Machine Learning Solutions
Utilize the power of purpose-built machine learning solutions to transform
business processes and solve real-world problems.
Transforming Data into Actionable Insights with Tailored Machine Learning Solutions
Stay Ahead of the Game with Folio3's Machine Learning Expertise. Our team leverages advanced statistical methods and deep knowledge of ML algorithms, including Deep Learning, to create customized solutions that meet the unique needs of your business. Elevate your product or service and stay ahead of the competition with the power of state-of-the-art machine learning.

Our Machine Learning Solutions Include

Computer Vision
Utilize computer vision techniques to extract meaningful data from pictures and the surroundings for face recognition, biometrics, transportation, and other use cases.

NLP
Create chatbots, teach machines to interpret voice and text as people do, extract useful information, identify topics in text documents, and more.

Predictive Analytics
Utilize data from the past and present to get a glimpse into the future. Eliminate speculation and discover how the future will affect your business, clients, or the whole sector.

Anomaly Detection
Leverage data to recognize unusual activity to spot fraud, security flaws, data breaches, health issues, structural problems, etc. and take timely action.

Custom Development
With our Machine Learning as a service platform, we offer our clients the opportunity to create tailored machine learning applications

ML Staff Augmentation
Hire ML developers as required for your project.
Find the best machine learning engineers who can handle your AI and ML needs. We are capable of providing you the agility you want when selecting a technology partner thanks to the flexible engagement and quick resource allocation choices.
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:
01

Submission of a hypothesis
02

Scope and feasibility of the project with defined Delivery of a Proof of Concept
03

Algorithm development with regular touchpoints
04

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.
ML Ops
Custom ML Development
Business Process Automation
AI Solutions By Folio3 Machine Learning Company
Our solutions create ease and efficiency for our clients and create a shift from the traditional rule-based processes to more intelligent ones, enabling the discovery of new unstructured data sets and patterns.
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.

Hassle-Free Project
Management

Full
Visibility

Extensive
Experience

Integrity and
Transparency

18+ Years of
Experience

Flexible Engagement
Models
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
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.