The data required for AI Lifecycle comprises four major stages in the cycle that provide High Quality Dataset to support the development of any AI initiative. The steps are data sources and data preparation as well as model training and deployment, and evaluation of models performed by human. Data gathering, data preparation, and evaluation of models are the most difficult and time-consuming and, if not executed properly could lead to problems with quality and delays in launch. AI practitioners spend more than 20% working with data therefore they require the most effective tools and resources for this vital element that is. We are experts in these three phases, and we strategically work with service providers that are experts in training models and deployment.
Deciding on the best Machine Learning Algorithm for a business issue or use case can be a difficult and time-consuming procedure. If you decide to apply an ML model to your business case and then test whether it is the best for the business, then you're using a slow method which is lengthy and takes an enormous amount work. There are a few aspects to consider when selecting the appropriate ML algorithm that will best meet your needs and objectives for your business.
Data for AI Lifecycle
1.Data Sourcing
The AI Data Collection from our global community that includes more than one million employees allows us to offer access to ethically-sourced datasets for any purpose you might require. This is accomplished through our complete service management. We also provide solutions for data sourcing for any organization, no matter what stage they are at in AI maturation they are at. Pre-labeled data sets will speed up the speed of your AI project by giving your team with licensed ready-made data that is tailored to your requirements. Our library of more than 250 pre-labeled data sets includes images, audio, text as well as video. Additionally synthetic data can be used for the generation of data that is hard to locate to improve the training of models.
2.Data Preparation
Our leading platform and machine learning-assisted software tools permit our customers to submit their data to our global community to make annotations, judgments and labels that will create quality labeled data for the models you are developing. We also provide industry-leading knowledge graph and support for ontology services that help you create a strong, reliable knowledge graph, transforming your data into information.
3.Model Training and Deployment
Information to support AI Lifecycle is our specialization and we opt to collaborate with the best in the field of models training, and their implementation. Whether it's your own in-house team composed of data scientists and engineers or you decide to collaborate with our technology partners who are strategic to us We give your team the data needed to develop and implement AI models. Some of our partners include Microsoft Azure, Amazon SageMaker, Google Cloud, NVIDIA, Pachyderm, and PwC Japan.
4.Model Evaluation by Humans
We provide the ability to validate model performance in real-time and provide tuning for a variety of demographics and use cases. With industry benchmarks, we are able to compare the performance of your model to those of competitors to ensure that you're able to get the best results.
How to Choose the Right ML Algorithm?
1.Problem Type
It is a good method to identify the type of business issue you are facing and then choose an algorithm which will best help tackle it. You can classify the problem according to both the output and input. Based on the input, you can classify the business issue into three categories.
- Supervised Learning Problem - If it is involving labels for data
- Unsupervised learning problem In the event that unlabeled data is involved
- Reinforcement learning issue is a problem that involves maximizing an outcome by interfacing with the surrounding
On the basis of this output, you could classify the issue into three kinds:
- Regression issue if the output is the form of a number
- Classification problem when the result of the model is an element of a class
- Problem with Clustering when you output includes a collection from input groupings
2.Training Set Size
Data sets function as the raw material used in the entire analysis process . They are a major factor in the choice of the algorithm. For small training data sets low variance or high bias classifiers are most suitable. However, for larger training data sets low bias or high variance classifiers aid in creating high-quality models.
3.Accuracy
The precision required is determined by the type of application being developed. Some applications require an approximate prediction, which aids in reducing process time. Flexible models are recommended if the goal of the business issue is accuracy, and if it is a matter of inference and the model is not limiting enough, then a more restrictive one will suffice.
4.Training Time
The kind of use scenario determines the time for training that the machine learns. For certain instances, such as movie suggestions, the model has to be trained each time a user logs in. However, for things like stock predictions the model must be trained continuously. Hence it is essential to take into consideration the amount of time required to develop this model.
5.Linearity
Linear algorithm are easy and quick to learn and are therefore commonly utilized as the primary option. Most of the ML models such as Support Vector Machines Logistic Regressions, Linear Aggression and others use the principle of linearity. Even though it can help solve certain business issues, in certain instances, it could lower the accuracy.
6.Number of Parameters
Parameters influence the performance of algorithms, mainly by the amount of iterations, tolerance for error and so on. Algorithms having multiple parameters must go through a lot of trials and errors in order to determine the best configuration. Even though greater numbers of parameters will guarantee agility, precision and time to train can be affected when trying to find the best setting.
