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Data Science & AI",{"name":109,"slug":110,"logo_url":10},"FLEX Capital","flex-capital","https:\u002F\u002Fassets.cdn.personio.de\u002Flogos\u002F77602\u002Fsocial\u002F6a3fd82a26d4558dba45c8b2f6257965.png",[113],"Berlin",[19],"onsite",0,[67],{"sql":30,"cloud":30,"python":29,"pytorch":41,"langchain":41,"tensorflow":41,"hugging face":41,"scikit-learn":41},[36,120,121,122,123,124,37,125],"PyTorch","TensorFlow","scikit-learn","LangChain","Hugging Face","Cloud",{"llm":29,"chatbots":41,"ai agents":41,"automation":41,"evaluation":41,"prototyping":41,"rag systems":41,"data science":29,"kpi deep-dives":41,"model training":41,"machine learning":29,"exploratory analysis":41},[43,128,76,129,130,131,132,133,134,135,136,137],"LLM","RAG Systems","Chatbots","AI Agents","Automation","Prototyping","Model Training","Evaluation","Exploratory Analysis","KPI Deep-Dives",{"id":139,"slug":140,"title":141,"raw_title":10,"is_featured":11,"company":142,"raw_hiringOrganization_logo_url":111,"processed_city_gmaps":143,"processed_country_iso_code_gmaps":144,"processed_home_office":20,"processed_salary_min":145,"processed_salary_max":146,"processed_salary_currency":23,"processed_salary_source":24,"processed_working_hours":147,"processed_employment_types":27,"processed_it_skills":148,"processed_it_skills_labels":149,"processed_job_expertise_skills":151,"processed_job_expertise_skills_labels":152,"max_cpc":52,"actual_cpc":52},56261,"junior-ai-engineer-wmd-flex-capital","Junior AI Engineer (w\u002Fm\u002Fd)",{"name":109,"slug":110,"logo_url":10},[113],[19],60000,80000,[26],{"aws":41,"sql":41,"azure":41,"python":29,"pytorch":41,"langchain":41,"tensorflow":41,"hugging face":41,"scikit-learn":41},[36,120,121,122,123,124,37,33,150],"AWS",{"agentic ai":41,"etl processes":41,"data pipelines":41,"data engineering":41,"machine learning":29,"llm-based solutions":29,"exploratory analysis":41},[43,153,154,46,155,156,157],"LLM-based solutions","Agentic AI","Data Pipelines","ETL processes","Exploratory analysis",{"id":159,"slug":160,"title":161,"raw_title":10,"is_featured":11,"company":162,"raw_hiringOrganization_logo_url":165,"processed_city_gmaps":166,"processed_country_iso_code_gmaps":168,"processed_home_office":20,"processed_salary_min":21,"processed_salary_max":22,"processed_salary_currency":23,"processed_salary_source":24,"processed_working_hours":169,"processed_employment_types":27,"processed_it_skills":170,"processed_it_skills_labels":171,"processed_job_expertise_skills":172,"processed_job_expertise_skills_labels":173,"max_cpc":52,"actual_cpc":52},45094,"senior-asset-modelling-forecasting-specialist-mfd-auxmoney-gmbh","Senior Asset Modelling & Forecasting Specialist (m\u002Ff\u002Fd)",{"name":163,"slug":164,"logo_url":10},"auxmoney GmbH","auxmoney-gmbh","https:\u002F\u002Fassets.cdn.personio.de\u002Flogos\u002F15362\u002Fsocial\u002F521160601d77c41d8e1c8f9797321507.jpg",[167],"Düsseldorf",[19],[26],{"sql":29,"python":29},[36,37],{"forecasting":29,"econometrics":29,"ifrs 9 \u002F ecl":41,"documentation":41,"stress testing":29,"machine learning":41,"attribution analysis":29,"statistical modelling":29,"data quality management":41,"credit portfolio modelling":29},[174,175,176,177,178,179,180,43,181,182],"Credit Portfolio Modelling","Forecasting","Attribution Analysis","Stress Testing","IFRS 9 \u002F ECL","Statistical Modelling","Econometrics","Data