Our Machine Learning as a Service Approach
Using an enhanced understanding of customer needs and alignment of end-goals, Folio3 has built and deployed successful Machine Learning solutions. We deliver a rich user experience by encouraging regular touchpoints with customers and relevant stakeholders. Our development approach is tailored as per your unique business needs:
Submission of a hypothesis
Scope and feasibility of the project with defined Delivery of a Proof of Concept
Algorithm development with regular touchpoints
Final delivery and live deployment
Utilize Folio3 Machine Learning as a Service Offering for Max Control Over Your ML Operations
Folio3’s Machine Learning services allow clients build, test and deploy ML applications seamlessly. Utilizing pre-configured machine learning algorithms and data-handling modules, it supports open-source technologies, giving the option to choose from thousands of open-source packages and machine learning components.
Folio3’s Machine Learning as a service offers features that help automate model generation and tuning to increase efficiency and accuracy, deploying better and faster models in the cloud or on-premises. Extensive model testing and customization ensures the best solution fit for your company, as our experts work in collaboration with you throughout every step of the process, from data preparation to model training and from evaluation to deployment.
Custom ML Development
Business Process Automation
Advantages of Our Machine Learning Solutions
We provide our clients with a seamless experience as they undergo the transition from traditional functioning towards more efficient processes.
Why Choose Folio3?
We offer tailored solutions for our clients with the flexibility that they need to stay competitive in their respective industries. Our experience of working in different verticals gives us an insight into business-specific tech solutions. Hire Machine learning experts on an hourly or full-time basis and save development costs.
18+ Years of
What We Do Right
Through the use of the latest technologies, we can help you maximize efficiency and productivity by automating your transcription process. This can provide multiple benefits to both individuals and corporations.
Provide Technical Expertise
Build robust solutions to ensure sustainable growth in the digital economy
Deliver the Best Solution
Smooth deployment in the best possible manner at the best price points
Feature Rich Solutions
Maintain an enriched ecosystem for guidance to increase efficiency
Build ML Apps with Ease
With our Machine Learning as a service platform, we offer our clients the opportunity to create tailored machine learning applications
Hear From Our Clients
Machine Learning FAQs
What is a kernel in machine learning as a service?
In machine learning, a “kernel” refers to the method of using a linear classifier to solve a non-linear problem. It requires changing linearly inseparable data into linearly separable ones. The kernel function allows mapping of non-linear observations into a higher-dimensional space, facilitating separability. The kernel approach is simply a framework for performing pattern analysis on different types of data across a series of tasks for a range of applications. It also facilitates the use of feature spaces, where parameter dimensionality is more than polynomial.
In computing, the kernel computer program is at the core of a computer’s operating system, with unrestricted control over the entire system. It is usually one of the first programs to load on start-up before the boot loader.
What is bias in machine learning as a service?
Machine Learning algorithms are designed utilizing data that is trained to make assumptions. The machine learning model uses new data input to generate values based on trained machine learning model. The data remain dependant on the set of training data that is used for scoring. Scoring is basically prediction and if not properly monitored can become susceptible to cognitive biases. Assumptions can be biased and in Machine Learning it helps generalize data so that better results can be formulated for larger datasets with various other attributes. Bias in Machine Learning helps make models less sensitive to single data point, however generalized algorithms can also produce outcomes that are systematically prejudiced. When it comes to crucial decisions, bias in Machine Learning can produce amplified results that may not provide a true picture of ground reality.
What is cross validation in machine learning?
This procedure of choosing the numerical outcomes measuring hypothesized connections between factors, are adequate as descriptions of the data, is known as validation. As an acceptable practice, an error estimation for the model is made in the wake of preparing, also called evaluation of residuals. A training error procedure would produce the estimated difference in the predicted and actual responses. Be that as it may, this procedure just gives an idea regarding how well our model does on data used to train it; giving two possible outcomes for the model, either underfitting or overfitting the data. Along these lines, the issue with this assessment procedure is that it doesn't give an indication of how well the learner will sum up to an independent/ unseen data set. Arriving at the conclusion is known as Cross Validation.
What is a classifier in machine learning as a service?
The procedure of predicting the class of given data points is known as classification. Terms such as labels or categories are interchangeably used for classes. Classification predictive modeling is a process of approximating a mapping function, from input variables to make output variables distinct.
Classification comes under supervised learning where the targets also input data. Classification applications are available across multiple domains such as in credit approval and medical diagnosis. Supervised learning procedures can broadly be divided into regression and classification algorithms. Classification algorithms in Machine Learning include Logistic Regression, Decision Tree, Random Forest and Naive Bayes.