Who should use the Develop machine learning models workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Development
Practical execution plan for develop machine learning models with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A deployable model artifact with comprehensive documentation
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A deployable model artifact with comprehensive documentation
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Activeloop Deep Lake to a clear problem statement and a raw dataset ready for exploration. Then, you pass the output to scikit-learn to a clean, transformed dataset with engineered features ready for modeling. Then, you pass the output to scikit-learn to a baseline metric that defines the minimum acceptable performance. Then, you pass the output to TensorFlow Hub to a set of trained candidate models with tuned hyperparameters. Then, you pass the output to scikit-learn to a single selected model with documented performance on validation data. Then, you pass the output to scikit-learn to an unbiased performance estimate confirming model readiness for deployment. Finally, MLEM is used to a deployable model artifact with comprehensive documentation.
Define Problem and Collect Data
A clear problem statement and a raw dataset ready for exploration
Exploratory Data Analysis and Preprocessing
A clean, transformed dataset with engineered features ready for modeling
Split Data and Establish Baseline
A baseline metric that defines the minimum acceptable performance
Model Selection and Training
A set of trained candidate models with tuned hyperparameters
Model Evaluation and Selection
A single selected model with documented performance on validation data
Test Set Final Validation
An unbiased performance estimate confirming model readiness for deployment
Model Packaging and Documentation
A deployable model artifact with comprehensive documentation
Start by clearly defining the business problem and the target variable. Then gather relevant raw data from internal databases, APIs, or public datasets, ensuring you have enough volume and variety for training.
Why Activeloop Deep Lake: Activeloop Deep Lake is designed for storing and versioning multimodal AI data, directly addressing the data collection and storage needs with cloud storage integration.
Explore the data to understand distributions, correlations, and anomalies. Then clean and transform the data—handle missing values, encode categorical variables, and scale numerical features—to prepare it for modeling.
Why scikit-learn: scikit-learn provides essential tools for data preprocessing, feature selection, and basic transformations, directly supporting EDA and preprocessing needs.
Split the dataset into training, validation, and test sets (e.g., 70/15/15) to prevent data leakage. Then train a simple baseline model (e.g., mean prediction or logistic regression) to set a minimum performance benchmark.
Why scikit-learn: scikit-learn provides train_test_split and baseline model implementations (e.g., DummyClassifier) directly needed for splitting data and establishing baselines.
Select 2-4 candidate algorithms (e.g., random forest, gradient boosting, neural network) based on problem type and data size. Train each on the training set, using cross-validation to tune hyperparameters and avoid overfitting.
Why TensorFlow Hub: TensorFlow Hub allows discovering and fine-tuning pre-trained models, which is a core part of model selection and training, especially for deep learning.
Evaluate each trained model on the held-out validation set using multiple metrics (e.g., precision, recall, AUC, MAE). Compare against the baseline and select the best-performing model based on business criteria (e.g., highest F1 or lowest cost).
Why scikit-learn: scikit-learn offers comprehensive evaluation metrics (accuracy, precision, recall, F1, confusion matrix) and cross-validation tools needed for model evaluation.
Run the selected model on the unseen test set to obtain an unbiased estimate of its real-world performance. This step confirms the model generalizes well and is not overfitted to the validation data.
Why scikit-learn: scikit-learn provides the necessary tools to apply the final model to a held-out test set and compute performance metrics for validation.
Package the final model (e.g., as a pickle file, ONNX, or TensorFlow SavedModel) along with the preprocessing pipeline. Write a model card detailing inputs, outputs, performance, and limitations for stakeholders and deployment teams.
Why MLEM: MLEM is specifically designed for model packaging, versioning, and saving, directly matching the needs of this step.
§ Before you start
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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