Quality Management","Documentation",{"id":184,"slug":185,"title":186,"raw_title":10,"is_featured":11,"company":187,"raw_hiringOrganization_logo_url":10,"processed_city_gmaps":191,"processed_country_iso_code_gmaps":193,"processed_home_office":20,"processed_salary_min":145,"processed_salary_max":194,"processed_salary_currency":23,"processed_salary_source":24,"processed_working_hours":195,"processed_employment_types":27,"processed_it_skills":196,"processed_it_skills_labels":197,"processed_job_expertise_skills":200,"processed_job_expertise_skills_labels":201,"max_cpc":52,"actual_cpc":52},44638,"consultant-mwd-data-science-rv-versicherung","Consultant (m\u002Fw\u002Fd) Data Science",{"name":188,"slug":189,"logo_url":190},"R+V Versicherung","rv-versicherung","https:\u002F\u002Fupload.wikimedia.org\u002Fwikipedia\u002Fcommons\u002Fthumb\u002Fb\u002Fb6\u002FR%2BV-Logo.svg\u002F330px-R%2BV-Logo.svg.png",[192],"Wiesbaden",[19],90000,[26],{"gitlab":41,"python":29,"azure cloud":41},[36,198,199],"GitLab","Azure Cloud",{"agent systems":41,"generative ai":29,"machine learning":29,"reinsurance knowledge":41,"agile project management (safe)":41},[202,43,203,204,205],"Generative AI","Agent Systems","Agile Project Management (SAFe)","Reinsurance Knowledge",{"id":207,"slug":208,"title":209,"raw_title":10,"is_featured":11,"company":210,"raw_hiringOrganization_logo_url":213,"processed_city_gmaps":214,"processed_country_iso_code_gmaps":215,"processed_home_office":20,"processed_salary_min":146,"processed_salary_max":216,"processed_salary_currency":23,"processed_salary_source":24,"processed_working_hours":217,"processed_employment_types":27,"processed_it_skills":218,"processed_it_skills_labels":219,"processed_job_expertise_skills":229,"processed_job_expertise_skills_labels":230,"max_cpc":52,"actual_cpc":52},43403,"senior-ai-engineer-architecture-platform-mwd-ottonova-holding-ag","Senior AI Engineer – Architecture & Platform (m\u002Fw\u002Fd)",{"name":211,"slug":211,"logo_url":212},"ottonova","https:\u002F\u002Fassets.ottonova.de\u002Froot\u002FPM_NotarVVaG_bKV.pdf","https:\u002F\u002Fassets.cdn.personio.de\u002Flogos\u002F1421\u002Fsocial\u002F507f7618a1905858db31eba288a98483.png",[62],[19],120000,[26],{"aws":29,"eks":41,"ci\u002Fcd":41,"docker":41,"lambda":41,"mlflow":41,"python":29,"bedrock":41,"kubeflow":41,"sagemaker":41,"terraform":41,"kubernetes":41,"serverless":41},[36,150,220,221,34,222,38,223,224,225,226,227,228],"Kubernetes","Docker","MLflow","Serverless","SageMaker","Bedrock","Lambda","EKS","Kubeflow",{"llms":29,"mlops":29,"embeddings":29,"generative ai":29,"machine learning":29,"vector databases":29,"agentic workflows":29,"technology scouting":41,"platform engineering":41,"solution architecture":29,"retrieval-augmented generation":29},[43,202,231,232,233,234,235,236,237,238,239],"LLMs","Retrieval-Augmented Generation","Agentic Workflows","Vector Databases","Embeddings","MLOps","Solution Architecture","Platform Engineering","Technology Scouting",{"id":241,"slug":242,"title":243,"raw_title":10,"is_featured":11,"company":244,"raw_hiringOrganization_logo_url":10,"processed_city_gmaps":248,"processed_country_iso_code_gmaps":249,"processed_home_office":20,"processed_salary_min":21,"processed_salary_max":250,"processed_salary_currency":23,"processed_salary_source":24,"processed_working_hours":251,"processed_employment_types":27,"processed_it_skills":252,"processed_it_skills_labels":253,"processed_job_expertise_skills":256,"processed_job_expertise_skills_labels":257,"max_cpc":52,"actual_cpc":52},39455,"model-risk-manager-non-financial-risk-analytics-wmd-ing-bank-nv","Model