Sophia and David from SAS discuss challenges in MLOps, integrating generative AI, transitioning to real-time processes, and empowering business users with AI innovation. They also explore obstacles in moving AI models to production, collaboration between data scientists and engineers, and common themes in high-performing ML AI teams.
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Quick takeaways
Implementing MLOps requires proper monitoring and alert systems to address zombie models and lack of governance.
MLOps applications span various industries, showcasing real-time event capture and the need for privacy measures.
Advanced AI agents simplify user interactions with natural language processing, emphasizing task specificity and planning.
Successful AI projects prioritize effective communication, collaboration, and experimentation for sustained success.
Deep dives
Challenges and Pitfalls in Implementing MLOps
Implementing MLOps often presents challenges like zombie models and lack of governance, leading to unintentional errors. Companies face issues with integrating different frameworks and managing numerous machine learning models, requiring proper monitoring and alert systems. Orgnaizations grapple with evaluating and monitoring chatbots and large language models, as real-time text monitoring poses technical complexities and demands for standardized evaluation.
Sophisticated Applications of MLOps in Various Industries
MLOps applications span a wide range of industries, from worker safety measures using computer vision in logistics to optimizing solar farms with IoT tech. The capture of real-time events, such as a partial solar eclipse, showcases the intricate applications in industries. The need for privacy and security measures in deploying large language models is a significant concern, ensuring sensitive data remains protected.
Enhancing User Experience with Advanced AI Agents
Advanced AI agents are being developed to streamline user interactions, making complex software easier to navigate. These agents offer user-friendly interfaces for users to input commands or queries using natural language processing, resulting in simplified workflows. While agents can automate various processes efficiently, their success hinges on task specificity and careful planning to anticipate user needs.
Success Factors in AI and ML Journey
Organizations excelling in AI and ML journeys share common themes like effective communication, collaboration, and experimentation. High-performing teams prioritize flexible yet standardized processes, fostering adaptability and continuous improvement. Valuing successes, celebrating milestones, and maintaining a forward-looking approach are critical aspects of sustained success in AI and ML projects.
Data Scientists and Data Engineers Collaboration in MLOps
The collaboration between data scientists and data engineers is crucial in ensuring the seamless implementation of MLOps. Organizations succeed when integrating machine learning models into production by establishing clear handoff points, efficient training processes, and effective data scoring mechanisms. Scalability and platform-centric approaches enable teams to efficiently deploy and manage diverse machine learning projects.
Overcoming Challenges in AI Implementation
Overcoming challenges in AI implementation requires addressing issues such as data handling, integration, and monitoring. Successful AI projects involve clear communication, structured processes, and adaptive strategies. Collaborative efforts between data scientists and engineers, along with a focus on privacy and security in AI applications, contribute to the overall success of AI projects.
Innovative Applications and Challenges in AI Implementation
Innovative AI applications across industries introduce unique challenges like privacy concerns, security issues, and technical complexities. Successfully implementing AI solutions involves effective communication, experiment-driven approaches, and standardized yet flexible work processes. Overcoming hurdles in AI deployment requires a holistic view, emphasizing collaboration, adaptability, and ongoing evaluation.
Sophia Rowland is a Senior Product Manager focusing on ModelOps and MLOps at SAS. In her previous role as a data scientist, Sophia worked with dozens of organizations to solve a variety of problems using analytics.
David Weik has a passion for data and creating integrated customer-centric solutions. Thinking data and people first to create value-added solutions.
Extending AI: From Industry to Innovation // MLOps Podcast #247 with Sophia Rowland, Senior Product Manager and David Weik, Senior Solutions Architect of SAS.
Huge thank you to SAS for sponsoring this episode. SAS - http://www.sas.com/
// Abstract
Organizations worldwide invest hundreds of billions into AI, but they do not see a return on their investments until they are able to leverage their analytical assets and models to make better decisions. At SAS, we focus on optimizing every step of the Data and AI lifecycle to get high-performing models into a form and location where they drive analytically driven decisions. Join experts from SAS as they share learnings and best practices from implementing MLOps and LLMOPs at organizations across industries, around the globe, and using various types of models and deployments, from IoT CV problems to composite flows that feature LLMs.
// Bio
Sophia Rowland
Sophia Rowland is a Senior Product Manager focusing on ModelOps and MLOps at SAS. In her previous role as a data scientist, Sophia worked with dozens of organizations to solve a variety of problems using analytics. As an active speaker and writer, Sophia has spoken at events like All Things Open, SAS Explore, and SAS Innovate as well as written dozens of blogs and articles. As a staunch North Carolinian, Sophia holds degrees from both UNC-Chapel Hill and Duke including bachelor’s degrees in computer science and psychology and a Master of Science in Quantitative Management: Business Analytics from the Fuqua School of Business. Outside of work, Sophia enjoys reading an eclectic assortment of books, hiking throughout North Carolina, and trying to stay upright while ice skating.
David Weik
David joined SAS in 2020 as a solutions architect. He helps customers to define and implement data-driven solutions. Previously, David was a SAS administrator/developer at a German insurance company working with the integration capabilities of SAS, Robotic Process Automation, and more.
// MLOps Jobs board
https://mlops.pallet.xyz/jobs
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
http://www.sas.com/
--------------- ✌️Connect With Us ✌️ -------------
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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Sophia on LinkedIn: https://www.linkedin.com/in/sophia-rowland/
Connect with David on LinkedIn: https://www.linkedin.com/in/david-weik/
Timestamps:
[00:00] Sophia & David's preferred coffee
[00:19] Takeaways
[02:11] Please like, share, leave a review, and subscribe to our MLOps channels!
[02:55] Hands on MLOps and AI
[05:14] Next-Gen MLOps Challenges
[07:24] Data scientists adopting software
[11:48] Taking a different approach
[13:43] Zombie Model Management
[16:36] Optimizing ML Revenue Allocation
[18:39] Other use cases - Lockout - Tagout procedure
[21:43] Vision Model Integration Challenges
[26:16] Costly errors in predictive maintenance
[27:25] Integration of Gen AI
[34:32] Governance challenges in AI
[38:00] Governance in Gen AI vs Governance with Traditional ML
[41:53] Evaluation challenges in industries
[46:49] Interface frustration with Chatbots
[51:25] Implementing AI Agent's success
[54:18] Usability challenges in interfaces
[57:03] Themes in High-Performing AI Teams
[1:00:51] Wrap up
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