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„Turing College“ kanalas nagrinėja, kaip duomenų analizės ir dirbtinio intelekto įgūdžiai įgyjami, pritaikomi darbe ir tampa karjeros dalimi. Viena turinio kryptis skirta besimokantiems bei profesiją…
„Turing College“ kanalas nagrinėja, kaip duomenų analizės ir dirbtinio intelekto įgūdžiai įgyjami, pritaikomi darbe ir tampa karjeros dalimi. Viena turinio kryptis skirta besimokantiems bei profesiją keičiantiems žmonėms, kita – vadovams, svarstantiems, kaip ugdyti savo komandą. Įrankių apžvalgos čia pateikiamos greta organizacijų patirčių ir klausimų apie mokymosi naudą. Dėmesys sutelktas į praktinį santykį su technologijomis: ką verta suprasti pačiam, ko mokyti darbuotojus ir kaip įgūdžius parodyti ieškant darbo.
Turinys aktualus svarstantiems karjeros pokytį, norintiems sustiprinti duomenų analizės pagrindą ar suprasti, kaip DI keičia kasdienes užduotis. Vadovams įdomūs gali būti komandos mokymosi ir organizacinių pokyčių aptarimai, o darbo ieškantiems – įrašai apie kandidatavimą į su DI susijusias pozicijas. Žiūrovas gali palyginti skirtingus mokymosi motyvus ir pamatyti, kaip įgūdžių ugdymas siejamas su konkrečia veikla. Kanale kalbama ir apie Vokietijos mokymosi finansavimo priemonę, todėl dalis turinio skirta labai konkretiems praktiniams klausimams. Bendras atspirties taškas išlieka mokymasis, kurį galima susieti su darbu ir tolesniu profesiniu keliu.
Pokalbiuose su įmonių atstovais keliami klausimai, ar vadovui būtina suprasti DI įrankius ir kodėl organizacija renkasi mokyti esamus darbuotojus. Kitame įraše pristatomas kelias į duomenų analitiką neturint programavimo patirties. Šalia tokių istorijų atsiranda praktinių bandymų, pavyzdžiui, savos užrašų ir darbo organizavimo sistemos kūrimas pagal „Notion“ pavyzdį. Formatai įvairuoja nuo pokalbių bei patirties pristatymų iki įrankių palyginimų ir nuoseklių paaiškinimų. Jie leidžia tą pačią technologijų temą matyti iš besimokančio žmogaus, komandos vadovo ar sprendimą išbandančio kūrėjo perspektyvos.
Naujausias epizodas
Inside NASDAQ’s Multimillion-Dollar AI Overhaul | Engineering VP
What does it really take to roll out an AI overhaul in a high-stakes, heavily regulated industry? When a single engineering error could end up on the global evening news, "vibe coding" just doesn't…
What does it really take to roll out an AI overhaul in a high-stakes, heavily regulated industry? When a single engineering error could end up on the global evening news, "vibe coding" just doesn't cut it. In this episode, we sit down with Aurimas Danaitis, Vice President of Software Engineering at NASDAQ, to get the real story behind their massive tech transformation.
Aurimas breaks down how his teams are building autonomous agentic workflows that handle everything from Jira tickets to complex software testing, empowering hundreds of engineers to scale their output. We also dig into the hidden costs of scaling GenAI, why identifying internal "AI Champions" was their ultimate cheat code for company-wide adoption, and how they securely manage Model Context Protocols (MCPs). If you want to know how enterprise heavyweights are actually making AI work right now, this is a must-watch.
🚀 Thinking about making a career switch into the world of tech and AI?
Turing College’s Software & AI Engineering program is built for exactly that — taking you from your current background all the way to a job-ready skill set, with mentorship and flexible learning designed around your life:
https://www.turingcollege.com/software-and-ai-engineering?utm_source=youtube&utm_medium=video&utm_campaign=nasdaq_podcast
📚 Helpful resources
FREE AI Engineering Guidebook – principles & path to becoming an AI engineer:
https://www.turingcollege.com/playbooks/ai-engineering-guidebook?utm_source=youtube&utm_medium=video&utm_campaign=nasdaq_podcast
Turing College Blog – deep dives on AI tools, agents, and career development:
https://www.turingcollege.com/blog?utm_source=youtube&utm_medium=video&utm_campaign=nasdaq_podcast
👉 Subscribe for more honest conversations with learners and industry professionals on AI, data, and what it really takes to build a career in tech!
⏳ Timestamps
00:00 – Overcoming AI Resistance & The Adoption Curve
00:54 – Meet Aurimas Danaitis: VP of Software Engineering
01:54 – Inside NASDAQ: Beyond Market Trading
04:55 – Building the Vilnius Tech Hub
06:27 – How AI Impacts Fintech Productivity
10:08 – Enterprise AI vs. SaaS Startups
12:20 – Upskilling Software Engineers in AI
14:41 – Building a Proprietary GenAI Platform
15:28 – Using AI Champions to Drive Adoption
16:49 – Scaling AI: Autonomous Agentic Workflows
22:25 – Managing GenAI Costs & Token Limits
26:03 – Model Context Protocol (MCP) in Finance
27:26 – Hosting Internal Corporate AI Hackathons
#nasdaq #AIcareers #Upskilling #DigitalTransformation #FutureOfWork #Leadership #TuringCollege #aiindustry
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