Risk Manager Non-Financial Risk \u002F Analytics (w\u002Fm\u002Fd)",{"name":245,"slug":246,"logo_url":247},"ING Bank N.V.","ing-bank-nv","https:\u002F\u002Fwww.ing.de\u002Fbinaries\u002Fcontent\u002Fgallery\u002Fing-images\u002Fuber-uns\u002Fpresse\u002Fpressebilder\u002Fing_logo-72dpi_rgb.jpg",[90],[19],100000,[26],{"r":41,"sas":41,"python":41},[36,254,255],"R","SAS",{"genai":30,"analytics":41,"data science":41,"risk assessment":29,"machine learning":41,"model governance":41,"model validation":29,"policy development":41,"statistical models":41,"regulatory requirements":41},[258,259,76,260,43,261,262,263,264,265],"Model Validation","Analytics","Statistical Models","GenAI","Model Governance","Regulatory Requirements","Risk Assessment","Policy Development",{"id":267,"slug":268,"title":269,"raw_title":10,"is_featured":11,"company":270,"raw_hiringOrganization_logo_url":10,"processed_city_gmaps":271,"processed_country_iso_code_gmaps":273,"processed_home_office":20,"processed_salary_min":274,"processed_salary_max":275,"processed_salary_currency":23,"processed_salary_source":24,"processed_working_hours":276,"processed_employment_types":68,"processed_it_skills":277,"processed_it_skills_labels":278,"processed_job_expertise_skills":283,"processed_job_expertise_skills_labels":284,"max_cpc":52,"actual_cpc":306},9353,"praktikant-fuer-das-pricing-aktuariat-mwd-allianz-insurance","Praktikant für das Pricing Aktuariat (m\u002Fw\u002Fd)",{"name":13,"slug":14,"logo_url":15},[272],"Unterföhring",[19],36000,45000,[26],{"ms office":41,"data mining tools":41,"data analysis tools":41,"machine learning tools":41},[279,280,281,282],"MS Office","Data Analysis Tools","Machine Learning Tools","Data Mining Tools",{"reporting":41,"statistics":29,"backtesting":41,"data mining":29,"risk models":29,"lapse models":29,"project work":41,"underwriting":41,"data analysis":29,"data modeling":29,"pricing tools":41,"data collection":41,"impact analysis":41,"data preparation":41,"machine learning":29,"technical pricing":29,"actuarial analysis":29,"product development":41,"process optimization":41,"actuarial methodology":29,"financial mathematics":41,"insurance mathematics":41,"strategic initiatives":41,"stochastic forecasting models":29},[285,286,287,288,101,289,43,100,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305],"Stochastic Forecasting Models","Risk Models","Lapse Models","Technical Pricing","Data Modeling","Data Mining","Actuarial Methodology","Data Collection","Process Optimization","Project Work","Strategic Initiatives","Product Development","Impact Analysis","Pricing Tools","Underwriting","Actuarial Analysis","Reporting","Data Preparation","Backtesting","Financial Mathematics","Insurance Mathematics",0.8,{"image_path":308,"content_de":309,"content_en":322,"composite_key":335,"last_updated":336},"\u002Fimages\u002Fexpertise\u002Fmachine-learning.webp",{"tab1":310,"tab2":313,"tab3":316,"tab4":319},{"title":311,"content":312},"Bedeutung & Relevanz","Machine Learning hat in **Deutschland** eine enorme strategische Bedeutung für die Finanzbranche entwickelt. Als führende Volkswirtschaft Europas setzt [Deutschland](\u002Fsearch) zunehmend auf datengetriebene Entscheidungsprozesse, um wettbewerbsfähig zu bleiben. Im Bereich [Asset Management](\u002Fjobs-by-industry\u002Fasset-management) ermöglicht Machine Learning die Entwicklung fortschrittlicher quantitativer Investmentstrategien und die Optimierung von Portfoliomanagement-Prozessen.