Harnessing MLOps in Finance // Michelle Marie Conway // MLOps Podcast Coffee #174
MLOps.community
Challenges in MLOps Projects
The speakers discuss the challenges that arise when things go wrong in machine learning operations (MLOps) projects, including the importance of model validations, the impact of COVID-19 on models, and the need for collaboration between data engineers and data scientists.
MLOps Coffee Sessions #174 with Michelle Marie Conway, Harnessing MLOps in Finance: Bringing Statistical Models to Life for Positive Impact, co-hosted by Stephen Batifol. // Abstract Michelle Marie Conway joins hosts Stephen Batifol and Demetrios to share their insights and experiences in the tech industry. Michelle emphasizes the importance of constant learning and adaptation in the rapidly changing tech industry. They discuss the need to stay up to date with the latest documentation, understand code logic, and be mindful when writing code. Michelle also reflects on their experiences as one of the few women in their university math class and often being the only woman on their team in the workplace. They discuss the need for more girls to pursue STEM subjects in schools and the importance of allies in the workplace. Additionally, Michelle explores the benefits and challenges of AI tools, sharing their experiences with tools like Gen AI and Chat GPT. While AI tools enhance productivity, Michelle also acknowledges the limitations of these tools in more technical tasks and the continued reliance on developer resources. This episode offers valuable insights into the importance of continuous learning, gender diversity in STEM, and the potential of AI tools in the field of MLOps. // Bio As an Irish woman who relocated to London after completing her university studies in Dublin, Michelle spent the past 12 years carving out a career in the data and tech industry. With a keen eye for detail and a passion for innovation, She has consistently leveraged my expertise to drive growth and deliver results for the companies she worked for. As a dynamic and driven professional, Michelle is always looking for new challenges and opportunities to learn and grow, and she's excited to see what the future holds in this exciting and ever-evolving industry. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Stephen on LinkedIn: https://www.linkedin.com/in/stephen-batifol/ Connect with Michelle on LinkedIn: https://www.linkedin.com/in/michelle-conway-40337432 Timestamps: [00:00] Michelle's preferred coffee [02:04] Takeaways [05:18] Please like, share, and subscribe to our MLOps channels! [06:18] Michelle's journey in tech [07:49] Engineering best practices [09:38] Getting comfortable with the hump [11:22] Clean coding fundamentals [13:29] Working with the people [14:09] GCP migration [18:00] GCP migration length of journey [18:38] Moving data focus [19:18] Effectiveness of running 2 systems [21:00] Dealing with discrepancies [22:15] Using Nexus [24:04] Migrating data from Teradata to BigQuery, strict security [28:48] Hiring new people [30:56] Securely managing financial data with millions of customers [32:30] When things go wrong [35:08] Finding the root cause [36:28] Dealing with the producers' problems [40:46] Rapid tech evolution constant learning [44:44] Teaching Python, using Gen AI for tasks [46:34] Dealing with LLMs use cases [49:15] Dealing with stakeholders and MLOps teams [51:17] Having a translator [52:18] Being a woman in the tech industry [55:11] Encourage more girls in STEM, support women [56:36] Women in the conversation on tech and female representation [1:03:49] Wrap up
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