Adventures in Machine Learning
Charles M Wood
Machine Learning is growing in leaps and bounds both in capability and adoption. Listen to our experts discuss the ideas and fundamentals needed to succeed as a Machine Learning Engineer.
Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
Categorias: Tecnología
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Today, Ben and Michael dive into a compelling discussion on the intricate dance between challenges, feedback, mentorship, and growth in the field of software development. In this episode, Michael shares their journey of overcoming the pains of independent problem-solving before receiving effective guidance. As we explore their experiences with Ben, they uncover the vital importance of openness to feedback and the profound value of peer review in refining solutions.
They delve into technical aspects, including Python's Pytest framework for unit tests and the delicate balance between complexity and simplicity in testing for maintainability and readability. Additionally, they touch on Michael's hands-on learning curve, tackling unfamiliar concepts such as RAG, embeddings, LLMs, and Git development, all while managing significant time constraints and social commitments.
Moreover, Ben shares his mentorship philosophy, likening it to military training—pushing mentees to their limits without prior warning to foster resilience and self-improvement. They also discuss the importance of documentation, bug bashes, and the fine art of balancing integration and unit tests to ensure robust and thorough software.
Join them as they explore the journey from initial struggle to increased autonomy and confidence, using real-world examples of testing gaps, code complexities, and the powerful impact of daily feedback. Whether you're a seasoned developer or just starting your tech career, this episode is packed with valuable insights to enhance your learning and development process. So, stay tuned and dive right in!
Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
Episodios anteriores
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206 - Learning, Testing, and Mentorship: Building Autonomy and Confidence in Python Development - ML 167 Thu, 26 Sep 2024
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205 - Evaluating and Building AI Systems - ML 166 Thu, 19 Sep 2024
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204 - Demystifying AI Innovations - ML 165 Thu, 12 Sep 2024
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203 - Maintaining Backward Compatibility in Software Projects: Strategies from Industry Experts - ML 164 Thu, 29 Aug 2024
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202 - Building, Testing, and Abandoning Software - ML 163 Thu, 22 Aug 2024
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201 - AI in Education: From Micro-Courses to Rigorous Training Programs - ML 162 Thu, 15 Aug 2024
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200 - Transforming Recruitment with AI: Surveys, Sentiment, and Data-Driven Insights - ML 161 Thu, 08 Aug 2024
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199 - How AI and Deep Fakes Are Transforming Security and Customer Trust - ML 160 Wed, 24 Jul 2024
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198 - AI Deployment Simplified: Kit Ops' Role in Streamlining MLOps Practices - ML 159 Thu, 18 Jul 2024
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197 - Functional Programming Shift and Scalable Architecture Insights - ML 158 Thu, 11 Jul 2024
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196 - Mentorship and Management: Creating a Collaborative Work Environment - ML 157 Thu, 04 Jul 2024
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195 - The Intersection of Success and Talent Retention in Software Development - ML 156 Thu, 27 Jun 2024
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194 - Redefining Data Science Roles: Beyond Technical Skills and Traditional Job Descriptions - ML 155 Thu, 20 Jun 2024
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193 - Balancing Theoretical Knowledge with Hands-on Experience - ML 154 Thu, 13 Jun 2024
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192 - AI in Security: Revolutionizing Defense and Outsmarting Attackers in the Digital Era - ML 153 Thu, 06 Jun 2024
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191 - The Journey to Expertise with Fernando Lopez - ML 152 Thu, 23 May 2024
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190 - Unraveling the Complexities of Model Deployment in Dynamic Marketplaces - ML 151 Thu, 09 May 2024
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189 - The Impact of AI Tools on Software Development and Quality Assurance - ML 150 Thu, 02 May 2024
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188 - Adaptive Industry ML: Challenges, Automation, and Model Applications - ML 149 Thu, 18 Apr 2024
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187 - Harnessing Open Source Contributions in Machine Learning and Quantization - ML 148 Thu, 18 Apr 2024
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186 - Data Platform Innovation: Navigating Challenges and Building a Unified Experience - ML 147 Thu, 11 Apr 2024
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185 - The Science-Engineering Blend - ML 146 Thu, 04 Apr 2024
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184 - The Impact of Process on Successful Tech Companies - ML 145 Thu, 28 Mar 2024
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183 - Delivering Scoped Solutions: Lessons in Fixing Production System Issues - ML 144 Thu, 21 Mar 2024
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182 - MLOps 101: Scoping, Latency, Data Curation, and Continuous Model Retraining - ML 143 Thu, 14 Mar 2024
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181 - Navigating Authority and Transparency in Organizations - ML 142 Thu, 22 Feb 2024
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180 - Evolution of Dlib: Addressing Challenges in Machine Learning and Computer Vision - ML 141 Thu, 08 Feb 2024
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179 - Strategies for Improving Code Quality and Maintenance in the Python Environment - ML 140 Thu, 25 Jan 2024
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178 - Lyft's ML Infrastructure Journey - ML 139 Thu, 18 Jan 2024
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177 - From Open Source to Traditional ML with James Lamb - ML 138 Thu, 04 Jan 2024
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176 - Wars of AI and Justice: Handling Uncertainties and Ethical Quandaries - ML 137 Thu, 21 Dec 2023
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175 - Beyond Machine Learning - ML 136 Thu, 07 Dec 2023
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174 - Unraveling AI's Impact: Computer Vision, Generative Models, and Challenges in Software Development - ML 135 Thu, 30 Nov 2023
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173 - Complexity Theory - ML 134 Thu, 23 Nov 2023
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172 - How To Recession Proof Your Job - BONUS Thu, 23 Nov 2023
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171 - Data Watchdogs - ML 133 Thu, 16 Nov 2023
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170 - Causal Analysis - ML 132 Thu, 09 Nov 2023
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169 - Data Visualization and Hugging Face - ML 131 Thu, 02 Nov 2023
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168 - Confidence as Data Scientist - ML 130 Thu, 19 Oct 2023
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167 - A Case Study: Recommendation Engines - ML 129 Thu, 05 Oct 2023
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166 - Maximizing Efficiency in ML Project Development - ML 128 Thu, 21 Sep 2023
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165 - AI that Make You Better - ML 127 Thu, 14 Sep 2023
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164 - Challenges for LLM Implementation - ML 126 Thu, 07 Sep 2023
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163 - ML in the Cannabis Industry - ML 125 Thu, 24 Aug 2023
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162 - How AI Impacts Society - ML 124 Thu, 17 Aug 2023
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161 - LLMs on Azure - ML 123 Thu, 03 Aug 2023
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160 - How to Create Team Utils - ML 122 Fri, 21 Jul 2023
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159 - How to Get Sh*t Done - ML 121 Thu, 13 Jul 2023
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158 - ML at Netflix and How to Learn Deeply - ML 120 Fri, 30 Jun 2023
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157 - How to get Promoted - ML 119 Fri, 23 Jun 2023