Daliana's Game

I interviewed Geoffery Angus, ML team lead @Predibase to talk about why adapter-based training is a game changer. We started with an overview of fine-tuning and then discussed five reasons why adapters are the future of LLMs. Later we also shared a demo and answered questions from the live audience. Try fine-tuning for free: https://pbase.ai/GetStartedGeoffrey’s LinkedIn:https://www.linkedin.com/in/geoffreyangusDaliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/⁠Daliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/Geoffrey’s LinkedIn: https://www.linkedin.com/in/geoffreyangusTry finetuning for free: https://pbase.ai/GetStarted(00:00:00) Intro(00:01:19) What is Fine-tuning?(00:08:18) Utilizing Adapters for Finetuning Enhancement(00:09:50) 5 reasons why adapters are the future of LLMs(00:26:34) Common Mistakes in Adapters Usage(00:28:34) Training Your Own Adapter(00:32:23) Behind the Scenes of the Adapter Training Process(00:37:51) Config File Guidance for Fine-Tuning(00:39:41) Debugging Strategies for Suboptimal Fine-Tuning Results(00:42:23) User Queries: Creating a LoRa Adapter and Future Support(00:51:06) Key Takeaways and Recap

Jay Feng created a viral project using Seattle crime data and later got into data science. He later founded "Interview Query" helping data scientists get jobs. We'll talk about how he landed his data science job through his blog, and his journey from data scientist to founder. Subscribe to Daliana's newsletter on ⁠www.dalianaliu.com⁠ for more on data science and career.Daliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/⁠Jay Feng's LinkedIn: ⁠https://www.linkedin.com/in/jay-feng-ab66b049/⁠Jay Feng's YouTube: ⁠https://www.youtube.com/c/DataScienceJay⁠(00:00:00) Introduction(00:01:11) From engineer to data scientist(00:03:10) Got a job through a project(00:05:35) Daliana's portfolio project with Zillow(00:09:13) From data scientist to entreprenuer(00:13:19) "Tinder" for job(00:15:01) How he chose companies to work for(00:15:56) Why he became an entreprenuer(00:17:37) How many hours does he work(00:18:54) Challenges when building "interview query"(00:20:18) Speed vs scale(00:22:11) Growth hacks he used(00:24:22) YouTube vs newsletter(00:27:21) Lessons he learned as a CEO(00:29:16) How to grow from tech employee to founder(00:31:59) How he defines success(00:34:38) If you have a business idea for Jay

Discussion on GenAI projects, stable diffusion models for social media apps, AI in biotech, and scaling ML teams. Insights on self-supervised learning, research vs production, AGI, and data quality in GenAI. Erik's journey from biologist to ML consultant, mistakes made, and new trends in GenAI. Philosophy in LLMs, OpenAI vs Open Source, and how he hires people.

Jay Feng is the CEO of interview query, a service that help data scientists get jobs. Previously he worked as a data scientist at Nextdoor, Monster. We talked about data science job market, the rise of AI engineering, and the softskills people overlook during interviews. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career.Daliana's Twitter: https://twitter.com/DalianaLiuDaliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/Jay Feng's LinkedIn: https://www.linkedin.com/in/jay-feng-ab66b049/Jay Feng's YouTube: https://www.youtube.com/c/DataScienceJay00:00:00 Introduction00:01:11 Data science job market in 202400:09:13 Build projects with AI00:16:19 Softskills in interviews00:23:18 Daliana's story on "socializing ideas"00:28:38 Common mistakes in interviews00:35:30 Product DS vs ML interviews00:36:27 Product analytics interview questions00:39:18 Career transition in DS00:43:04 Jay's career journey00:45:38 Is there a principal data analyst?00:51:52 AI engineer00:54:28 New roles vs obsolete roles in DS01:04:46 Is data science dead?

We are joined by two data scientists who have firsthand experience with layoffs. We’ll talk about how to negotiate severance packages, how to handle stress, strategies for job hunting post-layoff, and how to reduce risks in full-time employment. Working with Daliana on personal branding: https://forms.gle/heNuZzaHjaAMQwLu6Her email: daliana@dalianaliu.comGuests: Susan Shu Chang:Linkedin: https://www.linkedin.com/in/susan-shu-chang/Newsletter: susanshu.substack.comSundar SwaminathanLinkedin: https://www.linkedin.com/in/sswamina3/Website: https://www.sundarswaminathan.com/⁠(00:00:00) Introduction(00:06:13) Severance Negotiation(00:20:29) Identity crisis(00:26:22) Job search after layoff(00:30:21) Networking(00:35:23) Risk at pre-seed startups(00:37:03) How should data scientists pick companies(00:40:43) What to ask hiring managers(00:45:01) Does GenAI change interview processes?(00:47:17) Are data science teams getting leaner?(00:48:56) Future of data science roles(00:50:37) Full time employment and job security(00:53:46) Benefits of full time jobs(00:58:14) Reduce risk of being laid off(01:00:43) How to sell yourself(01:02:43) How to plan your finances(01:05:09) How to become an independent consultant

Jenny Wu, former data analyst turned sales engineer at Hex, discusses the transition into a customer-facing role, traits of successful salespeople, and embedding career and personal goals for happiness. Topics include sales engineer vs data analyst, day-to-day tasks of a sales engineer, and advice on career breaks.

Barry McCardel, Founder and CEO of Hex, talks about the future of data science teams, integrating AI into data science workflows, building data apps for stakeholders, and the potential of conversational user experiences in data work.

Sid Sharan, Senior Data and Applied Scientist at Microsoft, discusses evaluating AI products, OpenAI API for sentiment analysis, data science team culture, collaboration between data scientists and product managers, dealing with burnout, and the role of product data scientists in the age of generative AI tools.

Jess Ramos, Senior Data Analyst at Crunchbase, discusses topics such as job hopping, SQL in the real world, data analyst vs data scientist, salary negotiation, and the future of data analytics. She shares her experience with job hopping, her day-to-day work as a data analyst, and how to talk data to stakeholders. Jess also discusses the importance of salary negotiation and the impact of LinkedIn on her career. The podcast concludes with a discussion about the future of data analytics and the speaker's online presence and courses.

Mehdi Noori, applied science manager at the Generative AI Innovation Center at Amazon, discusses his transition from civil engineering to data science, working on sports vertical projects at Amazon, the transferability of skills across industries, building trust and effective communication, and the role of luck in success. He also shares his experience getting into college in Iran, the power of quitting and moving on, and his work on an application called Margarabe, a conversational date planner. Lastly, he talks about his future career plans in sustainability and AI.