Machine Learning


Machine learning development services leverages AI and ML tools, techniques, and tactics to develop machine learning models for cognitive business technology that enhances system performance and transforms raw data into clean and usable datasets.

Our experts help companies capitalize on ML technology, selecting applications for their business needs and delivering ML solutions at scale. DarkRockMountain helps companies incorporate machine learning into their processes to discover patterns, predict outcomes, and drive automation.

Machine Learning (ML) Engineering

DarkRockMountain’s ML engineers develop smart, data-driven business applications with machine learning models for data extraction & analysis, image & pattern recognition, and precision predictive engines to enable better business decision-making.

Data Migration for ML

We convert data from legacy and big data systems into usable datasets for executing multi-label classification, regression, clustering, density estimation, and dimensionality reduction analyses before deploying those models across relevant systems.

Machine Learning Operations (MLOps)

We utilize MLOps and AutoML strategies to eliminate business machine learning adoption bottlenecks, empowering IT teams to lead production machine learning projects without compromising model quality, performance, or interpretability.

Deep Learning (DL) Development

DarkRockMountain specializes in the development of DL technologies using ML algorithms to build cognitive BI technology frameworks that imitate the way humans retrieve & store information, used to identify specific concepts within processing workflows.

Machine Learning Services Cycle

  • 1

    Business analysis

    • Defining business needs a firm wants to address with machine learning.
    • Analyzing the existing machine learning environment (if any).
    • Determining regulatory compliance requirements for an ML solution.
    • Designing a machine learning implementation strategy and roadmap.
    • Deciding on machine learning solution deliverables.
  • 2

    Technical design

    • Designing an optimal feature set for an ML solution.
    • Architecting an ML system according to scalability, security, and compliance requirements.
    • Selecting optimal machine learning technologies (ML programming languages, ML development frameworks, data processing techs, etc.).
    • Designing role-specific UX and UI to interact with an ML solution.
  • 3

    Data preparation

    • Exploratory analysis of the existing data sources.
    • Data collection, cleansing, and structuring.
    • Defining the criteria for the machine learning model evaluation.
  • 4

    Development and implementation of machine learning models

    • ML model exploration and refinement.
    • ML model testing and evaluation.
    • Fine-tuning the parameters of ML models until the generated results are acceptable.
    • Deploying the ML models.
  • 5

    Support and maintenance of machine learning models

    • Continuous monitoring and tuning of ML models for greater accuracy.
    • Adding new data to the ML models for deeper insight.
    • Building new ML models to address new business and data analytics questions.
  • Optimized investment

    Our machine learning consulting company can help your organization select an optimal tech stack and identify use cases requiring ML instead of conventional solutions.

  • Streamlined project planning

    With the support of a machine learning consulting firm, you can set up a suitable ML development and implementation roadmap and accurately define timelines, budgets, tasks, teams, and iterations.

  • Faster rollout

    Our ML consultants can complement your in-house experts to complete your ML project within a shorter time frame without recruiting and training additional talent.

  • Lower business risk

    A team of ML consultants will help you address potential business or technical challenges (lack of training data, ML model bias, non-compliance, etc.) and mitigate related risks.

Techs & Tools we are proficient in:

Programming languages

  • .Net

  • C++

  • Golang

  • Java

  • JavaScript

  • Kotlin icon

    Kotlin

  • Php

  • Python

  • Qt

  • Rails

  • Rust

  • Scala

  • Swift icon

    Swift

Cloud

  • AWS - Amazon Web Services

    AWS

  • Microsoft Azure

    Azure

  • DigitalOcean

    DigitalOcean

  • Google Cloud

    Google

  • IBM Cloud

    IBM

Databases

NoSQL Databases
  • Apache HBase

  • Apache Nifi

  • Cassandra

  • MongoDb

  • Neo4j

  • Redis

SQL Databases
  • MicrosoftSQL

  • MySql

  • Oracle

  • PostgreSQL

Big Data

  • Apache Spark

  • Apache Storm

  • Confluent

  • Databricks

  • Hadoop

  • Hive

  • Kafka

  • Snowflake

Machine learning & Data Science

  • Alteryx

  • Apache Mahout

  • Keras icon

    Keras

  • Mathworks icon

    MatLab

  • OpenCV

  • PyTorch icon

    PyTorch

  • logo--r-script

    R

  • Scikit-learn

    Scikit-Learn

  • SpaCy

  • TensorFlow icon

    TensorFlow

  • Theano

DevOps

CI/CD & Automation
  • Ansible

  • CircleCI

    CircleCI

  • file_type_cloudfoundry

    Cloud Foundry

  • GitHub Actions

    GH Actions

  • Git

  • GitHub

  • GitLab

  • Jenkins

  • Packer

  • Tekton

    Tekton

  • Terraform

  • Travis CI

Containerization
  • Apache Mesos

  • Docker

  • Kubernetes

  • logo--openshift

    OpenShift

Monitoring
  • Datadog icon

    DataDog

  • Grafana

  • Prometheus icon

    Prometheus

Security & Testing
  • Gremlin

    Gremlin

  • HashiCorp Vault

  • selenium

    Selenium

  • Snyk icon

    Snyk

Blockchain

Platforms
  • EOS

  • Ethereum

  • Graphene

  • Hyperledger

  • Solana

Development tools & languages
  • OpenZeppelin

    OpenZeppelin

  • file_type_solidity

    Solidity

  • Vyper

  • Waffle

Front & Back End Frameworks

  • angular

    Angular

  • Astro

    Astro

  • django

    Django

  • Flask

  • Flutter

  • Gatsby

  • GraphQL

  • Hugo

  • Next.js

  • Node.js

  • React

    React

  • Vue.js

    Vue