Guests Amritha Arun Babu Mysore and Abhik Choudhury discuss best practices for building ML Ops architectures, challenges in ML model lifecycle, customer-centric approach, data processing hurdles, diverse paths in MLOps transition, and challenges in transitioning to data science concepts.
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Quick takeaways
Data engineers must prioritize scalability, outliers, and compliance in MLOps landscape.
Data scientists need to ensure visibility from experimentation to deployment for successful model implementation.
DevOps professionals entering MLOps should understand hyperparameter tuning and seamless model deployment.
Deep dives
Data Engineering Perspective in Maturity Levels of MLOps
Data engineers focus on data acquisition, transformation, and scalability in the MLOps landscape. Understanding data science terminologies and requirements is crucial to support data scientists effectively. Addressing scalability challenges, handling outliers, and ensuring compliance are key aspects data engineers should prioritize.
Data Scientist's Role in MLOps Evolution
Data scientists play a vital role in the experimentation and development phases of MLOps. From parameterization to model deployment and version management, data scientists need to transition to working with code repositories and automation tools. Ensuring visibility from experimentation to deployment is essential for successful model implementation.
DevOps Professional Transitioning into MLOps
DevOps professionals venturing into MLOps must grasp concepts like hyperparameter tuning to optimize machine learning models. Understanding model parameter changes based on data shifts, and deploying the model seamlessly are crucial skills for DevOps professionals entering the machine learning domain.
Importance of Compliance in MLOps
Ensuring compliance with data regulations and company policies is vital in MLOps implementations. Rigorous security reviews, legal oversight, and consistent monitoring of data handling policies are necessary to align MLOps practices with legal and regulatory requirements.
Model Monitoring Across Maturity Levels
Model monitoring evolves across MLOps maturity levels, with rudimentary approaches in lower stages to more advanced, tool-driven strategies in higher maturity levels. Level Zero involves basic data analysis periodically, while Level Two requires dedicated modeling tools, KPI tracking, and proactive model adjustments for optimally monitoring and maintaining machine learning models.
Amritha Arun Babu Mysore has been an expert in the field of consumer electronics, software products, and online marketplaces for the past 15 years. She has experience developing supply chains from the ground up, delivering AI-based products to millions of users, and advocating for ethical AI across Amazon, Wayfair, Salesforce, and NetApp.
Abhik Choudhury is a Senior Analytics Managing Consultant and Data Scientist with 11 years of experience in designing and implementing scalable data solutions for organizations across various industries.
Huge thank you to @latticeflow for sponsoring this episode. LatticeFlow - https://latticeflow.ai/
MLOps podcast #221 with Amritha Arun Babu Mysore, ML Product Leader at Klaviyo and Abhik Choudhury, Managing Consultant Analytics at IBM, MLOps - Design Thinking to Build ML Infra for ML and LLM Use Cases.
// Abstract
As machine learning (ML) and large language models (LLMs) continue permeating industries, robust ML infrastructure and operations (ML Ops) are crucial to deploying these AI systems successfully. This podcast discusses best practices for building reusable, scalable, and governable ML Ops architectures tailored to ML and LLM use cases.
// Bio
Amritha Arun Babu Mysore
Amritha is an accomplished technology leader with over 12 years of experience spearheading product innovation and strategic initiatives at both large enterprises and rapid-growth startups.
Leveraging her background in engineering, supply chain, and business, Amritha has led high-performing teams to deliver transformative solutions solving complex challenges. She has driven product road mapping, requirements analysis, system design, and launch execution for advanced platforms in domains like machine learning, logistics, and e-commerce.
Abhik Choudhury
Abhik is a Senior Analytics Managing Consultant and Data Scientist with 11 years of experience in designing and implementing scalable data solutions for organizations across various industries. Throughout his career, Abhik developed a strong understanding of AI/ML, Cloud computing, database management systems, data modeling, ETL processes, and Big Data Technologies. Abhik's expertise lies in leading cross-functional teams and collaborating with stakeholders at all levels to drive data-driven decision-making in longitudinal pharmacy and medical claims and wholesale drug distribution areas.
// MLOps Jobs board
https://mlops.pallet.xyz/jobs
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
AI Quality in Person Conference in collaboration with Kolena: https://www.aiqualityconference.com/
LatticeFlow website: https://latticeflow.ai/
--------------- ✌️Connect With Us ✌️ -------------
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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Abhik on LinkedIn: https://www.linkedin.com/in/abhik-choudhury-35450058
Connect with Amritha on LinkedIn: https://www.linkedin.com/in/amritha-arun-babu-a2273729/
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