\n\nDie Versicherungsbranche nutzt Machine Learning für präzisere Risikobewertungen, Betrugserkennung und personalisierte Tarifgestaltung. Besonders in Finanzzentren wie [Frankfurt am Main](\u002Fjobs-in-frankfurt-am-main) treibt Machine Learning die digitale Transformation voran und schafft neue Geschäftsmodelle. Die Fähigkeit, große Datenmengen zu analysieren und Muster zu erkennen, macht Machine Learning zu einem entscheidenden Wettbewerbsfaktor für deutsche Finanzinstitute im globalen Markt.",{"title":314,"content":315},"Top-Branchen & Standorte","In **Deutschland** konzentriert sich die Nachfrage nach Machine Learning-Expertise besonders auf Schlüsselstandorte und Kernbranchen. [Frankfurt am Main](\u002Fjobs-in-frankfurt-am-main) als Finanzzentrum sowie [München](\u002Fjobs-in-muenchen) und [Düsseldorf](\u002Fjobs-in-duesseldorf) als wichtige Wirtschaftsstandorte bieten zahlreiche Möglichkeiten für Machine Learning-Spezialisten.\n\nDie [Asset Management](\u002Fjobs-by-industry\u002Fasset-management)-Branche nutzt Machine Learning intensiv für Portfolio-Optimierung und Risikomanagement. Ebenso setzt die Versicherungsbranche Machine Learning für präzise Risikomodellierung und Prozessautomatisierung ein. Diese Standorte bieten nicht nur etablierte Finanzinstitute, sondern auch innovative FinTech-Unternehmen, die Machine Learning für disruptive Geschäftsmodelle einsetzen.",{"title":317,"content":318},"Gefragte Skills & Arbeitgeber","Führende Unternehmen wie Allianz Insurance und ARAG SE suchen in **Deutschland** nach Machine Learning-Experten mit spezifischen Fachkenntnissen. Gefragt sind insbesondere Expertise in [Quantitative Finance](\u002Fjobs-by-expertise\u002Fquantitative-finance), [Risk Management](\u002Fjobs-by-expertise\u002Frisk-management) und [Financial Analysis](\u002Fjobs-by-expertise\u002Ffinancial-analysis).\n\nWeitere relevante Fachgebiete umfassen [Time Series Analysis](\u002Fjobs-by-expertise\u002Ftime-series-analysis), [Financial Modeling](\u002Fjobs-by-expertise\u002Ffinancial-modeling) und [Process Improvement](\u002Fjobs-by-expertise\u002Fprocess-improvement). Die Kombination aus mathematischer Modellierung und wirtschaftlichem Verständnis ist für Arbeitgeber besonders wertvoll. Deutsche Finanzinstitute integrieren Machine Learning zunehmend in ihre Kernprozesse, von der Risikobewertung bis zur Entwicklung innovativer Finanzprodukte.",{"title":320,"content":321},"Karriere & Entwicklung","Karrierewege für Machine Learning-Experten in **Deutschland** sind vielfältig und reichen vom Data Scientist im [Asset Management](\u002Fjobs-by-industry\u002Fasset-management) bis zum Quantitativen Analysten. Essenzielle IT-Skills wie [Python](\u002Fjobs-by-skill\u002Fpython), [SQL](\u002Fjobs-by-skill\u002Fsql) und Kenntnisse in Cloud-Plattformen wie Azure bilden die technische Grundlage.