· Machine Learning & AI Frameworks
Machine Learning & AI Frameworks

Machine Learning & AI Frameworks

Machine learning and AI frameworks enable model development, training, and deployment across the ML lifecycle, from research to production. PyTorch dominates deep learning with >35% prevalence in Machine Learning Engineering positions, favored for research flexibility and dynamic computation graphs. TensorFlow maintains strong presence (>30% in ML roles) with production-ready deployment capabilities. Classical ML library scikit-learn appears in >10% of Machine Learning Engineering and Data Science roles, providing accessible algorithms for traditional ML tasks. Gradient boosting frameworks like xgboost serve structured data problems. The MLOps landscape centers on MLflow (>10% in MLOps) for experiment tracking and model registry, while Kubeflow enables ML on Kubernetes and SageMaker provides managed AWS ML infrastructure. Entry-level accessibility is strongest for TensorFlow and PyTorch (>45% and >40% respectively in entry-level ML roles), scikit-learn (>15%), and basic MLOps tools. The ecosystem reflects specialization: PyTorch for research and computer vision, TensorFlow for production deployment, scikit-learn for classical ML, and MLOps tools for operationalizing models. These frameworks are fundamental to ML engineering, data science, and AI application development careers.

Deep Learning Frameworks

Neural network frameworks for training and deploying deep learning models. PyTorch leads in research and modern development, TensorFlow serves production deployments, and Keras provides high-level API. These frameworks are essential for ML engineering and deep learning specializations with strong entry-level opportunities.

PyTorch

Very High Demand
Rank: #1
Entry-Level: High
Leading deep learning framework in Machine Learning Engineering (>35%), MLOps (>25%), Data Science (>10%), and LLM/AI Application Development (>5%). Strong entry-level demand with >40% in ML engineering roles. Dynamic computation graphs. Used for deep neural network research, computer vision models, natural language processing, developing transformer models, research-to-production workflows, GPU-accelerated training, and building custom architectures with Pythonic flexibility.

TensorFlow

Very High Demand
Rank: #2
Entry-Level: High
Google's ML framework in Machine Learning Engineering (>30%), Data Science (>10%), MLOps (>15%), and LLM/AI Application Development (>5%). Strong entry-level presence with >45% in ML roles. Production-ready ML platform. Used for training deep learning models, TensorFlow Serving for deployment, TensorFlow Lite for mobile/edge, scalable distributed training, production ML pipelines, pre-trained models via TF Hub, and enterprise ML applications requiring robust deployment.

Keras

Low Demand
Rank: #3
Entry-Level: Low
High-level neural network API in Machine Learning Engineering (>5%), Data Science (>5%), integrated with TensorFlow. Lower explicit demand. User-friendly deep learning. Used for rapid prototyping neural networks, beginner-friendly deep learning, building standard architectures quickly, experimenting with models, educational purposes, and abstracting complexity of lower-level frameworks with intuitive API.

Classical Machine Learning Libraries

Traditional ML libraries for supervised and unsupervised learning on structured data. Scikit-learn dominates general-purpose ML, while xgboost and LightGBM specialize in gradient boosting for tabular data. These tools are essential for data science and ML engineering with moderate entry-level accessibility.

scikit-learn

High Demand
Rank: #1
Entry-Level: Moderate
General-purpose ML library in Machine Learning Engineering (>10%), Data Science (>15%), MLOps (>5%), and LLM/AI Application Development. Moderate entry-level demand with >15% in data science roles. Comprehensive classical ML. Used for classification and regression, clustering and dimensionality reduction, feature engineering and preprocessing, model selection and evaluation, traditional ML algorithms (SVM, random forests, etc.), and accessible machine learning on structured data.

xgboost

Low Demand
Rank: #2
Entry-Level: Low
Gradient boosting library in Machine Learning Engineering (>5%), Data Science (>5%), and MLOps. Lower entry-level accessibility. High-performance boosting. Used for structured/tabular data prediction, Kaggle competitions and data science challenges, feature importance analysis, handling missing values, classification and regression on structured data, and achieving state-of-art results on tabular datasets.

LightGBM

Low Demand
Rank: #3
Entry-Level: Low
Microsoft's gradient boosting framework with limited presence in ML and data science roles (<5% prevalence). Minimal entry-level demand. Fast tree-based learning. Used for large-scale tabular data, faster training than xgboost, handling categorical features efficiently, distributed and GPU learning, memory-efficient gradient boosting, and applications requiring speed with large datasets.

MLOps & Model Management Platforms

Tools for operationalizing machine learning through experiment tracking, model versioning, deployment, and monitoring. MLflow leads open-source MLOps, Kubeflow enables ML on Kubernetes, SageMaker provides AWS-managed ML, and Weights & Biases serves experiment tracking. These tools bridge development and production with moderate to low entry-level accessibility.

MLflow

Moderate Demand
Rank: #1
Entry-Level: Low
Open-source MLOps platform in MLOps (>10%), Machine Learning Engineering (>5%), and ML lifecycle management. Lower entry-level accessibility. End-to-end ML lifecycle. Used for experiment tracking and logging, model registry and versioning, model deployment and serving, comparing ML runs, packaging ML code for reproduction, tracking hyperparameters and metrics, and managing ML models from training to production.

Kubeflow

Low Demand
Rank: #2
Entry-Level: Low
Kubernetes-native ML platform in MLOps (>5%) and cloud-native ML contexts. Limited entry-level opportunities. ML on Kubernetes. Used for ML workflows on Kubernetes, distributed training, hyperparameter tuning with Katib, model serving, ML pipeline orchestration, notebook servers, and organizations running ML workloads in Kubernetes clusters.

Weights & Biases

Low Demand
Rank: #3
Entry-Level: Low
ML experiment tracking platform with limited explicit presence (<5% prevalence). Modern MLOps tool. Minimal entry-level demand. Used for experiment tracking and visualization, hyperparameter optimization, model versioning, collaborative ML development, comparing model runs, monitoring training in real-time, and teams seeking better experiment management than spreadsheets.

SageMaker

Low Demand
Rank: #4
Entry-Level: Low
AWS managed ML service in MLOps (>5%), Machine Learning Engineering, and AWS ML contexts. Lower entry-level accessibility. Fully managed ML platform. Used for training models at scale on AWS, model hosting and deployment, managed Jupyter notebooks, built-in algorithms, distributed training, hyperparameter tuning, and end-to-end ML workflows without infrastructure management on AWS.