Thursday, December 2, 2021

Key Machine Learning (ML) Trends 2021

Machine learning (ML), a commonly used type of artificial intelligence (AI), is one of the fastest-growing fields in technology. 

Especially as the workplace, products, and service expectations are changing through digital transformations, more companies are leaning into machine learning solutions to optimize, automate, and simplify their operations. 

So what does ML technology look like today and where is it heading in the future? Read on to learn about some of the top trends in machine learning today.

5 Trends to Watch in Machine Learning

  1. Automation through MLOps
  2. ML democratization and broadening access
  3. Achieving scalability through containerization
  4. APIs and wider availability of prepackaged tools
  5. ML and time series solutions for future planning

More on the ML market: Machine Learning Market

1. Automation through MLOps

Many businesses are investing significant time and resources into ML development because they recognize its potential for automation. 

When an ML model is designed with business processes in mind, it can automate a variety of business functions across marketing, sales, HR, and even network security. MLOps and AutoML are two of the most popular applications of machine learning today, giving teams the ability to automate tasks and bring DevOps principles to machine learning use cases.

Read Maloney, SVP of marketing at H2O.ai, a top AI and hybrid cloud company, believes that both MLOps and AutoML strategies eliminate several traditional business blockers. 

“Scaling AI for the enterprise requires a new set of tools and skills designed for modern infrastructure and collaboration,” Maloney said. “Teams using manual deployment and management find they are quickly strapped for resources and after getting a few models into production, cannot scale beyond that. 

“Machine learning operations (MLOps), is the set of practices and technology that enable organizations to scale and manage AI in production, essentially bringing the development practice of DevOps to machine learning. MLOps helps data science and IT teams collaborate and empowers IT teams to lead production machine learning projects, without having to rely on data science expertise.

“AutoML solves a few of the biggest blockers to ML adoption, including faster time to ROI and more quickly and easily developing models. AutoML automates key parts of the data science workflow to increase productivity, without compromising model quality, interpretability, and performance. 

“With AutoML, you can automate algorithm selection, feature generation, hyper-parameter tuning, iterative modeling, and model assessment. By automating repetitive tasks in the workflow, data scientists can focus on the data and the business problems they are trying to solve and speed time from experiment to impact.”

Automation through ML is desirable in theory, but in practice, it’s sometimes difficult for business leaders to envision how ML tools can optimize their business operations.

Amaresh Tripathy, SVP and global business leader at Genpact, a digital transformation and professional services firm, offered some common examples of how MLOps and MLOps-as-a-service help businesses in various industries.

“One [MLOps] example is using AI models to efficiently direct sales teams to identify the next best customer,” Tripathy said. “Another is optimizing pricing and revenue management systems using dynamic demand forecasting.”

AI and automation in the workforce: Artificial Intelligence and Automation

2. ML democratization and broadening access

Machine learning is still considered a niche and complex technology to develop, but a growing segment of tech professionals are working to democratize the field, particularly by making ML solutions more widely accessible.

Jean-Francois Gagne, head of AI product and strategy at ServiceNow, a workflow management software company, believes that ML democratization involves creating easier access to develop and deploy ML models as well as giving more people access to useful ML training data.

“Good training data is often scarce,” Gagne said. “Low-data learning techniques are helping in enterprise AI use cases, where customers want to adapt pre-trained out-the-box models to their unique business context. In most cases, their own data sets are not that big, but methods such as transfer learning, self-supervised learning, and few-shot learning help minimize the amount of labeled training data needed for an application.”

ML democratization is also about creating tools that consider the backgrounds and use cases of a more diverse range of users.

Brian Gilmore, director of IoT product management at InfluxData, a database solutions company, believes that more users and developers are starting to recognize the benefit of a diverse team for developing ML solutions.

“Ignoring the technical for a moment, we must focus on the human aspects of AI as well,” Gilmore said. “There seems to be a trend building around the democratization of the ML ecosystem, bringing more diverse stakeholders to the table no matter where in the value chain.

“Bias is probably the single greatest obstacle to ML efficacy, and leading companies are learning to combat bias and build better applications by embracing diversity and inclusion (D&I).

“ML needs additional variety in training data, for sure. Still, we should also consider the positive impact of D&I on the teams that design, build, label, and deliver the ML-driven applications — this can genuinely differentiate ML products.”

More on data democratization: Data Democratization Trends

3. Achieving scalability through containerization

ML developers are increasingly creating their models in containers

When a machine learning product is developed and deployed within a containerized environment, users can ensure that its operational power is not negatively impacted by other programs running on the server. More importantly, ML becomes more scalable through containerization, as the packaged model makes it possible to migrate and adjust ML workloads over time.

Ali Siddiqui, chief product officer at BMC, a SaaS company with a variety of ITOps solutions, believes that containerized development of machine learning is the best way forward, particularly in the case of digital enterprises incorporating autonomous operations.

“It’s trending to use machine learning workloads in containers,” Siddiqui said. “Containers allow autonomous digital enterprises to have isolation, portability, unlimited scalability, dynamic behavior, and rapid change through advanced enterprise DevOps processes. 

“ML workloads are typically spiky and require high scalability and in some cases, real-time stream processing. For instance, when you take a look at ML projects, they typically have two phases: algorithm creation and algorithm execution. The first involves a lot of data and data processing. The second typically requires a lot of compute power in production. Both can benefit from container deployment to ensure scalability and availability.”

More on containerization: Containers are Shaping IoT Development

4. APIs and wider availability of prepackaged tools

In another trending effort toward ML democratization, a number of ML developers have perfected their models over time and found ways to create template-like versions, available to a wider pool of users via API and other integrations.

Bali D.R., SVP at Infosys, a global digital services and consulting firm, believes that prepackaged ML tools, particularly via APIs and digital storefronts, are some of the most common and useful applications of machine learning today:

“API-fication of ML models is another key trend we are seeing, whether it is GPT3, CODEX, or even Hugging Face, where they train and deploy state-of-the-art NLP models and make them available as web APIs or Python packages for inferencing,” DR said. “[There’s also] AI stores with pre-trained models exposed via APIs, which provide a drag-and-drop option for AI development across enterprises.”

Also read: Artificial Intelligence vs. Machine Learning

5. ML and time series solutions for future planning

Machine learning models can only improve their functionality over time if they are consistently fed new data in intervals. Since so many ML models rely on timeline-based updates, a number of ML solutions are using a time series approach to improve the model’s understanding of the what, when, and why behind different data sets.

Read Maloney of H2O.ai explained why time series solutions are necessary for truly predictive ML:

“On a long enough horizon, all problems eventually become time series problems,” Maloney said. “ML is a phenomenal method for predicting events in real-time, and as we observe these predictions over time, we need more and more time series solutions. 

“Every business needs to make predictions, whether forecasting sales, estimating product demand, or predicting future inventory levels. In all cases, data is necessary as well as specific techniques and tools to account for time.”

Selecting the right machine learning support for your business: Top Machine Learning Companies

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