As we progress towards 2024, machine learning technology will continue its momentum becoming even more ingrained in business operations across sectors. However, most enterprise ML deployments require significant custom software engineering work coupled with data science expertise – skill sets many companies lack internally right now. This growing talent gap is fueling demand for external machine learning development services and machine learning consulting support to drive AI transformation.
Surging Interest Calls for ML Development Services
Machine learning has demonstrated enormous potential to deliver competitive advantages, from predictive insights to automated decision-making and personalization. By 2024, IDC forecasts over 50% of enterprises will be running some form of machine learning in production – whether computer vision for advanced inspection, intelligent process automation via RPAs, customer churn predictions or other use cases.
As excitement builds, ML development services provided by specialized consulting partners allow companies to jumpstart prototype projects with agile sprints targeting key operational pain points ready for ML improvement. Partners handle the heavy lifting of data preparation, model training, deployment and monitoring leaving enterprises to focus on scoping value-add use cases while their team builds the tailored solutions.
Depending on internal stacks and systems, ML productionization can demand software engineering across data infrastructure, API plumbing, UI adaptation and more. These implementation needs are driving more enterprises to seek out full-service machine learning consulting starting from strategic blueprinting through to technology execution.
Why Invest in Custom ML Solutions?
The promises of AI are tantalizing but off-the-shelf ML tools alone rarely solve specific business problems outright. The most transformative enterprise ML applications require research, data and engineering customization from the ground up. ML algorithms must be selected, tuned and trained around available datasets and interfaces to deliver real ROI.
This makes machine learning consulting partners so valuable for getting ML products deployed and working within existing IT environments. They combine data science and DevOps skills to research innovative modeling techniques while also meeting enterprise infrastructure requirements.
Together, business leaders and ML consultants can match promising use cases to rich data streams. With clean, accurately labeled training data flowing to purpose-built ML pipelines, models can deliver the desired functioning. This end-to-end view allows ML to achieve its potential inside companies through custom solutions.
Overcoming Key Enterprise ML Adoption Roadblocks
Great opportunities remain for machine learning to transform business operations but enterprises continue facing barriers to successful adoption, including around:
Limited ML Talent: Demand for ML experts across data and software engineering far outstrips supply. Proven ML consulting fills internal team gaps.
Data Readiness: Many companies lack the pipelines, labeling and clean, integrated data at scale required to fuel accurate modeling. Data must be ML-ready.
Model Monitoring: After deployment, ML model performance decays without monitoring and retraining. MLOps processes address this but add complexity for internal teams.
IT Infrastructure: Getting ML products working within legacy enterprise systems presents engineering hurdles around latency, availability and dependencies.
The right mix of ML consulting services helps enterprises overcome these multifaceted data, ops and compliance challenges to extract meaning from their data.
High-Value ML Applications Areas to Target By 2024
While companies are applying ML across functions, the most promising enterprise use cases include:
Computer Vision: Cameras and video feeds combined with ML algorithms will deliver transformative visibility into processes. Intelligent image analytics provides a scalable solution to traditionally manual monitoring tasks.
Predictive Maintenance: By combining IoT sensor data with telemetry, asset-heavy industries can minimize disruptive downtime through ML breakdown predictions and optimized maintenance scheduling.
Conversational AI: Chatbots, voice interfaces and other natural language applications will provide smoother, more personalized customer and employee experiences while improving operational efficiency.
Personalization Engines: Advanced ML personalization systems will allow for tailored content recommendations, advertising and shopping experiences based on individual user affinities and context.
Fraud Detection: ML will allow financial firms, merchants and public sector agencies to automatically detect emerging fraud patterns early using up-to-the-minute model adaptation.
Positioning for ML Success by 2024 To capitalize on accelerating enterprise ML adoption, technology leaders today should focus on:
- Auditing and enriching internal data pipelines
- Exploring high-impact ML application prototypes
- Building internal ML literacy through education
- Working with specialized ML partners to architect for scale
- Roadmapping model maintenance and monitoring
Now is the time for companies to firmly embed reliable machine learning capabilities leveraging both in-house skills and external ML consulting talent. By combining business context with ML technical excellence, enterprises can shift towards data-driven operations and lasting competitive advantages by 2025. The next few years represent a pivotal window to lay machine learning foundations through strategic investments.
