By Radha Basu, Founder and CEO, iMerit
Artificial intelligence (AI) and machine learning (ML) are considered among the top technologies driving digital business transformation. Organizations are recognizing the value of AI and ML by investing in technology that assists it in achieving maximum value for their business. AI and ML are fundamentally changing the way we think of data and driving the need for best practices within ML DataOps.
ML DataOps is a combination of process and technology to deliver reliable data pipelines with agility, making businesses data-driven. While analyzing data, one must identify in what context it can be used to build an ML model – computer vision, natural language processing (NLP), or content services.
Removing supply chain friction
Data collection and storage are the first steps in any AI project, followed by data organization for use in training, model training, error correction, model monitoring and production deployment. These are categories in which an enterprise can disrupt the space to tackle inefficient or poor model training. Enterprises can increase value along the supply chain by building tools that make the data pipeline run smoother.
With a long, convoluted supply chain, the AI industry depends on end-to-end solutions that can deliver consistently high-quality data. This may entail creating a command-and-control centre where practitioners can come in, visualize data as it moves through the lifecycle, and change and learn from it. The tools ecosystem, which may allow data scientists to simplify their tasks, accelerate outcomes and contribute more meaningfully to the data value chain.
Impact Of AI, ML Dataops
According to a recent PwC survey, 75 per cent of business leaders believe AI will help them make better decisions; 64 per cent believe it will be crucial to the future of their company’s efficiency and production. Over the past year, the AI ecosystem has witnessed a push to move to a more data-centric approach from the current model-centric one. Data is the single most important differentiator for ML models to succeed. Everyday companies are using AI to improve processes, increase revenue and reduce costs.
ML DataOps is gaining traction as it enables us to handle data at scale as it flows through the cyclical journey of AI training and deployment, if properly structured. This is critical to ensure the long-term viability of the resulting AI solutions because a transition from testing to production is required, which must be accomplished through repeatable and scalable methods. Implementing best practices to facilitate rapid, safe, and efficient development and operationalization of any business requires time and resources in three key areas: talent, technology, and technique.
One of the main impediments to scaling AI and analytics is the non-availability of technical expertise. ML DataOps includes methods to recruit and retain key personnel. Most technical talent is enthralled by the prospect of working on cutting-edge projects with cutting-edge technologies, allowing them to focus on difficult analytical problems and witness the results of their efforts in the real-world.
Today, building AI at scale needs a diverse range of unique skill sets. A data scientist, for example, develops algorithmic models that accurately and consistently anticipate behaviour whereas an ML engineer optimizes, bundles, and integrates research models into products while continuously monitoring their quality. To successfully scale, business leaders should build and empower specialized, dedicated teams that can focus on high-value strategic priorities that their team can accomplish.
Employees sometimes fear being replaced by AI, which can slow down transformation. Companies should create opportunities for employees to reskill and upskill, restructure business processes, workflows and policies, and improve top-down communication to ensure that everyone understands what is changing, why it is changing and what the expectations are.
Data is the lifeline of ML models, and an organization-wide AI strategy should start with data management. As data is vast, it becomes increasingly challenging to manage, cleanse, maintain and analyze it. As a result, deploying a data pipeline to scale across an organisation is practically difficult without tools for managing the many components of a data lifecycle. The tools required vary for the different parts of the data pipeline. But one overall requirement across the board are tools that offer transparency and visibility into the activities taking place as well as their impact on the rest of the pipeline.
AI development lies in the resolution of edge cases or outliers in the data. The ability to handle edge cases can make or break the production-readiness of a trained ML system. Companies in the ecosystem are constantly in need of ways to seamlessly integrate human expertise with tooling capabilities for auditing, monitoring, and handling edge cases.
Making sense of data is necessary for making sound business decisions. Upskilling in technological knowledge has become a must. Your workforce is your guide through a very competitive world, helping your business surpass competitors, which means each team member needs to have the tools and technology that can help them perform at their best.
Tools and end-to-end platforms that support AI/ML at scale must support creativity, speed, and safety. Without proper tools, a company will struggle to maintain all of these at the same time.
The building of AI models is a creative process that necessitates ongoing repetition and modification. It’s quite simple to construct ML models that work well for certain business challenges but implementing AI across the enterprise can soon become tough. This is because developing ML models necessitates much trial and error to find the best datasets, work flows, hyperparameters, scripts and so on. Feedback loops become critical to be able to make real-time decisions to drive impact.
AI is no longer merely a frontier to be explored. As businesses seek to deploy their models, this mix of technology and human-in-the-loop knowledge provides a real end-to-end AI data solution. As demand for AI has surged, so has the pace of technological innovations that can automate and simplify building and maintaining AI systems. The finest quality data thus can be obtained by bringing together the necessary knowledge, judgment, and technology.