Machine learning consulting

  • Ensure faster ROI with an ML strategy that targets high-impact business areas and focuses on measurable outcomes

  • Validate your data quality and unify scattered datasets to develop accurate and reliable ML solutions

  • Avoid security risks and protect your business-specific data with a guided approach to ML adoption

  • Develop an efficient ML solution implementation plan leveraging ML integration best practices to ensure success

Yalantis ML consultants help you achieve during implementation:

  • Up to 50%

    less time on ML solution integration

  • Up to 30%

    increase in ML development productivity

  • Up to 15%

    cost savings during ML project execution

  • Up to 70%

    reduction in security risks

Machine learning consulting services Yalantis provides

Our proficient machine learning consultants help you eliminate any concerns and misconceptions you might have about ML by developing a customized ML implementation strategy. Maximize the benefits of machine learning for your business with a fitting ML adoption path.

  • ML strategy and planning

    • Conducting market and competitor analysis

    • Eliciting business and compliance requirements

    • Identifying business issues

    • Assessing existing data

    • Optimizing data collection and management

    • Preparing an iterative ML project plan

  • Technology consulting and ML approach selection

    • Evaluating current infrastructure and technical expertise

    • Researching the latest ML algorithms and models

    • Recommending optimal solutions

  • Feasibility study

    • Rapidly evaluating and comparing models

    • Validating hypotheses

    • Estimating required resources for the ML project

    • Preparing a cost-breakdown structure

    • Shortlisting ML model candidates

  • Risk assessment

    • Identifying risks (e.g. related to data, ML models, budget)

    • Risk scoring and prioritization

    • Devising risk mitigation strategies

    • Addressing emerging risks

  • Cost–benefit analysis

    • Defining analysis frameworks

    • Listing direct and indirect costs and benefits

    • Providing a cost–benefit ratio of ML solution adoption

    • Offering best-fit decisions

    • Suggesting alternatives

Assess the feasibility of machine learning integration for your business

Ensure effective and productive ML implementation by leveraging our extensive ML consulting expertise.

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FAQ

How long does a typical machine learning consulting engagement last?

ML consulting typically takes from several days up to two weeks. During this process, we work closely with you to understand your needs, provide a specific estimate and timeline for your project, and offer you the most beneficial ML development strategy to accelerate your time to market.

 

ML consultants can also be available on demand during project execution. Simple projects like automating a data analysis task might be completed within three to six months. More complex projects, such as building a large-scale recommendation engine, could take 12 months or even longer.

What qualifications and expertise do your machine learning consultants have?

Our machine learning consultants are highly qualified specialists with master’s degrees in machine learning and real-world experience in data science and business analysis, allowing them to build and deploy scalable and high-functioning machine learning solutions across various industries. A background in business analysis allows them to not only understand the technical aspects of your project but also translate them into actionable business value.

What are the expected deliverables of your machine learning consulting services?

Deliverables are tailored to your project but typically include a custom ML strategy, recommendations on the technology stack, and a feasibility study to indicate how relevant ML adoption is for your business. To provide even more accurate results and help you make a carefully considered decision regarding ML adoption, we can also conduct a cost–benefit analysis and risk assessment session.

Do you offer ongoing support or maintenance services after a consulting engagement?

We offer various support packages that can last for the duration of your ML project. We can even offer support and maintenance services if you decide to develop your solution with your in-house data science team. You can rely on our SRE and DevOps services for continuous improvement and monitoring of ML models and data infrastructure.

Benefits of cooperating with a machine learning consulting company

Partnering with a machine learning consulting company offers plenty of benefits for businesses seeking to adopt powerful ML technology. IT consulting services are often undervalued and skipped in favor of development services. However, with proper preparation, projects tend to be more successful and result-driven, as the development team has all the necessary information about your business to make sure each technical decision aligns with your needs.

Possible outcomes of the consultant’s review of your business idea that may affect your solution’s development process:

Outcome #1: During machine learning or deep learning consulting services, our specialists can help you define optimal computational capacity for ML model training. This can eventually help you decrease the costs of running your ML solution infrastructure.

Outcome #2: A consulting session can recommend starting with a proof of concept (PoC) to validate if machine learning can benefit your company. PoCs are especially helpful for untested project ideas, as they require less investment and carry lower risks compared to full-scale development.

Let’s dive into more advantages of machine learning consulting services:

Expertise and guidance. Machine learning consultants possess a deep understanding of the machine learning and deep learning fields and have a solid background in working with business challenges of varying complexity. They can assess your specific needs and recommend the most effective ML solutions, ensuring you invest in the right technology for your goals. With expert guidance, you have a much higher chance of succeeding in ML initiatives.

