AI and ML (Machine Learning) and ML with AI explained скачать в хорошем качестве

AI and ML (Machine Learning) and ML with AI explained

In this video, I explain—very simply—how to build a robust Machine Learning (ML) model using AI tools across 5 practical steps: Framing, Data Preparation, Labeling, Training, and Testing. This is not a coding-heavy video. It’s meant for business leaders, architects, developers, and anyone curious about how real ML systems are built in production. Below are the tools shown in the video, grouped by each step, with links so you can explore them yourself. 1️⃣ Framing (Problem Understanding) Use these tools to think clearly, define the problem, choose what to predict, and decide success metrics. ChatGPT – AI assistant for problem framing, feature ideas, and ML design https://chat.openai.com Claude – reasoning assistant for requirements, risks, and trade-offs https://claude.ai Gemini – Google’s AI for planning, research, and system design https://gemini.google.com Perplexity AI – research assistant to find use cases and best practices https://www.perplexity.ai 2️⃣ Data Preparation (Cleaning & Feature Engineering) Use these tools to clean messy data, fix missing values, and create useful features. PandasAI – talk to your data using natural language https://pandas-ai.com DataRobot – automated data prep + feature engineering https://www.datarobot.com H2O.ai (Driverless AI) – automated feature engineering + pipelines https://h2o.ai/products/h2o-driverless-ai/ AWS SageMaker Data Wrangler – visual data cleaning and transformations https://aws.amazon.com/sagemaker/data-wran... Google Vertex AI Feature Store – manage and reuse ML features https://cloud.google.com/vertex-ai/docs/fe... 3️⃣ Labeling (Human-in-the-loop) Use these tools to label data faster with AI help and human review. Label Studio – open-source data labeling with ML assist https://labelstud.io Scale AI – enterprise-grade labeling + workflows https://scale.com SuperAnnotate – AI-assisted annotation platform https://www.superannotate.com Snorkel AI – programmatic labeling & weak supervision https://snorkel.ai Amazon SageMaker Ground Truth – managed labeling service https://aws.amazon.com/sagemaker/groundtruth/ 4️⃣ Training (Model Building & Tuning) Use these tools to automatically train multiple models and tune performance. H2O.ai AutoML – trains many models and picks the best https://docs.h2o.ai/h2o/latest-stable/h2o-... DataRobot AutoML – enterprise AutoML for production use https://www.datarobot.com/platform/automl/ AWS SageMaker Autopilot – end-to-end AutoML on AWS https://aws.amazon.com/sagemaker/autopilot/ Google Vertex AI AutoML – AutoML on Google Cloud https://cloud.google.com/vertex-ai/docs/tr... Azure AutoML – AutoML on Microsoft Azure https://learn.microsoft.com/azure/machine-... 5️⃣ Testing (Validation, Drift, Explainability) Use these tools to test models, catch data drift, and explain predictions. Deepchecks – validate data and ML models https://deepchecks.com Arize AI – model monitoring, drift detection, explainability https://arize.com Fiddler AI – explainability and monitoring for ML https://www.fiddler.ai WhyLabs – data drift and ML observability https://whylabs.ai Robust Intelligence – stress-test ML models for failures https://www.robustintelligence.com 🎯 Key Takeaway AI tools don’t replace ML engineers or business thinking. They save time, reduce repetitive work, and help teams move faster. Humans still define the goals, risks, fairness, and responsibility. This is how AI and ML work together in real production systems—no hype, no magic, just practical engineering.