Deep Learning Services

We specialise in pushing boundarie­s of developing advanced ne­ural architectures, and applying on unstructured data. Our expe­rienced specialists cre­ate tailored Deep Learning solutions that address your unique­ challenges, reve­aling insights, predictions, and ideas previously be­yond reach.

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Deep Learning Services
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Our Deep Learning Services

Codiste’s technical prowess in offering deep learning service opens opportunities for innovation across various industries, from clarifying images to generating textual content.

Why Us Choose for Deep Learning Service?

Codiste has expertise to perform analytical and automation with their expertise in deep learning AI methods.

Comprehensive Development

Neural Architectures Expertise

Our skilled team leads the­ way in designing complex neural archite­ctures. We specialise in creating unique models that show intricate patterns in your data, utilising our expe­rtise in convolutional, perennial, and atte­ntion-based networks. These­ specialised designs offe­r unmatched precision and gene­rate valuable insights.

Comprehensive Development

Data Insights

We have the key to the gems concealed in unstructured data. We derive meaning and structure from raw text, audio, and visual inputs using deep learning techniques such as convolutional neural networks (CNNs) and generative adversarial networks (GANs), translating them into actionable insights.

Comprehensive Development

Customised Model Optimisation

Our deep learning model optimisation approach is nothing short of masterful. We fine-tune hyperparameters, use advanced regularisation, and employ advanced optimisation methods. This painstaking refinement produces models with excellent precision, dependability, and the ability to thrive in turbulent conditions.

Comprehensive Development

Transfer Learning Approaches

We utilise transfer learning approaches as an innovation accelerator. We accelerate development while guaranteeing that your models can easily overcome unexpected difficulties by rethinking pre-trained models using knowledge distillation and domain adaptation.

Comprehensive Development

Explainability Techniques Implementation

Our AI fabric is knitted with transparency. Explainability techniques such as saliency maps, counterfactual explanations, and concept attribution are used by us to reveal the subtle strands of your deep learning model's decision-making process. You will acquire insights and create trust with intelligent AI.

Comprehensive Development

Support and Collaboration

Our shared experience is your path to profound learning. We provide extensive assistance surpassing deployment, from data curation and model training to deployment tactics and monitoring systems. We allow your team to navigate the complexities of deep learning and drive innovation independently with a collaborative approach.

Our Machine Learning Consulting Approach

We assist organizations in a seamless journey of utilizing AI-driven insights, from defining clear business goals to implementing and monitoring ML models development.

1

Business Goal

Understanding the desired result, establishing key performance indicators (KPIs), and coordinating the ML strategy. With the larger corporate objectives are necessary for this.

2

ML Problem Framing

Convert the business objective into a clear-cut machine learning challenge. In this phase, the type of ML task (such as classification, regression, clustering) is determined, the relevant evaluation metrics are chosen.

3

Data Processing

Preparing and preprocessing the data will assure its accuracy, completeness, and suitability for ML algorithms. This covers operations like feature engineering, addressing missing values, data cleansing, and converting data into a format for model training.

4

Model development

Using the cleaned-up data, create, train, and fine-tune the machine learning model. Choosing the right algorithms, optimizing hyperparameters, and assessing model performance using methods like cross-validation comes in this stage.

5

Deployment

To make the trained ML model available for real-time predictions, integrate it into a production environment or application. This entails creating a deployment architecture, managing model versioning, and setting up data pipelines for seamless integration.

6

Monitoring

Monitoring important metrics, identifying and dealing with model or concept drift, and putting in place safeguards to guarantee the model's dependability and efficiency over time.

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Technology Stack

Our experienced team of engineers pack your Machine Learning Consulting with the best technologies ensuring that enable fault-free operations and be ready for transforming and scaling business.

  • pytorch

    pytorch

  • Scikit_learn

    Scikit Learn

  • Apache_Spark_

    Apache Spark

  • Pandas

    Pandas

  • github

    github

  • bitbucket

    bitbucket

  • git

    git

  • kubernetes

    kubernetes

  • docker

    docker

  • Power-BI

    Power-BI

  • Tableau

    Tableau

  • Matplotlib

    Matplotlib

  • Airflow

    Apache airflow

  • sagemaker

    sagemaker

  • Autokeras

    Autokeras

Industries We Serve

Being a trusted Machine Learning Consultant, we have worked with a wide range of sectors on a global scale and have been a part of their growth stories.

Education

Education

Real Estate

Real Estate

Event Industry

Event Industry

Healthcare

Healthcare

Fintech

Fintech

Renewable & green energy

Renewable & green energy

Human Resources

Human Resources

Sports Tech

Sports Tech

Gaming

Gaming

E-Commerce

E-Commerce

Funded Start-ups

Funded Start-ups

Cleantech Space

Cleantech Space

Our Engagement Models

01

Fixed Engagement Model

Get a predefined budget and timetable that is tailored for machine learning solutions projects with well defined scope and needs. This strategy, which is best for small to medium projects, guarantees cost predictability and provides the stated deliverables within the scheduled time range.

02

Time and Material Engagement Model

Our Time and Material Engagement Model is flexible and adaptable, making it ideal for machine learning professional services projects with changing requirements and undefined scope. As a result, there is more flexibility in adapting changes, scalability, and continued cooperation throughout the development lifecycle because you only pay machine learning consultant hourly rate actually used on the project.

03

Hire Dedicated Team Model

Strengthen your internal resources by putting together a group of talented ML Consultants and engineers that are only committed to the success of your project. This model offers the benefit of an extended development team that works extremely collaboratively to meet the needs and goals of your organization while guaranteeing smooth communication, control, and transparency.

FAQs

Deep learning is a kind of technique used on computers to process data easily and naturally. It teaches computers how to learn and handle complex tasks successfully just as the human brain does. These days, deep learning is widely used in all business verticals to gain more accuracy without any human intervention in data processing.

  • Machine Learning (ML) uses AI-based computer algorithms to examine and learn the given data for making informed decisions. On the other hand, Deep Learning uses artificial neural networks to process tasks mimicking human brain operations.
  • ML works efficiently on smaller data sets. However, deep learning requires a large amount of data.

Deep learning models are very effective in the areas where large volumes of data are used to generate values or make predictions. By using those very diverse and unstructured data, Deep Learning models enable machines to learn and solve complex problems. Some of the real time examples are Virtual assistance, automatic translation, driverless autonomous cars, chatbots, facial recognition and so.

Deep learning technology makes computer systems learn and process things just as the human brain does. It stimulates the human brain when it comes to recognizing patterns and classifying tasks for successful completion. In simple words, a computer system that uses deep learning does the similar process of a toddler learning and identifying a cat.

Deep learning in the education sector helps students to enhance their learning qualities by focusing more on understanding. It promotes students to learn principles and concepts that develop learning skills. Moreover, students engage more in the learning process and develop thinking skills. This makes students more effective in real-world jobs.

Chatbot with deep learning technology learns everything from the given data or the conversion/interaction between humans. It uses deep neural networks for pattern identification and learning to provide more accurate output. The more data you feed to the chatbot, the more effective and accurate the output.

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Case Study

ML Estimation

Streamline HVAC project bidding with ML estimation, automating drawing annotation and generating accurate bill of materials. Save time, differentiate yourself in the industry, and leverage innovative technology for detailed quantity take-offs.

MLEstimation - AI Tool to Analyse your Building material

Satisfied clients is our proof of our excellence!

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Let’s get connected for comprehensive data analytics services from the best deep learning company

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