Machine Learning as a Service

STRATEGYZ’s Machine Learning as a Service (MLaaS) service is designed to allow organisations of all sizes and budgets to access data science capabilities.

STRATEGYZ’s Machine Learning as a Service (MLaaS) was designed from the ground up by working directly with clients across diverse industries and use cases. Our service provides end-to-end support for your machine learning needs. We empower businesses to make better decisions and gain insights that were previously impossible. 

Our platform is scalable and flexible to meet the changing needs of your business. No matter the size of your business, we can help you grow your business in the ways that matter most. 

Our service is underpinned by best in class data privacy and security to keep you worry free as data privacy and security laws evolve. We help you unlock the potential of your data with a partner you can trust.

How it works

Define the Strategy

Our Data Scientists use our Data Science by Design methodology to help you understand how machine learning can be used to address your business needs. Our highly skilled consultants will work with your business and data experts to comprehensively define the ML problem and outline the recommended roadmap to achieve the required project goals. We make sure you have a solid plan in place with measurable goals to help you quantify and measure success.

Build the Models

Now that we’ve developed a plan together, we’ll implement it using our Science+Data methodology and best-in-class models with a focus on ROI. Our models are architecture agnostic and can be deployed on our infrastructure or yours. We make sure that you are informed along the way by providing you with summaries of the data exploration, feature engineering, and model effectiveness. We seamlessly integrate with your systems so you can focus on the outcomes.

Manage & Monitor

Once built, we deploy our models on our infrastructure or yours. All our models come with a comprehensive monitoring suite to ensure reliability of results and continuously monitor the data and predictions for drift. We partner with you to keep the model outputs relevant to the business with a long-term focus on ROI. If you’ve deployed on our system we manage access, availability, and accessibility of all our models. We’re not here to just develop and hand off a model. We’re here to partner with you for the long-haul and focus on long-term business success

How Our Machine Learning Services Work

Analyze and clarify the business problem

It is critically important to ensure that the goals of the model target business requirements, and not just machine learning requirements (e.g., precision, accuracy, etc.). The fundamental purpose of the model is solving significant, practical, and relevant business objectives !

Identify data requirements

The acronym GIGO (“garbage in, garbage out”) applies here. Without access to a sufficient volume of good data, the machine learning model will be inherently unable to generate accurate and reliable predictions. Ensuring both data quantity and data quality enables the model to fulfill its purpose of training AI.

Gather and prepare data

Next, there are a variety of structured (e.g., revenue numbers), unstructured (e.g., customer surveys), and semi-structured (e.g., emails) data preparation activities to cover, such as collection, cleaning, aggregation, augmentation, labeling, normalizing, and transformation.

Train the model

Training data is the dataset used to train the machine learning model and this teaches the algorithm how to make decisions.

Evaluate and measure performance

Think of this step in the process as a quality assurance effort that includes tasks like Model metric evaluation; Model performance metrics; Confusion Matrixcalculation; Model quality measurements and Model quality measurements

Operationalize and iterate the model

Operationalizing the machine learning model can be a relatively simple process (e.g., generating a report) or a more complex effort (e.g., multi-endpoint deployment). However, even if the model is firing on all cylinders, there is no assurance — and there should be no expectation — that it will remain optimized over time.

Your ambitions depend on better knowledge, deeper understanding and more actionable decisions.

ROI

We apply deep industry experience to de-risk the solution and innovative mathematics to increase your return on investment. We plan how to measure the return on your investment from the start and provide you with confidence in our solution.

Cost

Unlike others in the market, we combine innovative technologies and human intelligence to deliver solutions that are 25-50% cheaper than off-the-shelf alternatives. Our combination of people and technology delivers you better solutions at an affordable price.

Outcome

We work with you to identify specific Machine Learning use cases for your business with clearly defined outcomes. Our Data Science by Design methodology prioritizes solutions that will drive the outcomes important to your business.

Accuracy

At STRATEGYZ accuracy is more than a number. Our model’s performance is measured across a full suite of metrics to prioritize overall accuracy, model robustness, and business impact. We retrain our models regularly to maintain performance even as conditions change.

Service

We don’t just deliver a model, we work with you to grow and advise your initiatives. We continuously look to improve our models and work with you as your goals and objectives change.

Risk

85% of ML projects fail to deliver, with only 53% reaching the production phase. At STRATEGYZ, we deliver end-to-end Machine Learning solutions and manage the full delivery from prototype to production.

Ability

STRATEGYZ isn’t just a firm of data scientists. We’re also data engineers, data visualization experts, data privacy experts, and cloud service experts. We can bring on the right talent to ensure your projects are successful and grow with the changing needs of your business

Previous slide

Next slide