Machine Learning Professionals’ Practical Insights: Tools and Frameworks in Focus

Insights from the ML professionals on tools and frameworks used in practice

In today’s dynamic job market, Machine Learning (ML) has surged in importance, influencing industries from finance to entertainment. With the shift towards Large Language Models (LLMs) and Artificial Intelligence (AI), professionals are exploring new career paths, notably transitioning from Data Scientist to ML / AI Engineer roles.

Tools and Frameworks for ML Professionals Survey

I reached out to ML Engineers on LinkedIn to get real insights from those actively working in the field. 


The survey I conducted was a deliberate effort to bridge the gap between prevailing trends in machine learning (ML), as discerned from job descriptions and AI/ML conferences, and the actual experiences and preferences of professionals in the field. By assessing the disconnect between these established trends and real-world experiences, this research aimed to uncover the nuanced differences, understand the prevailing practices, and identify the evolving needs within the ML landscape. The intention was to gain insight into the practical application of ML tools and frameworks in professional settings, ultimately gauging the alignment between the industry’s expectations and the ground reality of ML careers.


The survey is still open to ML practitioners.


Section 1: Participant Demographics

The survey revealed an intriguing mix of experience levels across participants:

  • Experience Distribution: The majority stood at mid-level, signifying a seasoned cohort:

  • Location and Industry: Spanning across diverse geographies like France, Czechia, United Kingdom, USA, Brazil, Germany, Poland, and Denmark, participants hailed from various industries including Financial Services, Fintech, Media & Entertainment, Retail, IT,  Healthcare, and Video Technology.

Section 2: Machine Learning Frameworks

Survey insights and job descriptions converged on the prominence of PyTorch as the primary ML framework. Both data sources indicated a utilization mix that encompassed scikit-learn, Keras, and TensorFlow alongside PyTorch:

Section 3: Data Processing Tools

Alignment was evident in the predominant use of Python, especially Pandas, for data preprocessing among both surveyed professionals and job descriptions:


Section 4: Model Deployment and Management

An overlap surfaced in the methods and challenges of model deployment. Docker, Kubernetes (K8s), AWS SageMaker, and Kubeflow featured commonly in both the survey and job descriptions. Challenges concerning large model sizes during deployment echoed through both datasets.


Section 5: DevOps tools or practices

Inquiring about the most effective DevOps tools or practices for streamlining machine learning model deployment and management revealed various strategies. The responses highlighted the significance of CI pipelines, automated tests, and regression test suites. The GitOps philosophy was mentioned as a facilitator for rapid and replicable deployments. Kubernetes (k8s) emerged as a popular choice, along with tools like Airflow, Git, GitLab, and CI/CD pipelines, underscoring the value of containerization (Docker) and infrastructure-as-code tools like Terraform in the ML workflow.

Section 6: Computing Approach

Edge vs. Cloud Computing: Most prefer cloud-based processing due to easier management, better resource utilization, and less operational complexity.

Section 7: Low-Code/No-Code Tools for ML

  1. Usage of Low-Code/No-Code Platforms: Only one participant occasionally uses AutoML toolkits for quick model development.
  2. Satisfaction and Suggestions: Overall low usage and varying satisfaction levels; lack of support for codifying frustrates users.

Overall Summary and Insights

  • PyTorch is a prominent ML framework.
  • AWS and GCP are popular cloud providers.
  • Kubernetes is widely used for deployment.
  • Preference for cloud-based processing over edge computing.
  • Low use and mixed satisfaction with low-code/no-code tools.
  • While job descriptions often emphasize senior-level expertise, the reality reflects that  mid-level practitioners are contributing meaningfully to the ML domain.

The survey highlights a gap between job expectations and practical experiences in the ML domain. While job descriptions stress senior-level expertise and specific tools, real-world practice reveals a diverse landscape across different expertise levels. This mismatch shows that although there’s alignment in tools and frameworks, there’s a disparity in seniority levels. Bridging this gap means acknowledging real-world complexities, embracing diverse approaches and tools, and fostering an inclusive environment for ML professionals at all levels to contribute effectively and grow within the field.

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1 thought on “Machine Learning Professionals’ Practical Insights: Tools and Frameworks in Focus

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