MLOps Consulting

Leveraging MLOps consulting services can transform your machine learning lifecycle. Expert consultants provide tailored solutions and industry best practices, optimizing development processes and ensuring seamless deployment from development to AI product release. This results in accelerated time-to-market, enhanced model performance, and the ability to gain faster insights, keeping your business ahead in a rapidly evolving market.

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Strategic Advantages of MLOps for Your Business

Navigating the business landscape requires strategic adaptability. MLOps focuses on efficiency and agility, addressing the high infrastructure costs that cause 90% of ML startups to fail. By automating model deployment, reducing operational overhead, and improving scalability, MLOps helps your business tackle challenges and seize opportunities effectively.

Cost Savings

MLOps helps businesses save money by optimizing the entire machine learning lifecycle. Automated workflows and efficient resource management reduce the need for manual intervention, minimizing operational costs.

By detecting and addressing issues early in the development process, Machine Learning operations reduces the risk of costly rework, ensuring that projects stay within budget.

Strategic Development

MLOps as a service accelerates continuous delivery, minimizing operational overhead and optimizing resource use. It streamlines AI and machine learning processes, ensuring swift deployment and efficient scaling.

This approach empowers MLOps startups to prioritize strategic tasks, enhancing productivity and responsiveness to market demands.

Increased Performance

MLOps enhances productivity by automating repetitive tasks and refining workflows, allowing data scientists and engineers to focus on higher-value activities. Continuous integration and delivery ensure that models are always up-to-date and performing optimally.

This boosts overall performance, enabling businesses to make data-driven decisions quickly and effectively, and maintain a high level of operational efficiency.

Defining Your Company’s MLOps Maturity Levels

Level 0:
Manual & Code-based Approach

At maturity level 0 in ML operations, data and model processing are predominantly manual on local machines.

There are no formalized deployment, monitoring, or model maintenance processes, and automation and version control tools are used minimally.

This level also shows limited experiment reproducibility, infrequent model updates, and minimal collaboration within the machine learning team.

Level 1:
Mostly Code-based
Approach

Some automation is introduced, primarily for repetitive tasks such as data pre-processing.

Version control is implemented but often minimally, serving only to meet basic requirements. Model deployment remains largely manual, and monitoring is reactive, addressing issues only after they occur rather than preventing them.

This level represents an initial step towards improving efficiency but lacks comprehensive integration.

Level 2:
Mostly Low-code
Approach

Continuous integration and continuous delivery (CI/CD) pipelines are established for automated model training and deployment.

Version control practices are well-defined, and collaboration between teams is significantly improved. Monitoring becomes more proactive, with systems in place to alert teams to potential issues before they escalate.

This level demonstrates a substantial advancement in process automation and team coordination.

Level 3:
No-code or Managed and Cloud-Native Approach

At the highest maturity level, MLOps practices are fully integrated into the development lifecycle.

Advanced features such as A/B testing and model rollback mechanisms are implemented, enabling safe and efficient model experimentation. Monitoring is comprehensive and includes automated actions for performance optimization.

This level represents a fully mature MLOps environment, where processes are streamlined, and performance is continually optimized through sophisticated, automated practices.

Elevate Your MLOps: Achieve Higher Efficiency Level

Our focus is on addressing your current ML process challenges through consulting, aiming to enhance your operational efficiency and move towards higher maturity levels. By optimizing workflows and providing targeted solutions, we aim to improve automation, foster stronger collaboration, and implement proactive monitoring strategies as you advance.

Challenges Addressed by MLOps in Machine Learning Deployments

Lack of Model Deployment Consistency

Lack of Model Deployment Consistency

Companies without MLOps struggle with inconsistent machine learning model deployments across environments, causing inefficiencies and delays in updates. MLOps consulting companies addresses this by using Docker for containerization and frameworks like TensorFlow Serving or AWS SageMaker Endpoints for standardized, reliable deployment pipelines.

Data Quality and Accessibility Issues

Data Quality and Accessibility Issues

Without MLOps, companies face challenges in maintaining high-quality, accessible data for training and inference. MLOps implements data management strategies including versioning with tools like DVC, data cataloging, and secure storage solutions like Amazon S3, ensuring data consistency and compliance.

Lack of Optimization

Lack of Optimization

Companies without MLOps find it challenging to optimize model performance and manage infrastructure costs. Through our ML consulting services, we proactively identify bottlenecks and performance issues, ensuring efficient operations and informed decision-making.

Scaling Challenges

Scaling Challenges

Scaling ML systems without MLOps poses difficulties in handling increasing data loads and inference demands. MLOps enables dynamic resource allocation, auto-scaling in cloud environments, and pipeline automation tools.

Automation Gaps in ML Workflow

Automation Gaps in ML Workflow

Manual interventions in ML workflows without MLOps lead to inefficiencies and delays. MLOps transforms the ML pipeline with automation and orchestration tools, accelerating model development deployment.

Monitoring and Diagnostics Challenges

Monitoring and Diagnostics Challenges

Companies lacking MLOps struggle to monitor ML models effectively, risking performance issues like drift or degradation. MLOps implements continuous monitoring with tools like Grafana, Prometheus, or Amazon CloudWatch to ensure consistent performance and timely corrective actions.

