Sure, can you please check if ollama is running on your machine or not?
@saboo_shubham_
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Anthropic Model to Surpass GPT-4o Claims Analyst
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GPT-4o is not gonna replace humans. On the other hand, humans are gonna replace gpt-4o with a far better model coming from @AnthropicAI
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Stay Current with AI Trends and Continuous Learning
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Tip 7: Keep Up with Industry Trends Stay updated with the latest trends and advancements in AI. Mention any relevant courses or technologies you have learned and always keep that tab up-to date. This shows your dedication to continuous learning and staying current in the field.
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Showcase Proficiency Recent AI Technologies LLMs Generative
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Tip 6: Focus on Recent Technologies Mention your proficiency with LLMs, reinforcement learning, or other generative AI technologies. Highlight any recent work or projects involving these technologies.
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Showcase Open Source AI Contributions on Your Resume
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Tip 5: Include Open Source Contributions If you’ve contributed to open-source AI projects, list these contributions. Mention any significant pull requests, issues resolved, or your role in major projects. This demonstrates your commitment and expertise.
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Production Deployment Skills for ML Engineers
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Tip 4: Emphasize Deployment Experience Highlight your experience with deploying models into production environments using tools like Docker, Kubernetes, or cloud platforms such as AWS, GCP, and Azure. Include specific examples and the impact they had.
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Mastering Cross-Functional Collaboration in AI Solutions
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Tip 3: Highlight Collaboration with Cross-Functional Teams Showcase your ability to work with data engineers, product managers, and other stakeholders. Mention specific instances where you collaborated to deliver impactful AI solutions.
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Quantify AI Contributions with Concrete Metrics and Impact
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Tip 2: Quantify Your Contributions with Real-World Impact Use concrete metrics to quantify your achievements, such as 'Reduced customer churn by 20% through predictive modeling' or 'Increased sales by 15% with a recommendation system'. Real-world impact is more compelling.
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End-to-End AI Project Lifecycle: From Concept to Deployment
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Tip 1: Showcase End-to-End Project Work Describe projects where you took an idea from concept to deployment. Outline the problem, data collection, model development, validation, and deployment. Demonstrate your ability to handle the entire lifecycle of an AI project.