What is the Difference between ML and AI Consulting?

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What is Machine Learning?

Machine learning (ML) is a rapidly evolving technology that can have a significant impact on all aspects of a business. ML enables computers to “think” and learn in the same way as people do, by drawing conclusions and forecasting the future based on historical and real-time data analysis.

Machine Learning consulting is a becoming more and more popular with established firms and young start-ups. This type of consulting enables your business to take advantage of a variety of new prospects, such as customising customer service, automate operations, and adopt solutions that alter how customers interact with your product.

What is the Difference Between ML and AI Consulting?

Though machine learning (ML) is the domain of artificial intelligence (AI) with the most significant commercial applications, it is prudent to distinguish between the two.

  • AI: Encompasses all applications in which a machine emulates human intellect
  • Machine Learning: Encompasses all applications in which known data is used to generate models that can be used to classify/process new data.

Is ML Consulting the Same as Deep Learning Consulting?

Not exactly. Deep learning is a subset of machine learning. However, deep learning is the most accurate machine learning technology available today in the majority of applications.

It is not uncommon to see alternative techniques such as decision forests being used in place of deep learning in the industry, due to a deep learning model’s inability to explain its outputs. There are some instances where deep learning models are not implemented in production:

  • When managers feel uneasy with complicated models or those that do not provide an explanation for their outcomes
  • When audibility is necessary; labour law, for example, prohibits discrimination.

Any algorithm that employs previously discriminatory criteria (e.g., gender or race) cannot legally make human resource choices without offering a rationale that includes factors other than those criteria. Unfortunately, excluding potentially discriminatory measures from the model does not resolve the issue. For instance, a person’s name, PTO patterns, salary disparity, and a variety of other data points could be utilised to include gender in decision-making in an indirect manner. No matter how precise or valuable their results are, black-box models cannot be used in specific scenarios.

What are the Barriers to ML Adoption?

As Deloitte notes, the following are the most often identified hurdles by practitioners:

  • Talent scarcity: by 2020, PwC predicts that there will be more than 2.9 million job openings for data science and analytics professions in the United States alone. However, there is insufficient data science talent to meet this need.
  • Inadequate maturity of machine learning infrastructure and processes: machine learning is a novel programming paradigm in which rules are derived from data rather than programmer input, but it takes a long time for machine learning techniques and frameworks to mature. Tensor Flow, one of the most used machine learning frameworks, was first released in late 2015.
  • Most machine learning approaches are data-hungry: accurately labelled training data generation is time-consuming and costly. Practitioners of machine learning must be resourceful in utilising publicly available data or obtaining labelled data.
  • To acquire public data, businesses can rely on data collectors such as Bright Data, which automates real-time web data extraction and automatically delivers it to enterprises in the specified format.
  • On the other hand, businesses can rely on the various data labelling companies that have been growing in popularity since the 2010s.
  • Another possibility is to use one-shot learning or other less data-intensive algorithms; nevertheless, this is an area of active research.

What Is the Future of Machine Learning Consulting?

ML consultancy will expand as a result of addressing the following issues:

  • Increasing the pool of available talent: most consultancies are doing in-depth analyses of their employees to discover data scientists. After a brief training period, persons with a background in programming, statistics, or arithmetic are typically qualified to work as data scientists.
  • Enhancing the infrastructure and processes for machine learning: as machine learning advances as a programming paradigm, more efficient methods, improved computing resources (i.e. GPUs and AI chips), and increased automation will make machine learning faster and easier.
  • Using data creatively: Natural language processing (NLP) advances have been made possible by the widespread availability of translated government documents in Canada and Europe. While data collection is a relatively simple approach, areas of AI study such as transfer learning or data synthesis may require more technological expertise.
  • Eliminating biases in algorithms: Machine learning algorithms may produce erroneous conclusions due to developer biases and insufficient training data. Avoiding or reducing these biases can result in more accurate models.
  • Finally, local machine learning applications are anticipated to make IoT applications smarter and faster by delegating decision-making to edge devices.

Why Are You Seeking Machine Learning Consultancy Now?

Implementing machine learning technologies provides numerous benefits, including the following:

  • Increased staff productivity as a result of computer vision and natural language processing automating tedious and routine work.
  • Improved customer support experience, facilitated by AI-powered chatbots and virtual assistants.
  • Accelerated sales process as a result of enhanced opportunity visibility and lead prioritisation.
  • Cost savings associated with equipment maintenance as a result of predictive monitoring and preventative maintenance.
  • Increased production efficiency results from anticipating demand and throughput, optimising manufacturing processes, and predictive modelling of product quality.

See also,

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