Machine Learning Model Validation Services
After developing a machine learning model, it is extremely important to check the accuracy of the model predictions and validate the same to ensure the precision of results given by the model and make it usable in real life applications. Machine learning validation is important to ensure high quality and fine-tuning of the model hyperparameters to avoid deficiencies and errors in outputs.
Why Model Validation is Important?
Without checking the accuracy of model output, relying on the results can be devastating if used in sensitive fields like healthcare or medicine research. With the help of ML model validation services you can evaluate the predictions and validate the same using various techniques, out of which few ML model validation methods are mentioned below.
Model Validation Methods:
- ML Model Validation by Humans
- Holdout Set Validation Method
- Cross-Validation Method for Models
- Leave-One-Out Cross-Validation
- Random Subsampling Validation
- Teach and Test Method
- Bootstrapping ML Validation Method
- Running AI Model Simulations
- Overriding Mechanism Method
How to Validate Machine Learning Models?
People who don’t know how to validate machine learning models can take help of experts like machine learning engineers for ML validation But choosing the right validation method is more important to ensure the accuracy and biasness of the validation process to authenticate your machine learning model. You can choose between automated and manual (done by humans) model validation process depending on the types of model your are developing through machine learning or deep learning algorithms.
Automated vs Human ML Model Validation
No doubt that automated model validation methods can do this job faster but manual model validation techniques are more reliable and accurate. Under the manual approach, humans check and validate each prediction given by model and correct the same if there is any deviation. Cogito solves this challenge through its Annotation QA Services.
Benefits of Human Model Validation Services
ML or AI model validation done by humans manually has many advantages over automated model validation methods. In human backed validation process each prediction is evaluated by a dedicated team ensuring 100% quality. This can help machine learning engineers to develop more efficient models with best-in-class accuracy. Humans can do this job in an unbiased manner making your ML model reliable and acceptable in the AI world.
Unbiased ML Model Validation with Cogito
Model validation process can become cumbersome with biased automated techniques or skills and can lead to AI algorithm validation services becoming unreliable. Cogito solves this problem by offering a dedicated team of experts who use their knowledge, skills and vast annotation experience to provide unbiased AI Model Validation Services for machine learning with highest accuracy at affordable pricing.
Validate Pre-trained ML Models
Captured through CCTV image, ML models at their pre-training stage doing mistakes like annotating the two people into a single bounding box. The ML model validation services corrects such wrong annotations helping machine to learn with corrected data sets and give the right output at the same time reducing the model validation chances with increasing accuracy in further model predictions.
Analyse Overlooked Objects
Analyze the various scenarios and check the missing or ignored objects in the images correcting the same helping the ML model learn from such inaccuracies and give better outputs if the validated data is again feed into model training. The prediction model can do such mistakes if insufficient amount of training data sets used while training such machine learning supported algorithms.
Authenticating Facial Annotations
We check and study the inaccuracy minutely into your each model comparing with real human faces ensuring the highest accuracy. Any kind of inaccurately pointed annotation will be marked and corrected by our model validation team providing annotation QA services for all types of annotated objects in images or videos.