Machine Learning

Advancements in data science have made it possible to automate the production of machine learning (ML) models that can analyze large, complex datasets and deliver more accurate results faster. By building precise models, an organization is more likely to identify profitable opportunities – or avoid risks.

These advancements in data science have led to a wide interest and adoption of machine learning, which can be used in many applications for natural language processing, cybersecurity, infrastructure management, healthcare decision support, training and education, and more.

To support this growing interest by customers, First Line has built an organizational structure, created business processes and incorporated methodologies to establish our expertise in Artificial Intelligence and Machine Learning.

Challenges of Machine Learning

Many business and technological breakthroughs have been promised by ML but there are data-related factors that must be resolved:

  • Machine Learning Algorithms require extensive amount of training data.
  • Training data may need to be organized, tagged and labeled.
  • The data can be biased.

Delivery Methodology

Machine learning is often a key element of a new system and requires special engineering and analytical skills. First Line has developed data management methodology that incorporates the use of machine learning and artificial intelligence for various use cases and clients. This methodology includes three phases: business analysis, implementation of the data pipeline, and development of ML models.

Close up look at team skills and capabilities

FLS ML Engineer Profile

  • MXNet, Caffe, TensorFlow, Keras, Torch, Dlib, scikit-learn
  • NLP networks architecture: LSTM/GRU, VAE/GAN, seq2seq
  • Develop scalable algorithms and methods
  • Work with engineering teams to implement new models
  • Leverage a test-and-learn approach to appropriately drive value
  • Rapid development and prototyping in Python
  • Familiar with GPU programming and multi-threading
  • Ability to communicate complex black-box models to cross-functional stakeholders
  • Extensive experience solving challenging real-world problems with machine learning, particularly in deep learning and/or reinforcement learning as a plus.

FLS Data Engineer Profile

  • Develop and manage data pipelines at enterprise scale
  • Build data expertise and own data quality for various data flows
  • Leads backend and ETL development effort of Customer facing Business Intelligence Applications.
  • Designs, maintains, and performance-tunes extraction, transformation, and load (ETL) processes using SQL and Hadoop stack
  • Experience with SQL, Python/R, Redshift, Glue and RDS

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