\n\nPraktische Erfahrung mit [Financial Data Analysis](\u002Fjobs-by-expertise\u002Ffinancial-data-analysis) und [Model Validation](\u002Fjobs-by-expertise\u002Fmodel-validation) ist ebenso wichtig wie Verständnis für [Financial Markets](\u002Fjobs-by-expertise\u002Ffinancial-markets). Fortbildungen in [Artificial Intelligence](\u002Fjobs-by-expertise\u002Fartificial-intelligence) und [Deep Learning](\u002Fjobs-by-expertise\u002Fdeep-learning) eröffnen Aufstiegschancen. Deutsche Universitäten und spezialisierte Weiterbildungsanbieter bieten umfassende Qualifizierungsmöglichkeiten für diesen zukunftsträchtigen Karriereweg.",{"tab1":323,"tab2":326,"tab3":329,"tab4":332},{"title":324,"content":325},"Strategic Importance","Machine Learning has become a cornerstone of innovation and competitive advantage in Deutschland's financial sector, driving unprecedented efficiency and insights across various domains. In a country renowned for its engineering excellence and financial stability, ML technologies are transforming traditional banking, insurance, and asset management operations. Financial institutions leverage ML for predictive analytics, fraud detection, and automated decision-making, enhancing both customer experience and regulatory compliance. The strategic importance is amplified by Germany's strong regulatory framework, which demands robust, transparent models—making ML not just a technological advantage but a compliance necessity. Companies like [Allianz Insurance](\u002Fcompany\u002Fallianz-insurance) and [ARAG SE](\u002Fcompany\u002Farag-se) are at the forefront, embedding ML into risk assessment and customer service to maintain their global leadership. As digital transformation accelerates, proficiency in Machine Learning is increasingly vital for sustaining Germany's economic resilience and fostering innovation in [Asset Management](\u002Fjobs-by-industry\u002Fasset-management) and [Insurance](\u002Fjobs-by-industry\u002Finsurance).",{"title":327,"content":328},"Top Industries & Locations","In Deutschland, Machine Learning expertise is highly sought after in key financial hubs and industries, with [Asset Management](\u002Fjobs-by-industry\u002Fasset-management) and [Insurance](\u002Fjobs-by-industry\u002Finsurance) leading the demand. Top locations include [Düsseldorf](\u002Fjobs-in-duesseldorf), a hub for insurance and fintech innovation; [München](\u002Fjobs-in-muenchen), known for its strong financial services and technology sectors; and [Frankfurt am Main](\u002Fjobs-in-frankfurt-am-main), Germany's financial capital with a dense concentration of banks and asset managers. These cities offer abundant opportunities for ML professionals, driven by the need for advanced analytics in portfolio optimization, claims processing, and regulatory reporting. The integration of ML in these industries enhances operational efficiency, risk management, and customer personalization, making roles in these locations critical for career growth. Additionally, emerging tech scenes in other German cities are adopting ML, but these three remain the primary hotspots for high-impact roles in finance.",{"title":330,"content":331},"In-Demand Skills & Employers","Employers in Deutschland's financial sector, such as [Allianz Insurance](\u002Fcompany\u002Fallianz-insurance) and [ARAG SE](\u002Fcompany\u002Farag-se), seek Machine Learning professionals with expertise in [Statistics](\u002Fjobs-by-expertise\u002Fstatistics), [Clustering](\u002Fjobs-by-expertise\u002Fclustering), [Feature Engineering](\u002Fjobs-by-expertise\u002Ffeature-engineering), [Time Series Analysis](\u002Fjobs-by-expertise\u002Ftime-series-analysis), and [Risk