Strategic implementation. Machine learning consulting firms guide you through the entire ML implementation process, from data collection and preparation to model development and deployment. Such thorough and ongoing ML consulting services ensure smooth and efficient integration, minimizing risks and maximizing return on investment (ROI).

Data savvy. Extracting value from data is key to successful ML projects and profitable business solutions. Yalantis ML consultants are experts in data management and can help you identify, organize, and clean your data to fuel accurate ML models that help you gain valuable insights.

Identifying and mitigating risks. ML projects can be complex and prone to pitfalls. Based on their experience and deep analytical skills, ML consultants can foresee challenges and develop strategies to mitigate risks, saving you time, resources, and effort.

Competitive advantage. Leveraging machine learning consultancy can help you develop custom ML solutions that give you a strategic advantage over competitors. With our experience delivering successful ML solutions across industries, in various market conditions, and fueled by continuous customer feedback analysis, we ensure you get the expertise to outpace your competition. Custom ML solutions can deliver valuable business insights much more efficiently than commercial alternatives.

Faster results. With their expertise, experience, business understanding, and financial literacy, ML consultants can help you achieve expected results quickly. They can help you streamline the ML adoption process, identify shortcuts, and ensure your ML project delivers value without unnecessary delays.

By working with a machine learning consulting company, you gain a valuable partner with the experience and know-how to navigate the ever-evolving world of ML services. Such collaboration empowers you to unlock the true potential of this transformative technology and achieve significant business success.

How do you choose a qualified machine learning consultant?

Finding a qualified machine learning consultant is the first and most important step when considering ML adoption. To truly unlock the potential of your ML project and work with experienced professionals, consider these decisive factors:

Domain expertise

Go beyond general machine learning proficiency, as you need not only technical skills but also experts capable of advising you on making business decisions. You should look for a machine learning consulting service company that has deep knowledge of your specific industry and its current needs. This ensures they understand the nuances of your business problems and can tailor ML solutions that integrate seamlessly and benefit your business in the long term.

Technical breadth and depth

While a strong foundation in core machine learning algorithms is essential in a machine learning consultant, you should also assess a consultant’s experience with cutting-edge data science and data management tools and technologies relevant to your project. Explore their familiarity with frameworks and libraries like TensorFlow and PyTorch, and other industry-specific toolkits.

Data engineering skills

Machine learning is heavily dependent on clean and well-structured data. Therefore, you should also evaluate a consultant’s knowledge of data collection, organization, wrangling, and feature engineering. Make sure your consultant can analyze and efficiently handle the complexities of your data infrastructure and ensure it’s suitable for model development. Experienced machine learning consulting firms should have a proven track record of successful data projects in equal measure to ML projects.

Communication style and cultural fit

Technical expertise is crucial, but the ability to bridge the gap between data science and business stakeholders is a true game changer in machine learning consulting. Look for consultants who can translate complex concepts into clear and concise language for both technical and non-technical stakeholders. Additionally, assess the cultural fit with your team. Smooth collaboration hinges on clear communication and a shared product mindset.

Project management and delivery strategy

Machine learning projects are iterative and require a flexible yet well-organized approach. Consider learning about your potential ML consultant’s project management methodology. Can they effectively define milestones, manage timelines, and adapt to changing requirements? How will they integrate with your existing workflows and processes?

Beyond the technical: Business acumen

A valuable machine learning consulting company understands the business implications of machine learning. Your ML consulting partner should be able to translate technical jargon into actionable insights and align the project with your overall business goals. Look for specialists who understand financial literacy, conduct cost–benefit analysis, and consider potential ROI and long-term sustainability of the implemented ML solution.

Scalability and future-proofing

Think beyond the immediate project. Does your potential ML consulting services company have a plan for scaling the solution as your data and business needs evolve? Can they integrate the model with your existing systems and infrastructure? Explore their approach to continuous monitoring, model retraining, and potential ethical considerations. Additionally, as your ML journey doesn’t end with deployment, learn about the company’s post-deployment support strategy. Find out whether they offer ongoing maintenance, troubleshooting, and assistance in refining the model over time.

By delving into these details when selecting a suitable machine learning consultant, you can discover a specialist who will not just execute your project but become a true partner in driving your machine learning journey to success.

Develop a comprehensive ML strategy tailored to your specific needs

Evaluate your existing datasets, optimize their collection and management, and establish an iterative project plan to ensure continuous improvement and ROI.

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