Top-Notch Machine Learning Development Company - EffectiveSoft

Machine Learning Development Services

As a subset of artificial intelligence (AI), machine learning (ML) uses massive quantities of data and sophisticated algorithms enabling machines to learn automatically. With such a powerful tool, companies can create smart workflows that allow employees to invest their energy in projects that require empathy and creativity.

Having hands-on experience in data science, EffectiveSoft offers outstanding machine learning services to help enterprises overcome critical business challenges. Our data-driven solutions empower companies to optimize operations, make smart business decisions, and streamline customer experience while staying ahead of the competition.

machine learning development services
machine learning development services

Our machine learning services

What about you?

We are eager to delve deeper into your specific needs and challenges to unlock the full potential of machine learning for your business. By gaining a thorough understanding of your goals and obstacles, we can tailor our solutions to effectively address your requirements. Together, we can harness the transformative power of machine learning to drive innovation and achieve your strategic objectives.

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    What we work with

    Our key industries

    • Healthcare

      AI and ML algorithms can enhance the predictive capabilities of medicine, as well as create and improve new therapeutic approaches to treating a variety of diseases. This is especially important when developing software for medical image analysis. Machine learning helps solve a lot of challenges, from developing new drugs to calculating the cost of insurance. ML algorithms can identify abnormalities that would otherwise go unnoticed. This makes machine learning a huge contributor to the early detection of high-risk diseases.

      AI ML in healthcare
      AI ML in healthcare
    • FinTech

      Machine learning helps financial institutions analyze the creditworthiness of borrowers and predict credit risks. With AI and ML algorithms, a lending platform can determine the eligibility of applicants, assess credit risks, and analyze portfolios. Machine learning helps brokers and investors create robust strategies for algorithmic trading and make informed investment decisions. Machine learning models can estimate the value, growth, and performance of stocks or other assets.

      AI and ML in fintech
      AI and ML in fintech
    • Logistics

      Machine learning helps financial institutions analyze the creditworthiness of borrowers and predict credit risks. With AI and ML algorithms, a lending platform can determine the eligibility of applicants, assess credit risks, and analyze portfolios. Machine learning helps brokers and investors create robust strategies for algorithmic trading and make informed investment decisions. Machine learning models can estimate the value, growth, and performance of stocks or other assets.

      machine learning in transportation and logistics
      machine learning in transportation and logistics
    • Ecommerce

      Businesses use AI and ML to get to know their clients and provide them with personalized recommendations. With machine learning algorithms, it is possible to predict customer behavior, calculate their next acquisition, or suggest related products. In addition, based on past analytics, purchases can be planned and costs can be reduced. ML-based algorithms also help build long-term relationships with customers and bring back inactive ones, increasing retention rates.

      Machine Learning in ecommerce
      Machine Learning in ecommerce
    • Telecom

      ML algorithms assist telecom network engineers with preventing churn and fraud, segmenting the client base, cross-selling, sending recommendations to subscribers and employees, and automating workforce management. Leveraging AI and ML, telecommunication companies can optimize their network to enhance customer experience, deliver business results, and drive ROI.

      machine learning in telecommunications
      machine learning in telecommunications
    AI ML in healthcare
    AI ML in healthcare
    AI and ML in fintech
    AI and ML in fintech
    machine learning in transportation and logistics
    machine learning in transportation and logistics
    Machine Learning in ecommerce
    Machine Learning in ecommerce
    machine learning in telecommunications
    machine learning in telecommunications

    ML software we create

    Our machine learning software development processes

    Business problem identification
    Monitoring and support
    1. 01

      Business problem identification

      You turn to us with an idea and we take on the preliminary work of studying your business goals, needs, and requirements, as well as your customers’ expectations to offer you a relevant solution.
    2. 02

      Exploratory data analysis

      Once the goal is set, we carry out an exploratory analysis. Our data analytics company reviews your current data infrastructure to summarize main characteristics, discover trends and patterns, spot anomalies, or check assumptions.
    3. 03

      Data preparation

      After the analysis, we start preparing the collected raw data to run it through ML algorithms. At this stage, we clean, label, classify, and transform your data into a unified format.
    4. 04

      Data modeling and evaluation

      We select specific algorithms and design the architecture of your future ML solution, training numerous models to find the one that provides the most accurate results.
    5. 05

      Implementation

      When the design is in place, we engineer, integrate, test, and put your product into the world, so that you or your clients can start taking advantage of the machine learning technology in a real environment.
    6. 06

      Monitoring and support

      Your ML model is built, trained, tested, and deployed. This is only the beginning. We offer comprehensive ML model maintenance services to ensure the solution continues to perform as expected. Our engineers regularly monitor and tune the models to keep them up to date.

    Why choose EffectiveSoft

    ML tech stack

    • Azure Machine Learning
    • Amazon SageMaker
    • Azure Cognitive Science
    • Bot Framework
    • Amazon Transcribe
    • Amazon Lex
    • Amazon Polly
    • Google Cloud AI Platform
    • Apache Mahout
    • Apache MXNet
    • Caffe
    • TensorFlow
    • Keras
    • Torch
    • Apache Spark
    • Theano
    • spaCy
    • Scikit learn
    • Gensim
    • Hadoop
    • Apache Spark
    • Cassandra
    • Apache kafka
    • Apache Hive
    • Apache Zookeeper
    • Apache hbase
    • Azure cosmos DB
    • Amazon redshift
    • Amazon DynamoDB
    • Power BI
    • Tableau
    • Grafana
    • Microsoft SQL Server reporting services
    • Microsoft Excel
    • Google Developers Charts
    • Chartist.js
    • FusionCharts
    • Data-wrapper
    • Infogram
    • Chartblocks
    • D3.js
    • Oracle business intelligence
    • MicroStrategy
    • QlikView
    • Sisense
    • Kyubit BusinessIntelligence

    F.A.Q. about Machine Learning

    • Artificial intelligence uses and processes data to make decisions and predictions. This is the brain of the system and the very “intelligence” of machines. Meanwhile, machine learning is a subset of AI and cannot exist without it. Machine learning is used to create and train artificial intelligence.

    • ML algorithms within AI, as well as other AI-based applications allow systems not only to process data, but also to use it to perform tasks, create predictions, learn, automate routine processes, improve productivity, and more.

    • They are essential members of an ML team. These experts are responsible for the design and creation of software that can automate AI/ML models. They build a large-scale system that uses massive data sets to train algorithms designed to generate valuable insights and predictions. ML engineers manage the whole data science pipeline, from sourcing and preparing data to deploying models into the real world.

    • The success of ML implementation does not depend on the size of the company but on its proactivity. Often, small companies are afraid to turn to machine learning software development because of the cost, so this technology remains a buzzword. Meanwhile, they lose competitive advantages that would help them increase efficiency, automate workflow, and grow.

      So the answer is yes, AI and ML suits a small company. The use of artificial intelligence and machine learning for small businesses goes a long way toward helping them cope with the shortage of skilled resources, reduce time on mundane tasks, improve services, enhance security, and more.

    • The cost of implementation of an ML project depends on the complexity of your solution. The best way to know exact numbers is to request a project estimate from our experts. But whatever the cost is, keep in mind the long-term profit the machine learning will bring.

    STILL HAVE QUESTIONS?

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    Get in touch

    Contact us

    Our team would love to hear from you.

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      What happens next?

      • An expert contacts you shortly after having analyzed your business requirements.
      • If required, we sign an NDA to ensure the highest privacy level.
      • A Pre-Sales Manager submits a comprehensive project proposal. It may include estimates, timelines, lists of CVs, etc., for a particular situation.
      • Now, we can launch the project.

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