Management](\u002Fjobs-by-expertise\u002Frisk-management). These skills are essential for developing models that drive [Quantitative Finance](\u002Fjobs-by-expertise\u002Fquantitative-finance) strategies, [Financial Forecasting](\u002Fjobs-by-expertise\u002Ffinancial-forecasting), and [Process Automation](\u002Fjobs-by-expertise\u002Fprocess-automation). Key areas include [Model Validation](\u002Fjobs-by-expertise\u002Fmodel-validation) and [Model Calibration](\u002Fjobs-by-expertise\u002Fmodel-calibration) to meet regulatory standards, as well as [Artificial Intelligence](\u002Fjobs-by-expertise\u002Fartificial-intelligence) applications in [Capital Market Scenarios](\u002Fjobs-by-expertise\u002Fcapital-market-scenarios) and [Anomaly Detection](\u002Fjobs-by-expertise\u002Fanomaly-detection). Professionals must also be adept in [Data Analysis](\u002Fjobs-by-expertise\u002Fdata-analysis) and [Financial Data Analysis](\u002Fjobs-by-expertise\u002Ffinancial-data-analysis) to support decision-making in [Asset Management](\u002Fjobs-by-industry\u002Fasset-management) and insurance claims processing. These competencies ensure that ML solutions are scalable, secure, and aligned with business objectives in Germany's rigorous financial environment.",{"title":333,"content":334},"Career & Development","Building a career in Machine Learning in Deutschland involves mastering both technical and domain-specific skills. Start by gaining proficiency in [Python](\u002Fjobs-by-skill\u002Fpython), [SQL](\u002Fjobs-by-skill\u002Fsql), and data engineering tools like [Databricks](\u002Fjobs-by-skill\u002Fdatabricks) and [Azure](\u002Fjobs-by-skill\u002Fazure) for cloud-based ML deployments. Complement these with expertise in [Financial Modeling](\u002Fjobs-by-expertise\u002Ffinancial-modeling), [Risk Assessment](\u002Fjobs-by-expertise\u002Frisk-assessment), and [Statistical Analysis](\u002Fjobs-by-expertise\u002Fstatistical-analysis) to apply ML effectively in finance. Career paths often begin with roles in data science or quantitative analysis, progressing to senior positions in model development or AI strategy. Continuous learning through certifications in [Generative AI](\u002Fjobs-by-skill\u002Fgenerative-ai) and hands-on projects using frameworks like TensorFlow and PySpark is crucial. Networking in hubs like [Frankfurt am Main](\u002Fjobs-in-frankfurt-am-main) and pursuing advanced degrees in fields like [Mathematics](\u002Fjobs-by-expertise\u002Fmathematics) can accelerate growth. Ultimately, combining IT skills with financial acumen ensures success in Germany's dynamic job market, where ML drives innovation in [Banking](\u002Fjobs-by-industry\u002Fbanking) and beyond.","expertise:machine-learning:de","2026-08-17T09:09:33.775614+02:00",{"total":116,"jobs":338},[],{"industry":340,"employment_type":360,"expertise_skills":366,"it_skills":416,"salary_currency":476,"processed_working_hours":478},[341,345,348,351,354,357],{"key":342,"label":343,"count":344},"insurance","Insurance",5,{"key":346,"label":347,"count":30},"private equity","Private Equity",{"key":349,"label":350,"count":52},"financial technology","Financial Technology",{"key":352,"label":353,"count":52},"banking","Banking",{"key":355,"label":356,"count":52},"asset management","Asset Management",{"key":358,"label":359,"count":52},"financial services","Financial