Up-to-date Technologies to Offer a Complete Range of ML Consulting Services

With extensive experience in the MLOps field, we provide guidance for the use of following technologies

Amazon SageMaker

Amazon Bedrock

NVIDIA Triton Inference Server

Apache Airflow

DVC

Vertex AI

Prometheus

MLflow

Azure ML

AutoML

Building a Project Path for Streamlining MLOps Optimization

MLOps optimization tasks are organized into a Maturity Level Based roadmap plan to enhance different areas efficiently, synchronously achieving maximum cumulative impact. Expert support helps avoid common pitfalls, implement proven methods and practices, and accelerate progress in achieving objectives, freeing up resources faster.

MLOps
Screening

Introduce MLOps, gather client feedback, assess maturity, and propose initial steps.

Consultation and Requirements Gathering

Initiate discussions to understand client goals and constraints thoroughly.

Current State
Assessment

Evaluate client infrastructure, audit existing solutions, and assess ML pipeline effectiveness.

Solution
Planning

Define priorities, select technologies, and present a detailed implementation plan to the client. 

Implementation and Support

Assist with implementing changes, provide knowledge transfer, and consult on optimizing processes based on KPI analysis.

Final Evaluation and Closure

Review project achievements, finalize outcomes, and deliver comprehensive project closure documentation.

How You Can Collaborate With Us

We offer a variety of flexible ways to work together, allowing clients to choose the one that fits their needs best. Our approach to MLOps can adapt to different preferences and the nature of each project. This means you can customize how we collaborate to suit your unique requirements.

Start

A brief consulting session designed to identify needs in MLOps, focusing on evaluating and providing recommendations for the client.

  • 16 hours of consulting
  • Assessment of ML processes
  • Identification of pain points and recommendations for improvement

You will receive:

  • A report analyzing the current state of ML processes, identified needs, opportunities, limitations, risks, and an implementation plan to enhance maturity
  • Or a determination of the infeasibility of further work due to the lack of clear business goals, expectations, resources, and understanding of potential benefits of machine learning
Optimal

An ML consulting session that includes infrastructure audits and a maturity level enhancement plan for the client, with minimal support during implementation.

  • 64 hours of consulting
  • Assessment of ML processes
  • Infrastructure audit
  • Identification of pain points and recommendations for improvement
  • Guidance in integrating and configuring advanced MLOps solutions to elevate maturity

You will receive:

  • Services from the Start package
  • Detailed infrastructure audit
  • Transition to the next maturity level
Full

A consulting session combined with hands-on assistance in developing and integrating MLOps approaches to elevate the client’s maturity level.

  • 168 hours of consulting and development
  • Assessment of ML processes
  • Infrastructure audit
  • Identification of pain points and recommendations for improvement
  • Full technical involvement in integrating and configuring advanced MLOps solutions to elevate maturity

You will receive:

  • Services from the Optimal package
  • Technical support throughout all stages of MLOps practice implementation

Success Stories

“Exposit you can trust not just to build complex, original work, but also support the developed product and ensure it thrives after release”.

  • Explore the Success MLOps Story of Wizart

    Explore the Success MLOps Story of Wizart

    Learn more how we’ve implemented MLOps solutions to streamline AI and data management, optimizing operational efficiency and scalability in retail and décor software.

  • Explore the Success Story of Audio to Text Converter

    Explore the Success Story of Audio to Text Converter

    Learn more about how Machine Learning helped to relieve the burden of remembering all the information people interact with during the day, increases the efficiency of work conversations, and consequently improves the business process.

  • Explore the Success Story of Football Analytics

    Explore the Success Story of Football Analytics

    Learn more how a Computer Vision-driven analytics helped create individual football training programs and increase the involvement of students’ parents using the exact statistics and measurements.

Why Choose Exposit MLOps Services

Simple and Effective Strategy

We use a straightforward plan that ensures quality and speed. This roadmap sets expectations, outlines the process, and helps clients understand the workflow and necessary investments. 

Proven AI Expertise

With experience in the industry and our proprietary AI product, Wizart, we quickly turn your ideas into software. Our capabilities make bringing your vision to life efficient and effective. 

MLOps Consultation

We offer consultations on MLOps to help clients understand how AI can benefit their business. Our guidance ensures well-informed decisions about integrating AI into your operations. 

2012

Founded

100+

In-house employees

275+

Completed projects for SMBs and Enterprises

Wizart

Own CV-based product released

Our clients and what they say about Exposit

Their project management style is very hands-on with lots of back and forth communication every day.

James Pursaill

CTO, Plend

Exposit has been our development partner for more than 3 years. They are doing a great job developing our digital platforms that are helping millions of refugees all over the world.

Alexander Bugge

COO, REFUNITE

They’re very excited about the growth and success of our company. It’s not just a job to them. They feel like a part of the team. Special thanks for the transparency and diligent work of the engineers. Deliveries were very solid and timely.

Hans Oberholzer

COO, FROX AG

View more proven positive testimonials from clients across different industries, with various projects’ complexity and expertise.

View testimonials

Starting Your Project with Exposit

Get in Touch

Reach out to Exposit to explore our Artificial Intelligence services. Share your project details and goals with our team. 

Schedule a Consultation

We’ll discuss how our AI consulting and development services can benefit your company and address any questions you may have. 

Get a Project Estimate

Following the discussion, we’ll provide you with a detailed estimate for your AI software project, customized to your needs. 

Kick Off Your Project

Once you’re satisfied with the estimate and plan, we’ll kick off your AI software project. Throughout each stage, we’ll keep you informed and involved to ensure a successful outcome.