AWS SageMaker
End-to-end ML lifecycle on AWS
0 case studies
4 specialists
6 specialists
General Infrastructure End-to-end ML lifecycle on AWS
Building, training, and deploying ML models end-to-end within the AWS ecosystem, including managed Jupyter notebooks, distributed training jobs, and auto-scaling inference endpoints. Teams use it to avoid managing GPU infrastructure directly while keeping tight integration with S3, Lambda, and other AWS services.
Sign into the AWS Console and navigate to SageMaker. Create a SageMaker domain and user profile to get a managed JupyterLab environment. You'll need an IAM role with SageMaker permissions and an S3 bucket for storing training data and model artifacts.
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AI Advisor & Investor @ Independent (ex-Amazon Head of ML)
Former Head of Machine Learning at Amazon. Now an independent AI advisor, investor, and one of the most-followed AI voices on LinkedIn with millions of followers. Named to Forbes 30 Under 30 and Inc.'s Female Founders list. Advises Fortune 500 companies on AI strategy and invests in AI startups.
AI Consultant & NLP Expert @ Opinosis Analytics
Founder of Opinosis Analytics, specializing in AI consulting for healthcare, pharma, and enterprise. Published peer-reviewed research in medical AI and collaborates with medical institutions. Author and educator on practical NLP applications. Helps organizations unlock value from unstructured text data.
AI Engineer @ Independent ()
AI engineer specializing in LLMs and machine learning with extensive experience in healthcare, finance, spatial analytics, and advertising. -vetted freelancer. Built robust clinical note generation pipelines, automated LLM evaluation testing on AWS Batch, and fine-tuned models for domain-specific tasks.
IIT-trained AI/ML Developer | LLM Agents | RAG Systems @ Independent
IIT-trained AI/ML Developer with 8+ years of experience in GenAI, LLM Agents, and RAG Systems. AWS Certified. Available for full-time freelance work (40-50 hrs/week).
Thought leaders
Follow for insights, tutorials, and thought leadership
Intelligent World
CEO of Intelligent World and one of the most prominent AI influencers globally. Expert in enterprise AI agents, logistics, manufacturing, and infrastructure applications. Advises organizations on AI strategy and digital transformation.
Pinecone
Founder and CEO of Pinecone, the leading managed vector database. Former Director of Research at AWS where he built SageMaker's algorithms. PhD in computer science with expertise in large-scale similarity search and streaming algorithms.
Hugging Face (ex-AWS)
Chief Evangelist at Hugging Face, formerly AWS's Global Technical Evangelist for AI/ML. Author of 'Learn Amazon SageMaker' (O'Reilly). Prolific creator of ML tutorials bridging cloud infrastructure with modern ML workflows.
Hugging Face
Technical Lead and Developer Advocate at Hugging Face creating widely-read tutorials on fine-tuning and deploying transformer models. Makes cutting-edge ML techniques accessible through hands-on blog posts and notebooks.
Anthropic (ex-Amazon Principal Applied Scientist)
Member of Technical Staff at Anthropic building AI-powered products. Previously Principal Applied Scientist at Amazon building real-time retrieval, bandit rankers, and AI systems for summarization, translation, and Q&A. Creator of ApplyingML.com and Applied-LLMs.org which collect practitioner knowledge on applying ML in production. Led ML/AI teams at Amazon, Alibaba, Lazada.
Metamaven (ex-Topbots)
AI strategist and former CTO of Metamaven and Topbots. Co-author of 'Applied Artificial Intelligence: A Handbook for Business Leaders.' Advises enterprises on AI adoption strategy, model selection, and building AI-first products. Bridges technical AI capabilities with business strategy.
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