FAQs
Machine Learning as a Service (MLaaS) is a cloud-based offering that provides businesses with access to machine learning algorithms, tools, and expertise without the need for significant investment in infrastructure or specialized resources. MLaaS allows organizations to leverage the power of artificial intelligence and predictive analytics to extract valuable insights from their data and make data-driven decisions.
Machine learning can benefit your business in several ways:
No, you don’t need to have expertise in machine learning to use MLaaS. Calligo’s MLaaS offering is designed to be accessible to businesses of all sizes and levels of technical proficiency. Our team of skilled data scientists and machine learning experts will work closely with you to understand your unique requirements, develop models, and guide you through the process.
At Calligo, we prioritize data security and privacy. We have robust security measures in place to protect your data, including encryption, access controls, and regular security audits. Additionally, we ensure compliance with relevant data protection regulations, giving you peace of mind that your data is handled with the utmost care.
To get started with MLaaS, simply reach out to our team through the contact form or contact information provided on the MLaaS service page. We will schedule a consultation to understand your requirements, discuss the scope of your project, and provide you with a tailored solution and pricing information.
Yes, MLaaS can be seamlessly integrated with your existing systems and applications. Our team will work with you to understand your infrastructure and integration requirements, ensuring a smooth integration process.
MLaaS can benefit businesses across various industries, including finance, healthcare, retail, manufacturing, marketing, and more. Any organization that deals with large amounts of data and wants to leverage it for insights and improved decision-making can benefit from MLaaS.
Have more questions? Feel free to contact our team for further assistance or clarification. We’re here to help you harness the power of machine learning and unlock the full potential of your data.
@import "../../resources/scss/util/variables";
@import "../../resources/scss/util/mixins";
.block-faqs{
padding: 3em 0;
@include bp($lg){
padding: 6em 0;
}
.heading{
font-size: rem-calc(24);
line-height: rem-calc(32);
letter-spacing: 0.02em;
font-weight: 400;
@include bp($lg){
font-size: rem-calc(40);
line-height: rem-calc(48);
}
}
.heading-col{
margin-bottom: rem-calc(24);
@include bp($lg){
margin-bottom: rem-calc(48);
}
}
&-accordion{
padding-bottom: rem-calc(25);
margin-bottom: rem-calc(25);
padding-left: rem-calc(48);
border-bottom: 1px solid $black;
position: relative;
@include bp($lg){
padding-bottom: rem-calc(32);
margin-bottom: rem-calc(32);
border-bottom: 2px solid $black;
padding-left: rem-calc(158);
}
&:last-child{
margin-bottom: 0;
}
&:before{
content: '';
position: absolute;
left: 0;
top: rem-calc(3px);
width: 24px;
height: 24px;
border: 1px solid $black;
border-radius: 100%;
background-repeat: no-repeat;
@include bp($lg){
top: rem-calc(8px);
}
}
&.expand{
.block-faqs-question{
font-weight: 600;
}
.block-faqs-answer{
padding-top: rem-calc(16);
}
&:after{
content: '';
position: absolute;
left: rem-calc(5px);
top: rem-calc(8px);
width: 14px;
height: 14px;
background-color: $black;
border-radius: 100%;
background-repeat: no-repeat;
@include bp($lg){
top: rem-calc(13px);
}
}
}
}
&-question{
font-size: rem-calc(18);
line-height: rem-calc(26);
letter-spacing: 0.02em;
font-weight: 400;
transition: 0.3s ease color;
cursor: pointer;
@include bp($lg){
font-size: rem-calc(24);
line-height: rem-calc(40);
}
&:hover{
color: $orange;
}
}
&-answer{
overflow: hidden;
max-height: 0;
transition: 0.3s ease-in-out all;
max-width: rem-calc(708);
*{
font-size: rem-calc(16);
line-height: rem-calc(24);
letter-spacing: 0.05em;
color: $primary;
&:last-child{
margin-bottom: 0;
}
}
}
&.bg--primary{
.overline,
.heading{
color: $white;
}
.block-faqs-accordion{
border-color: $white;
&:before{
border-color: $white;
}
&.expand{
&:after{
background-color: $white;
}
}
}
.block-faqs-question,
.block-faqs-answer *{
color: $white;
}
}
}class FAQs {
/**
* Create and initialise objects of this class
* https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Classes/constructor
* @param {object} block
*/
constructor() {
this.blocks = document.querySelectorAll('.block-faqs');
this.init();
}
init() {
this.blocks.forEach((block) => {
block.querySelectorAll('.block-faqs-accordion').forEach((accitem)=>{
accitem.addEventListener('click',function(e){
e.preventDefault();
if(!this.classList.contains('expand')){
this.closest('.block-faqs').querySelectorAll('.block-faqs-accordion.expand').forEach((liitem)=>{
liitem.classList.remove('expand');
liitem.querySelector('.block-faqs-answer').style.maxHeight = 0;
});
this.classList.add('expand');
this.querySelector('.block-faqs-answer').style.maxHeight = this.querySelector('.block-faqs-answer').scrollHeight + 160 + "px";
}
else{
this.classList.remove('expand');
this.querySelector('.block-faqs-answer').style.maxHeight = 0;
}
})
});
});
}
}
new FAQs();