Video Understanding: Perceptron AI and FiftyOne
Exploring video-native multimodal AI and integrating cutting-edge video understanding models into computer vision workflows with Akshat Shrivastava.
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A public record of conference keynotes, technical workshops, guest podcast appearances, and online courses by Harpreet Sahota. Everything is sorted temporally, newest first. Click any section below to expand.
Exploring video-native multimodal AI and integrating cutting-edge video understanding models into computer vision workflows with Akshat Shrivastava.
Deep dive into inspecting datasets, utilizing VLMs for dataset enrichment, and evaluating fine-tuned models under distribution shifts.
Addressing the robot learning bottleneck: why data quality and curation matter more than raw volume when collecting embodied episodes.
Walkthrough of multimodal robotics datasets, authoring MCAP containers, and visualizing state/action spaces inside FiftyOne.
Inspecting Hugging Face LeRobot dataset episodes, diagnosing sensor drift, and triaging teleoperation failures.
Demonstrating autonomous AI agents executing computer vision query and triage tasks inside FiftyOne.
Hands-on workshop covering advanced curation metrics, embedding analysis, and error diagnosis for production vision models.
Using autonomous agents to detect labeling noise, fix mislabeled bounding boxes, and curate high-quality agricultural CV datasets.
Practical strategies for data-centric AI: dataset pruning, deduplication, and hard negative mining.
Analyzing complex autonomous vehicle edge cases across multiple camera streams and sensor modalities in urban driving environments.
Using VLMs to perform temporal search, natural-language video querying, and automatic captioning.
Going beyond OCR: parsing complex tables, architectural diagrams, and unstructured layouts with vision foundation models.
Building production-grade UI and backend plugins for computer vision teams to bridge research models into interactive tools.
Building custom semantic document search plugins using vector embeddings and multimodal retrieval.
Architecture patterns for computer vision pipelines: from raw data ingestion to active learning and production monitoring.
A practical, code-level guide to instruction-tuning base open-weight language models using PEFT and QLoRA.
First-principles breakdown of residual connections, vanishing gradients, and the architectural mechanics behind deep CNNs.
Discussing the role of a Hacker-in-Residence, rapid prototyping, and removing friction between AI systems and end users.
Deep dive into object detection architectures, Neural Architecture Search (NAS), and compute-efficient edge deployment.
Navigating generative AI, career evolution in machine learning, and staying technically honest in a hype cycle.
Career journey through biostatistics and data science into developer advocacy for deep learning tooling.
Cutting through the AI noise: what generative models actually do well, what breaks, and how data engineers should adapt.
Discussing the data science mindset, mentorship, personal philosophy, and building developer trust.
Why data science is equal parts engineer, scientist, and philosopher, and how to build a lasting data flywheel.
Exploring stoic philosophy, continuous learning, and how to define personal and career success in data science.
Applying probability, combinatorial creativity, and intellectual rigor to personal decision-making.
How to build inspectable, business-relevant portfolio projects that prove technical chops without fluff.
Career transitions from mathematics and statistics into enterprise data science, mentorship, and building public proof.
Hands-on practice controlling text generation with open-weight models, tokenizers, chat templates, and advanced decoding strategies.
Comprehensive deep dive into Retrieval-Augmented Generation: document ingestion, embedding indexing, query engines, and evaluation.
Architecting LLM applications with LangChain: prompt templates, output parsers, memory management, and structured tool calling.
A comprehensive, data-centric guide to developing and maintaining high-quality computer vision datasets with FiftyOne and CVAT.
From convolutional neural networks to state-of-the-art architectures: training image classification models and optimizing workflows.
Understanding the mechanics of residual networks, skip connections, and training ResNets with SuperGradients.
Depthwise separable convolutions, inverted residuals, and deploying lightweight vision models to resource-constrained edge devices.
Understanding network design spaces, quantization trade-offs, and training RegNet models for production classification.
Developing the senior practitioner mindset: applying the scientific method, problem framing, and building high-signal portfolios.
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Direct answers regarding Harpreet Sahota's talks, guest appearances, and course curriculum.
Harpreet speaks on Developer Relations (DevRel) strategy for AI companies, physical AI and robotics dataset curation, Vision-Language Models (VLMs), production AI agent evaluation beyond LLM-as-a-judge, and building developer trust through inspectable proof.
Harpreet has appeared on leading data science and AI podcasts including SuperDataScience (Episodes 457 and 693 with Jon Krohn), Ken’s Nearest Neighbors (Episode 62 with Ken Jee), The Data Scientist Show (Episode 33 with Daliana Liu), Insights x Design (Episode 28 with Michael Green), Curious Lion, AI Stories Podcast, and Plumbers of Data Science.
Harpreet has authored courses across LinkedIn Learning (Prompt Engineering with LangChain, Hands-On AI: RAG using LlamaIndex, Generative AI with Hugging Face Models, Computer Vision for Data Scientists), Coursera with UC Davis (Hands-on Data Centric Visual AI), 365 Data Science (How to Think Like a Data Scientist to Become One), and Udemy (Computer Vision with ResNet, MobileNet, and RegNet).
Organizers can request Harpreet for keynote presentations, hands-on workshops, technical panels, and podcast interviews by reaching out through the contact form at https://employabledatascientist.com/contact/?service=developer-evangelism.
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Keynotes, conference workshops, technical panels, and podcast interviews on DevRel strategy for AI, physical AI, robotics, and evaluation.