Services",[361,364],{"key":27,"label":362,"count":363},"PERMANENT",6,{"key":68,"label":365,"count":29},"STUDENT",[367,369,371,374,376,378,380,382,384,386,388,390,392,394,396,398,400,402,404,406,408,410,412,414],{"key":368,"label":43,"slug":368,"count":4},"machine-learning",{"key":370,"label":76,"slug":370,"count":29},"data-science",{"key":372,"label":373,"slug":372,"count":30},"generative-ai","Generative Ai",{"key":375,"label":44,"slug":375,"count":30},"artificial-intelligence",{"key":377,"label":136,"slug":377,"count":30},"exploratory-analysis",{"key":379,"label":101,"slug":379,"count":30},"data-analysis",{"key":381,"label":100,"slug":381,"count":30},"statistics",{"key":383,"label":46,"slug":383,"count":30},"data-engineering",{"key":385,"label":259,"slug":385,"count":52},"analytics",{"key":387,"label":176,"slug":387,"count":52},"attribution-analysis",{"key":389,"label":132,"slug":389,"count":52},"automation",{"key":391,"label":303,"slug":391,"count":52},"backtesting",{"key":393,"label":130,"slug":393,"count":52},"chatbots",{"key":395,"label":49,"slug":395,"count":52},"cost-optimization",{"key":397,"label":174,"slug":397,"count":52},"credit-portfolio-modelling",{"key":399,"label":45,"slug":399,"count":52},"data-architecture",{"key":401,"label":292,"slug":401,"count":52},"data-collection",{"key":403,"label":290,"slug":403,"count":52},"data-mining",{"key":405,"label":289,"slug":405,"count":52},"data-modeling",{"key":407,"label":155,"slug":407,"count":52},"data-pipelines",{"key":409,"label":302,"slug":409,"count":52},"data-preparation",{"key":411,"label":181,"slug":411,"count":52},"data-quality-management",{"key":413,"label":102,"slug":413,"count":52},"data-visualization",{"key":415,"label":182,"slug":415,"count":52},"documentation",[417,420,422,425,428,431,433,435,438,441,443,446,448,450,453,455,458,460,462,464,466,469,471,474],{"key":418,"label":36,"slug":418,"count":419},"python",9,{"key":421,"label":37,"slug":421,"count":29},"sql",{"key":423,"label":424,"slug":423,"count":41},"ms-azure","MS Azure",{"key":426,"label":427,"slug":426,"count":30},"aws","Aws",{"key":429,"label":430,"slug":429,"count":30},"cicd","Ci\u002FCd",{"key":432,"label":32,"slug":432,"count":30},"databricks",{"key":434,"label":124,"slug":434,"count":30},"hugging-face",{"key":436,"label":437,"slug":436,"count":30},"langchain","Langchain",{"key":439,"label":440,"slug":439,"count":30},"pytorch","Pytorch",{"key":122,"label":442,"slug":122,"count":30},"Scikit-Learn",{"key":444,"label":445,"slug":444,"count":30},"tensorflow","Tensorflow",{"key":447,"label":34,"slug":447,"count":30},"terraform",{"key":449,"label":221,"slug":449,"count":52},"docker",{"key":451,"label":452,"slug":451,"count":52},"eks","Eks",{"key":454,"label":95,"slug":454,"count":52},"git",{"key":456,"label":457,"slug":456,"count":52},"gitlab","Gitlab",{"key":459,"label":228,"slug":459,"count":52},"kubeflow",{"key":461,"label":220,"slug":461,"count":52},"kubernetes",{"key":463,"label":226,"slug":463,"count":52},"lambda",{"key":465,"label":223,"slug":465,"count":52},"serverless",{"key":467,"label":468,"slug":467,"count":52},"large-language-models-llms","Large Language Models (Llms)",{"key":470,"label":281,"slug":470,"count":52},"machine-learning-tools",{"key":472,"label":473,"slug":472,"count":52},"ms-365","MS 365",{"key":475,"label":39,"slug":475,"count":52},"microsoft-foundry",[477],{"key":23,"label":23,"count":4},[479,481],{"key":26,"label":26,"count":480},7,{"key":67,"label":67,"count":41}]