Michelle Marie Conway, a tech industry professional, shares insights on continuous learning, gender diversity in STEM, and the potential of AI tools in MLOps. Topics include staying up to date with documentation, understanding code logic, challenges and benefits of AI tools, and the importance of communication with stakeholders in the banking sector. The importance of diversity in the tech industry and efforts to create inclusive environments are also discussed.
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Quick takeaways
The migration of a large bank's data to Google Cloud Platform (GCP) involves careful planning, secure environments, and parallel runs for accuracy and consistency.
Security and maintainability are crucial in the banking industry's transition to cloud platforms, emphasizing the need for strict security measures, continuous monitoring, and understanding legacy systems.
Constant learning and staying updated with the latest techniques, languages, and libraries are essential for professionals in the tech industry, along with leveraging large language models for increased productivity and collaboration within teams.
Deep dives
GCP Migration and the Challenge of Legacy Data
The podcast episode discusses the ongoing migration of a large bank's data to Google Cloud Platform (GCP). The bank, with 70,000 employees and a 300-year history, currently has a risk-averse approach to data management, keeping everything on-premises. However, they are embarking on a multi-year, multi-billion-dollar project to migrate their data to GCP. The main goal is to make their data more manageable, sustainable, and accessible by creating data products and ensuring proper risk management and metadata. The migration process involves carefully planning and implementing releases, with restricted and secure environments. Parallel runs of models and data in both systems are necessary to ensure accuracy and consistency. While the process is lengthy and challenging, the bank aims to create a secure and effective cloud-based infrastructure that will greatly benefit their operations.
The Importance of Security and Maintainability
The podcast highlights the importance of security and maintainability in the banking industry's transition to cloud platforms. With vast amounts of sensitive customer data at stake, banks must prioritize data protection and risk management. The speaker emphasizes the need for strict security measures, continuous monitoring, and access controls to safeguard data. Additionally, the discussion touches on the challenges of maintaining legacy code bases, where documentation and best practices may be lacking. The episode underscores the importance of understanding and navigating legacy systems while adopting modern technologies, such as Python, to ensure maintainability and effective collaboration among team members. Data scientists and engineers in the banking industry must strike a balance between embracing new technologies and approaches while appreciating the value and complexities of legacy infrastructure.
Learning and Evolving in the Tech Field
The podcast episode delves into the evolving tech landscape and the ongoing learning required for professionals in the field. The speaker discusses the challenges and opportunities faced when entering the tech industry, particularly as a data scientist. The episode acknowledges the differences in tech exposure and proficiency between generations, highlighting the speed at which Gen Z has adapted to and absorbed technology. The speaker emphasizes the need to constantly learn, stay updated with the latest techniques, and embrace different programming languages and libraries. Furthermore, the importance of understanding legacy code and documentation is emphasized, as it enables smooth transitions and effective knowledge transfer between generations. The episode also touches on the potential of large language models like GPT to enhance productivity and collaboration within teams by providing relevant information and insights.
Encouraging Girls in STEM Education
Encouraging girls to take up STEM subjects in school, such as science, technology, engineering, and math, is crucial for increasing diversity in the tech industry. Currently, there is a significant gender imbalance, with only around 20-30% of students in STEM degrees being women. By promoting and supporting girls' interest in these subjects from a young age, we can create a stronger pipeline of female talent entering the tech workforce.
Mitigating Problems and Model Validations
The podcast episode discusses the importance of having rigorous model validations in place to catch potential issues in machine learning models. One example mentioned is the use of statistical significance checks to ensure that features used in models are reliable. If a feature fails the significance threshold, the model will not produce a forecast. The episode also highlights the value of having robust pipelines and clear error notifications to quickly identify and debug problems in production.
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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