ML Model Development

Machine Learning Model Development is all about developing ML models with the right dataset to make predictions or decisions. Most of the B2C or B2B sectors are looking for Large language model with custom data set and thats where our expertise can help you to build ML Model that can be deployed within your premise with your own data. Our expert team at Codiste follows a meticulous process to provide you with the best machine learning model development services. From data collection to customizations, we ensure seamless model training and fine-tuning for maximum accuracy.

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ML Model Development
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Our Machine Learning Development Services

Our machine learning development services are one of the most powerful ones and are appreciated by different industry leaders. We get onboard and bring you custom solutions involved in the machine learning model development life cycle. Our ML modelengineering services are well-equipped and contribute to the success of businesses across various industries.

Why Choose Us For Your ML Model Development?

Choose Codiste as an expert ML model and AI Development Company to develop a more accurate ML model.

Comprehensive Development

Experience In ML Algorithm

As an expert, Codiste ensures that one can get the most promising knowledge about ML algorithms and then implement the same. It includes supervised, unsupervised as well as reinforcement learning with impactful results and expertise in algorithms! Based on your use case we can come up with the right solution and choose the Right Algorithm.

Comprehensive Development

Quality of Data and Pre-processing

We ensure that we bring forth a team that is the most experienced in quality data and can thus offer data cleaning. Not only that, but we also help you with ultimate model accuracy and the most promising pre-processing pipeline. The more cleaned data, the more accuracy, and ultimately more accurate output.

Comprehensive Development

End-to-End Model integration

From system assessment to data handling, integration, and infrastructure to model development and maintenance, we have the expertise to optimise Machine Learning development cycles that address your business problem and focus on key business areas to maximize ROI.

Comprehensive Development

Optimization of Performance

Codiste Team ensures you can get the ultimate ML model optimization services with us. The requirement for every business is different. Hence it is important to have a predicament that can handle such large loads of data without compromising the results.

Comprehensive Development

Evaluation Assessment

We ensure that only the best kind of evaluation is deployed so that it does not compromise the quality of the assessment. It helps us maintain accuracy, one of the most basic tenets of a domain like ML model development.

Comprehensive Development


Our ML development team always aims to help you have the ultimate unique and custom model developed using your own data. We aim to cater to the customizations by implementing our expertise in advanced machine learning technologies, ML algorithms, and neural networks and tuning them to meet your challenges.

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.


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.


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.


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.


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.



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.



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.

Want to hire expert ML resources to tackle Your Complex business problems?

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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


  • Scikit_learn

    Scikit Learn

  • Apache_Spark_

    Apache Spark

  • Pandas


  • github


  • bitbucket


  • git


  • kubernetes


  • docker


  • Power-BI


  • Tableau


  • Matplotlib


  • Airflow

    Apache airflow

  • sagemaker


  • 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.



Real Estate

Real Estate

Event Industry

Event Industry





Renewable & green energy

Renewable & green energy

Sports Tech

Sports Tech





Funded Start-ups

Funded Start-ups

Cleantech Space

Cleantech Space

Human Resources

Human Resources

Our Engagement Models


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.


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.


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.


ML Model or Machine Learning model is a computational algorithm which by learning the pattern and deciphering the connection between data, aids in making decisions without any explicit programming rules. Using the acquired knowledge, it generalizes and performs tasks on either new data or old ones.
It is the root of many kinds of applications namely image recognition, natural language processing, and recommendation engine, among others.

Training the ML model infers that one will teach the model to recognize patterns or algorithms and then generate predictions based on that. Training an ML model is difficult and is usually a professional's work. It includes many steps, like collecting and preprocessing and splitting the data with their weight.
Then comes training the model and adding a hyperparameter. Finally, you can focus on model evaluation as well as optimization. Once you have completely trained the ML model, it is time to deploy the model into your system.

Deploying an ML model ensures that it is ready for the practical world for use. The deployment of the ML model completely depends on the type of software and your system's infrastructure. Different steps involve choosing the environment for deployment, setting up the dependency, creating the interface, and deploying the model.
However, deploying the model is a complex process and can require added assistance. There are security considerations that need to be taken care for any unauthorized access. It can prevent your system from any susceptible attack.

ML model performance can be evaluated through techniques like Train-Test Split, K-Fold Cross Validation, Confusion Matrix, Receiver Operating Characteristic (ROC) Curve, Precision-Recall (PR) Curve, R2 Score, and more. This will depend on the use case and what we are trying to achieve.

The classification model in machine learning is a type of supervised learning model. It can help you to infer the categorical class or even label the features of the inputs. A classification model ensures that you can learn from the data that has been labeled. The major focus of a model like the classification model in machine learning is to make any predictions concerning new and unseen data.

Most of the ML model techniques can be classified into three main types, i.e.
  • The supervised learning model is trained on labelled data. It requires input-output pairs that include input data with corresponding labelled output data. Linear Regression, Neural Networks, SVM, Decision Trees, etc., are common supervised learning algorithms.
  • An unsupervised learning model is trained on unlabeled data, where the algorithm tries to search out structures or patterns in the input data without explicit output labels. Principal Component Analysis, K-Means Clustering, Hierarchical Clustering, etc., are common unsupervised learning algorithms.
  • The reinforcement Learning Model maximises a cumulative reward by including an agent learning to make decisions and act in an environment based on the feedback. Deep Q Networks (DQNs), Q-Learning, etc., are well-known reinforcement learning algorithms.

Other than the above mentioned, Transfer Learning Models, Generative Models, Ensemble Learning Models, and Instance-based Learning Models are some less popular machine learning models.

Ready to see your data come to life through specialized ML model development?

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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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