foundational approach in AI despite claims of its demise. While some marketing narratives suggest RAG is being replaced by fine-tuning or long context windows, these technologies are actually complementary rather than competitive. But how do you build a truly effective RAG system that delivers accurate results in high-stakes environments? What separates a basic RAG implementation from an enterprise-grade solution that can handle complex queries across disparate data sources? And with the rise of AI agents, how will RAG evolve to support more dynamic reasoning capabilities? Douwe Kiela is the CEO and co-founder of Contextual AI, a company at the forefront of next-generation language model development. He also serves as an Adjunct Professor in Symbolic Systems at Stanford University, where he contributes to advancing the theoretical and practical understanding of AI systems. Before founding Contextual AI, Douwe was the Head of Research at Hugging Face, where he led groundbreaking efforts in natural language processing and machine learning. Prior to that, he was a Research Scientist and Research Lead at Meta’s FAIR (Fundamental AI Research) team, where he played a pivotal role in developing Retrieval-Augmented Generation (RAG)—a paradigm-shifting innovation in AI that combines retrieval systems with generative models for more grounded and contextually aware responses. In the episode, Richie and Douwe explore the misconceptions around the death of Retrieval Augmented Generation (RAG), the evolution to RAG 2.0, its applications in high-stakes industries, the importance of metadata and entitlements in data governance, the potential of agentic systems in enterprise settings, and much more. Links Mentioned in the Show: Contextual AIConnect with DouweCourse: Retrieval Augmented Generation (RAG) with LangChainRelated Episode: High Performance Generative AI Applications with Ram Sriharsha, CTO at PineconeRegister for RADAR AI - June 26 New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

As AI continues to advance, natural language processing (NLP) is at the forefront, transforming how businesses interact with data. From chatbots to document analysis, NLP offers numerous applications. But with the advent of generative AI, professionals face new challenges: When is it appropriate to use traditional NLP techniques versus more advanced models? How do you balance the costs and benefits of these technologies? Explore the strategic decisions and practical applications of NLP in the modern business world. Meri Nova is the founder of Break Into Data, a data careers company. Her work focuses on helping people switch to a career in data, and using machine learning to improve community engagement. Previously, she was a data scientist and machine learning engineer at Hyloc. Meri is the instructor of DataCamp's 'Retrieval Augmented Generation with LangChain' course. In the episode, Richie and Meri explore the evolution of natural language processing, the impact of generative AI on business applications, the balance between traditional NLP techniques and modern LLMs, the role of vector stores and knowledge graphs, and the exciting potential of AI in automating tasks and decision-making, and much more. Links Mentioned in the Show: Meri’s Breaking Into Data Handbook on GitHubBreak Into Data Discord GroupConnect with MeriSkill Track: Artificial Intelligence (AI) LeadershipRelated Episode: Industry Roundup #2: AI Agents for Data Work, The Return of the Full-Stack Data Scientist and Old languages Make a ComebackRewatch sessions from RADAR: Forward Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

AI features and products are the hottest area of software development. Creating high quality AI software is both essential and challenging for many businesses. In this episode, we look at retrieval augmented generation, an important technique for improving text generation quality in AI applications. Beyond technical measures, we look at the broader quality problem for AI applications. How do you ensure your AI applications are effective and secure? What steps should you take to integrate AI into your existing data governance frameworks? And how do you measure the success of these AI-driven solutions? Theresa Parker is the Director of Product Management at Rocket Software. She has 25 years of experience as a technology executive with a focus on software development processes, consultancy, and business development. Her recent work in content management focuses on the use of AI and RAG to improve content discoverability. Sudhi Balan is the Chief Technology Officer for AI & Cloud. He leads the AI and data teams for data modernization, driving AI adoption of Rocket's structured and unstructured data products. He also shapes AI strategy for Rocket’s infrastructure and app portfolio. He has earned patents for safe and scalable applications of transformational technology. Previously, he led digital transformation and hybrid cloud strategy for Rocket’s unstructured data business and was Senior Director of Product Development at ASG. In the episode, Richie, Theresa, and Sudhi explore retrieval-augmented generation, its applications in customer support and loan processing, the importance of data governance and privacy, the role of testing and guardrails in AI, cost management strategies, and the potential of AI to transform customer experiences, and much more. Links Mentioned in the Show: Rocket SoftwareConnect with Theresa and SudhiCourse: Retrieval Augmented Generation (RAG) with LangChainRelated Episode: Getting Generative AI Into Production with Lin Qiao, CEO and Co-Founder of Fireworks AIRewatch sessions from RADAR: Forward Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Perhaps the biggest complaint about generative AI is hallucination. If the text you want to generate involves facts, for example, a chatbot that answers questions, then hallucination is a problem. The solution to this is to make use of a technique called retrieval augmented generation, where you store facts in a vector database and retrieve the most appropriate ones to send to the large language model to help it give accurate responses. So, what goes into building vector databases and how do they improve LLM performance so much? Ram Sriharsha is currently the CTO at Pinecone. Before this role, he was the Director of Engineering at Pinecone and previously served as Vice President of Engineering at Splunk. He also worked as a Product Manager at Databricks. With a long history in the software development industry, Ram has held positions as an architect, lead product developer, and senior software engineer at various companies. Ram is also a long time contributor to Apache Spark.  In the episode, Richie and Ram explore common use-cases for vector databases, RAG in chatbots, steps to create a chatbot, static vs dynamic data, testing chatbot success, handling dynamic data, choosing language models, knowledge graphs, implementing vector databases, innovations in vector data bases, the future of LLMs and much more.  Links Mentioned in the Show: PineconeWebinar - Charting the Path: What the Future Holds for Generative AICourse - Vector Databases for Embeddings with PineconeRelated Episode: The Power of Vector Databases and Semantic Search with Elan Dekel, VP of Product at PineconeRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business