Machine Learning Model Validation Services
After developing a machine learning model, it becomes extremely crucial to check the accuracy of the model predictions and validate the same ensuring precise outcomes by the model. Consequently, it is made usable for real-life applications. Machine learning validation hence becomes a crucial step to ensure high quality and fine-tuning of the model hyper-parameters to avoid deficiencies and rectify errors in outputs.
Why Model Validation is Important?
Without checking the accuracy of model output, putting it to use and then relying on the results can be devastating, specifically if used in sensitive fields like healthcare or medical 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 the help of experts like machine learning engineers for ML validation. But choosing the right validation method is a necessity for ensuring the accuracy and biases 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 the model you 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, our workforce devotedly checks and validates each prediction given by the model and corrects the same, in case of any deviation. Therefore, Cogito is your ONE STOP SOLUTION to solve such challenges through its Annotation QA Services.
Benefits of Human Model Validation Services
ML or AI model validation done by human beings 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
The model validation process can become a cumbersome task with biased automated techniques or skills, which 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 high accuracy at affordable prices.
Validate Pre-trained ML Models
In an image captured by a CCTV, ML models at their pre-training stage make mistakes like annotating two people into a single bounding box. The ML model validation services correct such wrong annotations helping the machine to learn with corrected data sets and give the right output at the same time reducing further errors. The model validation establishes memory enabling advanced accuracy in further model predictions.
Analyse Overlooked Objects
Analysis of various scenarios and checking the missing or ignored objects in the images, while correcting the same and helping the ML model learn from such inaccuracies, ensures better outputs if the validated data is fed again into model training. The prediction model can do such mistakes if an insufficient amount of training data sets is used while training such machine learning supported algorithms.
Authenticating Facial Annotations
We check and study the inaccuracy minutely into each model, while comparing with real human faces and ensuring the highest accuracy. Any kind of inaccurately pointed annotation will be marked and corrected by our model validation team. Thus, providing annotation QA services for all types of annotated objects in images or videos becomes our ultimate goal.